From 7fd0995e9607f211d6bd1c58dd1fd9f9bd8e6808 Mon Sep 17 00:00:00 2001 From: elisno Date: Thu, 26 Sep 2024 16:54:04 +0000 Subject: [PATCH] deploy: cleanlab/cleanlab@fcb653811c1ddd4afd960ba2710e443cf6edb384 --- latest_release.txt | 2 +- master/.buildinfo | 2 +- .../cleanlab/benchmarking/index.doctree | Bin 3248 -> 3248 bytes .../benchmarking/noise_generation.doctree | Bin 81345 -> 81345 bytes .../.doctrees/cleanlab/classification.doctree | Bin 290735 -> 290735 bytes master/.doctrees/cleanlab/count.doctree | Bin 283717 -> 283717 bytes .../.doctrees/cleanlab/data_valuation.doctree | Bin 26578 -> 26578 bytes .../cleanlab/datalab/datalab.doctree | Bin 174487 -> 174487 bytes .../guide/_templates/issue_types_tip.doctree | Bin 4354 -> 4354 bytes .../guide/custom_issue_manager.doctree | Bin 31452 -> 31452 bytes .../guide/generating_cluster_ids.doctree | Bin 6318 -> 6318 bytes .../cleanlab/datalab/guide/index.doctree | Bin 12087 -> 12087 bytes .../guide/issue_type_description.doctree | Bin 266308 -> 266308 bytes .../cleanlab/datalab/guide/table.doctree | Bin 63584 -> 63584 bytes .../.doctrees/cleanlab/datalab/index.doctree | Bin 5445 -> 5445 bytes .../datalab/internal/adapter/imagelab.doctree | Bin 159126 -> 159126 bytes .../datalab/internal/adapter/index.doctree | Bin 3624 -> 3624 bytes .../cleanlab/datalab/internal/data.doctree | Bin 105136 -> 105136 bytes .../datalab/internal/data_issues.doctree | Bin 77301 -> 77301 bytes .../cleanlab/datalab/internal/factory.doctree | Bin 64553 -> 64553 bytes .../cleanlab/datalab/internal/index.doctree | Bin 4620 -> 4620 bytes .../datalab/internal/issue_finder.doctree | Bin 46989 -> 46989 bytes .../_notices/not_registered.doctree | Bin 3440 -> 3440 bytes .../issue_manager/data_valuation.doctree | Bin 79832 -> 79832 bytes .../internal/issue_manager/duplicate.doctree | Bin 75245 -> 75245 bytes .../internal/issue_manager/imbalance.doctree | Bin 68346 -> 68346 bytes .../internal/issue_manager/index.doctree | Bin 5282 -> 5282 bytes .../issue_manager/issue_manager.doctree | Bin 80662 -> 80662 bytes .../internal/issue_manager/label.doctree | Bin 88614 -> 88614 bytes .../issue_manager/multilabel/index.doctree | Bin 3685 -> 3685 bytes .../issue_manager/multilabel/label.doctree | Bin 79258 -> 79258 bytes .../internal/issue_manager/noniid.doctree | Bin 90556 -> 90556 bytes .../internal/issue_manager/null.doctree | Bin 68181 -> 68181 bytes .../internal/issue_manager/outlier.doctree | Bin 78825 -> 78825 bytes .../issue_manager/regression/index.doctree | Bin 3685 -> 3685 bytes .../issue_manager/regression/label.doctree | Bin 108542 -> 108542 bytes .../underperforming_group.doctree | Bin 116536 -> 116536 bytes .../datalab/internal/model_outputs.doctree | Bin 78458 -> 78458 bytes .../cleanlab/datalab/internal/report.doctree | Bin 34190 -> 34190 bytes .../cleanlab/datalab/internal/task.doctree | Bin 57819 -> 57819 bytes .../datalab/optional_dependencies.doctree | Bin 3451 -> 3451 bytes master/.doctrees/cleanlab/dataset.doctree | Bin 100920 -> 100920 bytes .../cleanlab/experimental/cifar_cnn.doctree | Bin 407995 -> 407995 bytes .../cleanlab/experimental/coteaching.doctree | Bin 48525 -> 48525 bytes .../cleanlab/experimental/index.doctree | Bin 5365 -> 5365 bytes .../experimental/label_issues_batched.doctree | Bin 158466 -> 158466 bytes .../experimental/mnist_pytorch.doctree | Bin 555175 -> 555175 bytes .../experimental/span_classification.doctree | Bin 34890 -> 34890 bytes master/.doctrees/cleanlab/filter.doctree | Bin 94218 -> 94218 bytes .../.doctrees/cleanlab/internal/index.doctree | Bin 4532 -> 4532 bytes .../internal/label_quality_utils.doctree | Bin 19410 -> 19410 bytes .../cleanlab/internal/latent_algebra.doctree | Bin 85348 -> 85348 bytes .../internal/multiannotator_utils.doctree | Bin 46750 -> 46750 bytes .../internal/multilabel_scorer.doctree | Bin 183513 -> 183513 bytes .../internal/multilabel_utils.doctree | Bin 34042 -> 34042 bytes .../cleanlab/internal/neighbor/index.doctree | Bin 6725 -> 6725 bytes .../internal/neighbor/knn_graph.doctree | Bin 111899 -> 111899 bytes .../cleanlab/internal/neighbor/metric.doctree | Bin 38404 -> 38404 bytes .../cleanlab/internal/neighbor/search.doctree | Bin 32456 -> 32456 bytes .../cleanlab/internal/outlier.doctree | Bin 29778 -> 29778 bytes .../token_classification_utils.doctree | Bin 69171 -> 69171 bytes .../.doctrees/cleanlab/internal/util.doctree | Bin 212686 -> 212686 bytes .../cleanlab/internal/validation.doctree | Bin 41565 -> 41565 bytes .../.doctrees/cleanlab/models/index.doctree | Bin 4972 -> 4972 bytes .../.doctrees/cleanlab/models/keras.doctree | Bin 106237 -> 106237 bytes .../.doctrees/cleanlab/multiannotator.doctree | Bin 165197 -> 165197 bytes .../multilabel_classification/dataset.doctree | Bin 67275 -> 67275 bytes .../multilabel_classification/filter.doctree | Bin 86794 -> 86794 bytes .../multilabel_classification/index.doctree | Bin 4916 -> 4916 bytes .../multilabel_classification/rank.doctree | Bin 47085 -> 47085 bytes .../cleanlab/object_detection/filter.doctree | Bin 38032 -> 38032 bytes .../cleanlab/object_detection/index.doctree | Bin 3852 -> 3852 bytes .../cleanlab/object_detection/rank.doctree | Bin 149811 -> 149811 bytes .../cleanlab/object_detection/summary.doctree | Bin 166920 -> 166920 bytes master/.doctrees/cleanlab/outlier.doctree | Bin 98688 -> 98688 bytes master/.doctrees/cleanlab/rank.doctree | Bin 113711 -> 113711 bytes .../cleanlab/regression/index.doctree | Bin 3738 -> 3738 bytes .../cleanlab/regression/learn.doctree | Bin 222189 -> 222189 bytes .../cleanlab/regression/rank.doctree | Bin 19815 -> 19815 bytes .../cleanlab/segmentation/filter.doctree | Bin 28604 -> 28604 bytes .../cleanlab/segmentation/index.doctree | Bin 3788 -> 3788 bytes .../cleanlab/segmentation/rank.doctree | Bin 51266 -> 51266 bytes .../cleanlab/segmentation/summary.doctree | Bin 69021 -> 69021 bytes .../token_classification/filter.doctree | Bin 27210 -> 27210 bytes .../token_classification/index.doctree | Bin 3934 -> 3934 bytes .../token_classification/rank.doctree | Bin 60167 -> 60167 bytes .../token_classification/summary.doctree | Bin 79564 -> 79564 bytes master/.doctrees/environment.pickle | Bin 17051372 -> 17051392 bytes master/.doctrees/index.doctree | Bin 43029 -> 43029 bytes master/.doctrees/migrating/migrate_v2.doctree | Bin 28116 -> 28116 bytes .../tutorials/clean_learning/tabular.ipynb | 130 +- .../tutorials/clean_learning/text.ipynb | 1682 ++++---- .../nbsphinx/tutorials/datalab/audio.ipynb | 1156 +++--- .../tutorials/datalab/datalab_advanced.ipynb | 326 +- .../datalab/datalab_quickstart.ipynb | 138 +- .../nbsphinx/tutorials/datalab/image.ipynb | 3486 ++++++++--------- .../nbsphinx/tutorials/datalab/tabular.ipynb | 138 +- .../nbsphinx/tutorials/datalab/text.ipynb | 172 +- .../tutorials/datalab/workflows.ipynb | 1124 +++--- .../nbsphinx/tutorials/dataset_health.ipynb | 34 +- master/.doctrees/nbsphinx/tutorials/faq.ipynb | 588 +-- .../tutorials/improving_ml_performance.ipynb | 306 +- .../nbsphinx/tutorials/indepth_overview.ipynb | 210 +- .../nbsphinx/tutorials/multiannotator.ipynb | 146 +- .../tutorials/multilabel_classification.ipynb | 98 +- .../nbsphinx/tutorials/object_detection.ipynb | 195 +- .../nbsphinx/tutorials/outliers.ipynb | 568 +-- .../nbsphinx/tutorials/regression.ipynb | 202 +- .../nbsphinx/tutorials/segmentation.ipynb | 1052 ++--- .../tutorials/token_classification.ipynb | 212 +- .../tutorials/clean_learning/index.doctree | Bin 3019 -> 3019 bytes .../tutorials/clean_learning/tabular.doctree | Bin 64488 -> 64488 bytes .../tutorials/clean_learning/text.doctree | Bin 233888 -> 233886 bytes .../.doctrees/tutorials/datalab/audio.doctree | Bin 333649 -> 333645 bytes .../datalab/datalab_advanced.doctree | Bin 203507 -> 203507 bytes .../datalab/datalab_quickstart.doctree | Bin 145956 -> 145956 bytes .../.doctrees/tutorials/datalab/image.doctree | Bin 454838 -> 454840 bytes .../.doctrees/tutorials/datalab/index.doctree | Bin 3367 -> 3367 bytes .../tutorials/datalab/tabular.doctree | Bin 121626 -> 121626 bytes .../.doctrees/tutorials/datalab/text.doctree | Bin 150907 -> 150907 bytes .../tutorials/datalab/workflows.doctree | Bin 413674 -> 413674 bytes .../tutorials/dataset_health.doctree | Bin 329657 -> 329657 bytes master/.doctrees/tutorials/faq.doctree | Bin 199353 -> 199353 bytes .../improving_ml_performance.doctree | Bin 372312 -> 372312 bytes .../tutorials/indepth_overview.doctree | Bin 224033 -> 224033 bytes master/.doctrees/tutorials/index.doctree | Bin 3181 -> 3181 bytes .../tutorials/multiannotator.doctree | Bin 137334 -> 137334 bytes .../multilabel_classification.doctree | Bin 68223 -> 68223 bytes .../tutorials/object_detection.doctree | Bin 140181 -> 140181 bytes master/.doctrees/tutorials/outliers.doctree | Bin 107891 -> 107897 bytes .../tutorials/pred_probs_cross_val.doctree | Bin 20514 -> 20514 bytes master/.doctrees/tutorials/regression.doctree | Bin 110660 -> 110660 bytes .../.doctrees/tutorials/segmentation.doctree | Bin 1994473 -> 1994473 bytes .../tutorials/token_classification.doctree | Bin 176643 -> 176661 bytes .../tutorials/clean_learning/tabular.ipynb | 2 +- .../tutorials/clean_learning/text.ipynb | 2 +- master/_sources/tutorials/datalab/audio.ipynb | 2 +- .../tutorials/datalab/datalab_advanced.ipynb | 2 +- .../datalab/datalab_quickstart.ipynb | 2 +- .../_sources/tutorials/datalab/tabular.ipynb | 2 +- master/_sources/tutorials/datalab/text.ipynb | 2 +- .../_sources/tutorials/dataset_health.ipynb | 2 +- .../tutorials/improving_ml_performance.ipynb | 2 +- .../_sources/tutorials/indepth_overview.ipynb | 2 +- .../_sources/tutorials/multiannotator.ipynb | 2 +- .../tutorials/multilabel_classification.ipynb | 2 +- .../_sources/tutorials/object_detection.ipynb | 2 +- master/_sources/tutorials/outliers.ipynb | 2 +- master/_sources/tutorials/regression.ipynb | 2 +- master/_sources/tutorials/segmentation.ipynb | 2 +- .../tutorials/token_classification.ipynb | 2 +- master/searchindex.js | 2 +- master/tutorials/clean_learning/tabular.ipynb | 130 +- master/tutorials/clean_learning/text.html | 18 +- master/tutorials/clean_learning/text.ipynb | 1682 ++++---- master/tutorials/datalab/audio.html | 2 +- master/tutorials/datalab/audio.ipynb | 1156 +++--- .../tutorials/datalab/datalab_advanced.html | 4 +- .../tutorials/datalab/datalab_advanced.ipynb | 326 +- .../datalab/datalab_quickstart.ipynb | 138 +- master/tutorials/datalab/image.html | 52 +- master/tutorials/datalab/image.ipynb | 3486 ++++++++--------- master/tutorials/datalab/tabular.ipynb | 138 +- master/tutorials/datalab/text.html | 2 +- master/tutorials/datalab/text.ipynb | 172 +- master/tutorials/datalab/workflows.html | 424 +- master/tutorials/datalab/workflows.ipynb | 1124 +++--- master/tutorials/dataset_health.ipynb | 34 +- master/tutorials/faq.html | 6 +- master/tutorials/faq.ipynb | 588 +-- .../tutorials/improving_ml_performance.ipynb | 306 +- master/tutorials/indepth_overview.ipynb | 210 +- master/tutorials/multiannotator.ipynb | 146 +- .../tutorials/multilabel_classification.ipynb | 98 +- master/tutorials/object_detection.ipynb | 195 +- master/tutorials/outliers.html | 6 +- master/tutorials/outliers.ipynb | 568 +-- master/tutorials/regression.ipynb | 202 +- master/tutorials/segmentation.html | 10 +- master/tutorials/segmentation.ipynb | 1052 ++--- master/tutorials/token_classification.html | 20 +- master/tutorials/token_classification.ipynb | 212 +- versioning.js | 4 +- 183 files changed, 12052 insertions(+), 12462 deletions(-) diff --git a/latest_release.txt b/latest_release.txt index 57fec884d..873ca0fa6 100644 --- a/latest_release.txt +++ b/latest_release.txt @@ -1 +1 @@ -v2.6.6 +v2.7.0 diff --git a/master/.buildinfo b/master/.buildinfo index fe9891c8c..dbcde3356 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: 6aec967625063143b64e56727739db11 +config: 9dcda6a9c0263d6e44ec559ab4e92920 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index 0c53b3ea4bd6125bc9bb03bd236bc14576b2f1ba..85a64682be739aee36a5eb35ce2ea1fefaed73a3 100644 GIT binary patch delta 117 zcmdlWxj}M6IHRFqc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= YQj%$MVsfIv<_^XHPBOG{axZ5D04@k3!2kdN delta 117 zcmdlWxj}M6IHO^KS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) YNvff7Qj&?$<_^XHPBOG{axZ5D0A6h)LjV8( diff --git a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree index 314f3c3f32ba3b8ecabaad7804b1b8a6109c430e..c41622007debf521d91eb779461bb4da5cbbf81a 100644 GIT binary patch delta 1464 zcmX^3o8{ndmJRWYhKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3Xb|FtU=NZL*_z+-4PKKNiw$1ZkbFYs1JtS%9OEEbZ-^EjjOjglSY+1j)Dn@l|inYJ3Kb&?lBJ)0}EOPI;Db)`NVxqi(t zT0(|{H#?eNXD7?CC7bu!FXSXsE3?-_DYCT2PhPLbwYe@ekGyyVYoA<~DzJH4UVtnG zE@!&gMt2gt;itzVU0C44K*=n5b_*C&buCuJs`O!g7q= O)rCPtelFtU=NZL*_z+-4PKKNiw$1ZkbFYs1JtS%9OEEbZ-^EjjOjglSY+1j)Dn@l|inYJ3Kb&?lBJ)0}EOPI;Db)`NVxqi(t zT0(|{H#?eNXD7?CC7bu!FXSXsE3?-_DYCT2PhPLbwYe@ekGyyVYoA<~DzJH4UVtnG zE@!&gMt2gt;itzVU0C44K*=n5b_*C&buCuJs`O!g7q= OH>>+yObRn^f-Pfev)3x_?Cj!1{w9AO^; zS?tz)ow0sew#8~}ndUMd3nY%-_F%z&NZx3!23I&HjjaY950(^jpp~aU4lS?4U!0IK zXzBy)*rKGECi`RYVYev{b^!dHCUqCC^hUahDqQ`SdaNhm*4wr- z2OOb{9=ePg9rzllP~wPMGY^WVc~*NM(;; z@~EB7!DQ}W&tY<{llfqBPA+#XhX0 zlE)_nfXk89Zkq<`LFw%-Jb`N1&qxZL3P|PPVQs}wNw^%ffidV)m-I6Y6(8!A`f@RG z;NeYXH)fZl917i&$dQc8`xPmbUOkgWk6vzju95gSY^X?FY<MGJC7b=88A@ZWoi_R!wUhcCObNM{K=RNbD_neuD#mtGt z%*hs}MfQh!1D%x@9p&x84reGFa#bC0RfmJl_R3J8qr&CXf*o3*x;$JF)?C40MY%D| zJ_54n?fF`L{j6+>R$J1IWj^YSAHD6te0`9-!C3XJa7-Fq_1PXSDMo(_Pl6npUkAU~ zA*Ig<_qSn--e&l2#+3M}dcG=adToj9iN=PUhCC1e_&ZH%FJ0>ibrqC3`mRLGC+^nU zwliWwk<;-PB^u~W{3?AamxYbl&%Mc*w%eHh`anXp%2FZFux93NW9G+6m^-<=0BehW zWx$!=tr{>l@6QETyL)XI*(T;XlZUY}WP5CgZ*X=GO-;{|m;#dzrm%LH>`7;LV6tCj zk74qNmCeCqZeuTCa*dt2VRBYBy9|&?G|4y&{hi0g0O-c}AZ;)E29qZX*j1Q3UC55Y z0SSY)R6xA7Cv)_gJ={jc3LlXZw$X)+o&0IMGb0f%5|KQ}eB4n~Ddp$#2IS2M zn>szCm|OIjRM|?rkdGplMbn0a%?V^&Y?X3|)~oo_mIRiwj%r3Zzm7RHlURa%K=mXF jm`bQ{T@-Gip*;D#ie=YGd>q!7#V4c{!F*1<%ktqPjlEM#iktQ$M42)VYd zv0g!L2;Xt|!c2PPY-V)*Lat+LJ$94p*aN<8WH=U_e$NHYA=lPVVdu%U)iZhvxmsJ} zzmS`f7?T>vjU3KYTXMCYOHU!!ue-8ekrz4kn?(w>cu7ycz$9V6d0pMSAhNXTPxe!n zoE{s+D7yL9#oL_Zo3UB%j+H&>+NTFTX0(`YAjQbO{f0E-EixQ{WP*|!;}K)h-2ebn8myXJ%@`$MV)j5 KwgbCfElL0=IYZ)rCPtg974c{!F*1<%ktqPjlEM#iktQ$M42)VYd zv0g!L2;Xt|!c2PPY-V)*Lat+LJ$94p*aN<8WH=U_e$NHYA=lPVVdu%U)iZhvxmsJ} zzmS`f7?T>vjU3KYTXMCYOHU!!ue-8ekrz4kn?(w>cu7ycz$9V6d0pMSAhNXTPxe!n zoE{s+D7yL9#oL_Zo3UB%j+H&>+NTFTX0(`YAjQbO{f0E-EixQ{WP*|!;}K)h-2ebn8myXJ%@`$MV)j5 KwgbCfElL1=Mp9h> diff --git a/master/.doctrees/cleanlab/data_valuation.doctree b/master/.doctrees/cleanlab/data_valuation.doctree index 6adda08f19267c71be7fc9ebeabe03375090cc60..7b40367efac03d77a242af035945269921777f09 100644 GIT binary patch delta 477 zcmca~p7GLo#tqSohKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XE<7;liFt5MKl^HruDOyp^u{ExMPES-Uy*j-u3(>aM)rCPtHc7;liFt5MKl^HruDOyp^u{ExMPES-Uy*j-u3(>aMxkMo5!F$_-s@WRSRX<~i9!k<`XY`hg@N z8RB6a2!>{ugcY%uc!RK;q8gdGb|tN=ZuCaFFQWJ4U$C!#z~}iq-{(B({i@=LtL_F>Q+!^ZH(u}8Jl+P6&#frlxEz;V>?ZjEV7eS@ zVoR1&Wu+TUR9~`4dfMI((D_sc(;fCL5UMbjVHvF**(Vdd*R4{hX@%YHd4O6U_4c6FseV6dOXD=z#8}7S@>S#H=lPW3GZ7 z`n-rdUP!iu_2p&M+?TIp7Yo13!OY9Wk9G{zmx~f}t`<3l8?DcYgO5&=&^D|97X_kD zkgd&=3*f{J!P@wrZzKzTZL@&YkiLuf$5%*n6KzPMCT3veqRPNd zsEr_(qBMuy>_J1Y`}jaRt-_9OaPnz2uTg0l3;!Q)O3-y%>Cw47NGE{Dry;lqzjlMp gOQlPVz@&hm+@}d(-?)H>kX_sOBfp*d=V&MW2f0ik!2kdN delta 4228 zcmbuC-Aj{E9LG6lZMiLN*^7A*Swe3&?oFK$(xi~uL{}RbB-``sMN^cKnyjQ3Bnioo zJ&Xr}p&2GIi`XGf5Oz~kBQw{oq;=Je-bnXFbe{YR_Vo|={64?$@A;jxGqqq+3#ONC z#1?M|4@%wj!8&_Hj#QmBiq zA8T!dtaAV*hO2apRj^s92kAGTkXbs`iR_bx-tUQ0sA-wq>3fJ;ANTj6*6Fwlwf0_X zL#^kBy{Ps0$OvF%`NUt;D37`UqoS=t!|i)IU>!&{!aBxQk(-HRud;U&Kas2&dV2B( zl2t?fQ!&))PbN@ncPfKg7t|-Hb^VbJMaR?qC^|XQie?>{eUDm~=8zv{MOz6r{}#2H zGhb13=*1P_&@qx7MU7Rj0w`*DeHLg|OxMT0yzyb{;A1n3ht1HvfDm*g_k6E~+e6f-^X`K`w%FzW*1=?ZUk3 z!K2j3W0X!Kk63Y9HVaH_r+2II4)*c%A!-1>AQurn?ZM@GwZ=`$c>ENt+rkX=TvQp{ z4YlFrmH^FRH@jCA>;XR5Nh`6V>rFnZ;?+@_!ovT@TYBk+vGnL%8lqFc<5Lx!pI^U8 hGnUe&syiv*rw?c^uy0(z!^o~}{*m9wUGp?Z{{c@`Glu{G diff --git a/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree b/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree index 4aa91d901333501e83dc5aaa62e17b282d79b192..5f035b2dafcc1d30a3f7715cfb86f6d159f4c475 100644 GIT binary patch delta 62 zcmZotYEs(Z#$sq#o|2lHW?`V8W@>1jlw@X+nq-okYG|37mSk>fZklXnk!E6QX<}}e Rlw_Kmn4D;^xtyh&2LMdg5}5!1 delta 62 zcmZotYEs(Z#$s4tmRy{eZW^y|l#-NYker%qY;Iv}nVMvfVw#+oW@2n$n3QIiXlayc Rl4@w2lw@MGxtyh&2LN6T650R& diff --git a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree index 5b1d4b109db7827c718cef7858efa6f6a52c029c..f6bbd7f1dcee0d8a5ddb0ce0d32156847502bdaf 100644 GIT binary patch delta 64 zcmccfmGRD3#ti|ChKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q T=7vd0rpbxPi3Xb+8KWx!;!YJn delta 64 zcmccfmGRD3#ti|Ch6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG TMyV#LhQ>)rCPteZ8KWx!^Mw_G diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree index 1c8f69409924ccb1829fb0606e7daa5b2308d9a3..f6ffbd6187a60b082d895139919cc01aa175b440 100644 GIT binary patch delta 62 zcmZ2yxXy4xHlv|oc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= RQj%$MVsfIv<~fW);s9_D66F8@ delta 62 zcmZ2yxXy4xHltyIS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) RNvff7Qj&?$<~fW);sAk06D9xv diff --git a/master/.doctrees/cleanlab/datalab/guide/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree index 02b4cea44034373424c2052c2017f0e93e480a3e..c21eadf334dbc567d6160efe74c1eee68e734f86 100644 GIT binary patch delta 62 zcmdlUw>@q{G^3$mc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= RQj%$MVsfIv<{rk?x&Vgh6W{;< delta 62 zcmdlUw>@q{G^1gGS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) RNvff7Qj&?$<{rk?x&W9U6d?cr diff --git a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree index b66659c262250b2920959b84ab2656225d37b032..e0b2137a3e78e86e688d95fb7ecdf2b5fd1e7f22 100644 GIT binary patch delta 85 zcmX@IK;Xy%feopQMuz1nsi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q l=7vd0rpbxPi3a+UV+E9(cQLl_Vgz9(AZFgai;<;^4*;&E8v6hM delta 85 zcmX@IK;Xy%feopQMg?Zc#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG lMyV#LhQ>)rCPw;`V+E9(cQLl_Vgz9(AZFgai;<;^4*1VNLl200E*F!T#m;ygqNJwk}fRT~y=q z1cTmCW2kX~Xs4+Fj3vt=;>k&CCgrY}hT8H=%ovmfoKEq5Q3y1hhL03qHsR{bRXtg( zvu^<%h=B72OYZ6-S#}4DY&Ua#WfrXR7-m?9|NF#8SY$eU!`uQR@Hy2zM&KRpK_j)7 zF1%nwJ?`zr#zc?Kws_v;+}lM>*%pyp@>CuOJ-OU#2cX@tcIFD0MeLPph_ZqUS6~ft zSh})>t$*FhkJgiyof?`e_6P0L;3_n(br72I`MqQlZ*LpMLWm0I_IpUrbjPCN+Kxk5 zeo^7ul|UL@Q%&sIh^Zdn`JOg|psIn-4x_4;HC^N-WgIuUJgqqr zrz`WJW^QMJfeLZxyFz5sYw8+&>rfXqO6uzVI8O}z^qdVapDgq9+m%$x>PM@@rQd%< zqR()M`q2wkV;(G`@9s>baWfuN9Tw5{cV`Li!zFw2Xas4b2PRxx5!hsH5j~A8;|DBN zvbcg8KpRq>>Ge`PgHpa{DNU1aJVUP`cR}Vfq++=a4h0XaP$9ZjPz%xzYN~XC=U385 zArwS2yIoz#;~S|&UU?Bci7bg--h7q%ky4UnS(4ejd_7tDrVaEVUXoNL-NNQPYsAXi zny6fRQb~W1HXAI~{GFAjclKjrhb_sNl7&~oigYQ@_}r<02AJa)I~XQe!?GH9Ym zCK=7dHhwERTQfx-KS*|D(R9bsc)s~KrGRsJ1fI|BJ%qh>T6sqwO_#HJ=_(SWGLa_W z4GEedzdng|{o^d7{qzm8ZZa%Vd3guvsw_drDLP{)4Mkv<)^lV*zU8#F=^m!p{L70p z0W^lfONAeD z3`&rhV|JQBL#HAlY%HAVsfI7f)CD(0bP|Im)M#*SX~;lhVn{rM8jvW}vphGBXBc0jE=ZT@nIKXW*ly-eVPm34XIrvhYX0q_rfjQ7Eqk~Cgr0oLX9u7?@ebw+m__`#%ZRdq3s+z@ za#*^ug>Beoc!(fp{mjTcd-8NVCke+8^2O9Ij{=k5b5MNa_4GyP&!#9 zj-IJ5gqpd71qQ3c!LN#uO|PkI@Rx?Wu~AZ2_xlB6=*K5)fcaE~pWmpaa@IIjBQE~- z0}_3fLo|+Eup0AV5&gGjBaNH&pz5%Q=-*vsxDS`?Euc}Pk?xpqaYbO03rlD|vW)Mr z)X35*Y65LYb*5L!@eIoO-p6T%eEBijg4_k!(~yeg3OE!zuv~@cT25_9Kd7nF37%d- zqs34Vt?Wi^F;8rwGI?nodJ;JjyS(;14IrhYDsm)qc;yDN@)tJJA-p81O1h5CdCrKH zM_Z^;ds0b%kTx4E%PJKc_)NNMNlt5{-?2MTBXP*Yb_yY1e)<79xWb#IdYL!lap)wx zW;d1ckzHh!KkuaO`$6Z2`^CYp?WaN>eTy8K1ufUSLG5_^=yX!ieTm&|`dKMYD-D_` zkts&Av5nu(&NWPv#}1GkSu{QI44!X3Mrq(&{s%AQ_FlqXJMFx)pJvJj`sh0(NM#~J zz?+gZOMZS5>-u|H#s=sVvTm|0Qh9kD>8cz-=V>}?C=Eqmme%uRLB8d*wdo$AIsDUa zXcA})g_jCH?8qO>J-28FKQT^be&PVVA{YEhZeTQWG!{PdPr7cvM-zrkWZv-dj|mC` zjiA{-EUPE!7vRp73j77oJi6%6KXeILJ{ox6Cw|kDz&B0?d*s;?)6)F2RzIrzEHP2Q p&#W@JrDLV35=bmdA{EIIY+zdD?e(TXCsaU$ssGuswaw%<{RiM2tPcPH diff --git a/master/.doctrees/cleanlab/datalab/internal/adapter/index.doctree b/master/.doctrees/cleanlab/datalab/internal/adapter/index.doctree index 7b1d2b0725c965621f223b9773c0792c8d506779..63a307728e9c1cbc76dba7514275ef5de24bebd2 100644 GIT binary patch delta 62 zcmZ1>vqENrFQcJhc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= RQj%$MVsfIv<~qhzJOEu<6951J delta 62 zcmZ1>vqENrFQZ|BS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) RNvff7Qj&?$<~qhzJOFNy6F~p~ diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree index 1c1d28ad26d29f124ec91efa8473846fa635abd2..b6165c1fcc030cce3953b98652624804fcb2bb10 100644 GIT binary patch delta 5702 zcmbtYe`u6-80VS3x9Q#9-ENHS$h;{xXRf+k>~=SuY_<(2E_ZBrh|}A4=N&8OOl@oG z&Kadqn45l!uV~T!@SnhH@sQs?QWEN~Ah;lj3QAB=#I+~}Q6_!gdnNYw=Y{v*&yVMM zKF|9;ymOzs=Jd0b!r@owM^mY$SSHn&&V-vY*;F(Vjij4m*~Un7V>Fyf zMbbm*p$0KQ^8l*6eh1$vnneUPRNj1#%7o82geePmWd&EdE-#1SRovs=1)A6DRlK=$ zR}rjU%};v=aP?|l>w5`QXLqGo1{puZZGOg}<9zYS16aM|{7Ik-t96_|Si1*Roel0q zRWH}I0Bz#(`uDLsC%L6zl?$3_Qk;#I@IsRpYm){44HS+yyU)L}PPA}|Na_cg407JLCNy_zp2a@f=@VN-;58~kbiB<{UboE$R6H^~QZ4K~9-=XRbQ zJdLK{me0divuP*rk4B2n1X${uMmIQ67t`Y_#G@lWfnC%F3Jez#+xgm91WkmdIe*;6 zZyw8`fv}7OrmWY&Gvco)zgXSTAsS8$;AL@OY98z>&z$u(*v1ne+I!z4ueZ}0{^8fZ(F8r_s1P648GQ71Dg08m zz1Td@-W>$zAiVcYu6gh}c31QXbJ66;pF6=IvAH;fMYu-{))?}QW*X*W|5mSn25NIu z6%%&b6yWOZ5*jld$ZLFslvUK_fTL#(1!TUR?y3EL>c^64H$yW8)xlMizy`X6WOOy{ zao|L0Cr@CzVb5hDNLeREXndKR?4`|WT^-$Y5K?tAM8i&;XuMbD!jwZzvspx@!X7&gbbQNnJH9+9s#^+QXfw^*@^<2Pd*Il7>-Oj*$SIUZ9GBi@vSJ3xYYb<8o?LBCXt$?OzN*d Ut9tG&3VNVK?oj%F?9a>p1N5fAApigX delta 5702 zcmbtYZD>$W8Q&{iw0Yl{^-8(IuvHEj};bz7|;T}#!* zIyM~}&UU^V?{E|Q!#@KzD~I|1m}7$e8VoX+jBT(D6cn-R*eHyB$ljYPqraaE_wVy@ z&UwzghdcL~YfeAgC~{~+YBcd;<3Olxe=-wJrBn4Ao9dg>$?*QhR3g(*AC4q5kwkM{ zx*;8@PbM4c#01R)sM6YoOL&zFnrU2|Z7Sl04PLBG7W`LGINIz!_v$*)vgs;LAkN3O zb8hnyYAkJ@iyHE^`^8v}y&3GXhCxRx1WfAYGQNcAze%$5)6)qd$OM)CTelAH;U>^|3}Y5t`=Q zaTmXNEQJkQ-51m_^U4@|Cl_&Ro1^a*p(dJwc{W5fD$!3(PVD5Fjz?&#I_JN7d9g{X>m`3NsF_hGE3kx3 zO^?wbd>NK7Sr?J|WWvc->Ho1mF8>c60oFJG diff --git a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree index a552c19e8186267113746ee0278a195875e47fd2..82ca7c9191979bbd4dd5235e5b8277ef7b16ef70 100644 GIT binary patch delta 2705 zcmex*o8{|mmJOkdhKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3Xe77@5e>HrY`zX0s@BKNsmXg0xOHP=X_z|&4K&}naR_>dA=|+ z3t3vzCp*XpZ}ydlCeP;d%?>gb$gp{{qhb;}Sq{$G?4apMUexAnR@3fNAkWsxYTA60 z<9$5H(hUye$-WZ2o8x_tvyyFu#O7ez*4z!NE|ncciQiRnWy#EBz{r!>{ITE!CAMc4|0mP$o63vGw^(Ge zTxAR;NnWdd85yCrnX!2{8QLZ@HaBlBXjdYmL<4Fq*lgTW&rL@1W^B%wyr7MH@`+pO zn`>v7krUcbBQ}T6nnX_Jwee8l=I*7Z$?-Qx>yK3(;-vd~^TtCP$VnO-57liJILRYO zx{Z?s{}oJrf0JkO$y@))*S)#)4wC>`+G{8Cec;(__|}=6fEN4*(XRJt6B&tYv*5qy zoMbo}n6ReH@Gy#QU&g_Bmz)v=Xux)$0c2M9vD58^7)2*X%o5&iBEq)rCPtgv7@5e>HrY`zX0s@BKNsmXg0xOHP=X_z|&4K&}naR_>dA=|+ z3t3vzCp*XpZ}ydlCeP;d%?>gb$gp{{qhb;}Sq{$G?4apMUexAnR@3fNAkWsxYTA60 z<9$5H(hUye$-WZ2o8x_tvyyFu#O7ez*4z!NE|ncciQiRnWy#EBz{r!>{ITE!CAMc4|0mP$o63vGw^(Ge zTxAR;NnWdd85yCrnX!2{8QLZ@HaBlBXjdYmL<4Fq*lgTW&rL@1W^B%wyr7MH@`+pO zn`>v7krUcbBQ}T6nnX_Jwee8l=I*7Z$?-Qx>yK3(;-vd~^TtCP$VnO-57liJILRYO zx{Z?s{}oJrf0JkO$y@))*S)#)4wC>`+G{8Cec;(__|}=6fEN4*(XRJt6B&tYv*5qy zoMbo}n6ReH@Gy#QU&g_Bmz)v=Xux)$0c2M9vD58^7)2*X%o5&iBEq)rCPtg<7+aV~*Eac~VZr1D;sTpDv-k*+t-YB{omR5Ko3Be8VkS@fW-D1+ z7P7SVY&KCcBv0$a&FboIWW*0JpeJtD*Stwy98KJON+*z9Tkq;!Cd1av4-L1llNGv^ zo9(T7GRV?exH)&R3mbV_C+9Ba-JG!MlQJ3F1s{}c{&`M`oFoC%I{D{0$<4Pfv+|H` i^Jc*Ze_2V_Hd*jN-{!N=428+i3d!;Oo7erSW&{8cNN_R$ diff --git a/master/.doctrees/cleanlab/datalab/internal/index.doctree b/master/.doctrees/cleanlab/datalab/internal/index.doctree index c90b8b09bd3398da514b44e82c3f48908de3bae9..b3e3de48ae01f6529077fe8f6faf6f8ae3b863d9 100644 GIT binary patch delta 122 zcmeBC=~3C>&uC~^o|2lHW?`V8W@>1jlw@X+nq-okYG|37mSk>fZklXnk!E6QX<}}e flw_Kmn4D;^xq)#mvk}SK^d~2&uCa+mRy{eZW^y|l#-NYker%qY;Iv}nVMvfVw#+oW@2n$n3QIiXlayc fl4@w2lw@MGxq)#mvk}SK^d~2ww6qQT}4MoD(kwN2hAUa;ATvz>`7t!bMRcoewF()y4rU2&5Olz1ld$q8?E zkThl?--yk8aw=?OYv?J&+-8RWd1kU5Zm?M{WHXsjzd0dVmb}y+HyNmP ivt~M*E!hs|-F$k9Hkm;-*&%>;^64cqo7XM7F8}}uC0sE8 delta 1125 zcmeBu&(!;#X+t=pVS!n4abmh@yuMLNQkp?>YO=Apg|TI7l0k}Ta$=f^v4LSynqi`) zQL0I*p>a}@iP7c`MoD(kwN2hAUa;ATvz>`7t!bMRcoewF()y4rU2&5Olz1ld$q8?E zkThl?--yk8aw=?OYv?J&+-8RWd1kU5Zm?M{WHXsjzd0dVmb}y+HyNmP ivt~M*E!hs|-F$k9Hkm;-*&%>;^64cqo7XM7F8~1T`e4HV diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree index 4476af8d1b007681a55a7cdd3807d0ff1793f8ad..69b2af7673932e83786a5a8b5e2b248c28ef614b 100644 GIT binary patch delta 62 zcmew$^+9TbE0dvNc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= RQj%$MVsfIv<}#*VTmXHm6U+br delta 62 zcmew$^+9TbE0bY?S#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) RNvff7Qj&?$<}#*VTmX*Z6b%3X diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree index d4e4e95dd3fdd54fa6ff25903e042cc5590adc87..8b2b6d47b85b06dd6e50fa9251b5aad261e67475 100644 GIT binary patch delta 2628 zcmccdp5?}SmJQL2hKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XE<81IsyOVOxqvjlSk=EeSN$Vg3-Ax21Swh8Sd$ImeBo5INoZJ_qe_0e^d z*!(K?6&WE8_xHg>JxXl9ll+Jh+gGPAB_q-{H|CtgL%khskx0fV046iwPC{q&sKx!}-B%q-z64{LIb&mX&dk zsnu=+6B&NpT)0b=oC>CJ*Q(8lhtfF7aIB(H!Q_TF(vu%t5ZpZF%mi|Z7pM`NA6(c@ zUfm3Gf!gM{`;9#0d0=zH8zoILv;yOJ`Z^Itw#mvZ!rNQL7+;VX+mjcVv2M3fU`!=5 Mp>5ArXUr7<0J=s#3jhEB delta 2628 zcmccdp5?}SmJQL2h6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtHc81IsyOVOxqvjlSk=EeSN$Vg3-Ax21Swh8Sd$ImeBo5INoZJ_qe_0e^d z*!(K?6&WE8_xHg>JxXl9ll+Jh+gGPAB_q-{H|CtgL%khskx0fV046iwPC{q&sKx!}-B%q-z64{LIb&mX&dk zsnu=+6B&NpT)0b=oC>CJ*Q(8lhtfF7aIB(H!Q_TF(vu%t5ZpZF%mi|Z7pM`NA6(c@ zUfm3Gf!gM{`;9#0d0=zH8zoILv;yOJ`Z^Itw#mvZ!rNQL7+;VX+mjcVv2M3fU`!=5 Mp>5ArXUr7<02G->_5c6? diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree index 11d9e0e3fa134823e1d011dbc5794a0141b8c28c..448fe235f3829740b9fefad037d5c8f93c2fc470 100644 GIT binary patch delta 2632 zcmaERn&s_jmJNZ7wua>?si|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3a*9`N_rl#rdU0$*Gek+8b{^$T*kLkYrlB_WVQ+7B zUB4u^0EEejzP`gh!hj(*WA1fOL+BK%8kZbz~FW$|O^QV$)J4pNPWoG2s?w`gs z`NKM%$tJNu)2E9ws&2O0c!<0NkUcs6y!2!pf1b_z_e>z8r~#%SxDk{0?_u36esB$W z^>6m(`16Z+$cjyn3)D6rzVk_gEbaZ%<9Qfaw^y+-zU3g#0AMa+-L50R7)EA#nBFVJ V$g$l^oKcFr04UiGY`Z;S1OPz?K34z$ delta 2632 zcmaERn&s_jmJNZ7wgqO%#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPw-x`N_rl#rdU0$*Gek+8b{^$T*kLkYrlB_WVQ+7B zUB4u^0EEejzP`gh!hj(*WA1fOL+BK%8kZbz~FW$|O^QV$)J4pNPWoG2s?w`gs z`NKM%$tJNu)2E9ws&2O0c!<0NkUcs6y!2!pf1b_z_e>z8r~#%SxDk{0?_u36esB$W z^>6m(`16Z+$cjyn3)D6rzVk_gEbaZ%<9Qfaw^y+-zU3g#0AMa+-L50R7)EA#nBFVJ V$g$l^oKcFr04UiGY`Z;S1OP@DNWcI9 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree index 0f4de1af3ef7e205242aa11d7440098ffe5af5ba..a3a3a35e51b70b971c7afda07d6a8a06b74b8337 100644 GIT binary patch delta 2563 zcmex0mF3q|mJNZ7wua>?si|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3a*9`N_rl#rdU0$*GeYcNuR!$T*kLkYrHY?fMl{eoO@Zlm~`{cE<9FyNm|0Cao&1+?SDRY4E*O+9CFjcgK+oFf4nov4FiyNPrtY1`X8iSDkz;2 z+fzgCk!kzp2jQiZ*lZbfjZB*-gZ%wDb{@Hr)}UvwIWD<|oy;WDprM@8+3xF64&+#E8|+N}S|+qT6WmoZf6U3bbFFVoA2`dMcYQ z%p)@h+a@nqBe*$wMIU*^M%(5EYc}wb?(fZwy9CJ425fdr+#G!3iU#R6LRu`qpnCC9 lh}?z)D7`0q)rCPw-x`N_rl#rdU0$*GeYcNuR!$T*kLkYrHY?fMl{eoO@Zlm~`{cE<9FyNm|0Cao&1+?SDRY4E*O+9CFjcgK+oFf4nov4FiyNPrtY1`X8iSDkz;2 z+fzgCk!kzp2jQiZ*lZbfjZB*-gZ%wDb{@Hr)}UvwIWD<|oy;WDprM@8+3xF64&+#E8|+N}S|+qT6WmoZf6U3bbFFVoA2`dMcYQ z%p)@h+a@nqBe*$wMIU*^M%(5EYc}wb?(fZwy9CJ425fdr+#G!3iU#R6LRu`qpnCC9 lh}?z)D7`0q)yo*bO zl{_OhcL;1?CsXSu2_5o6taI}XDK<6oY@IyAF`7J`o7Xy>qb#6VH~V@tkm+S$RD73y=}Yn?+L!$P0(A%?@cF$@4$NX8x=cGHnJsJTT_~CAPoM*Q3Pt--SCUv3+7` z4Y`q~;KsfAa@8qLvXV{rWIbQ5&B?7CJVvJW&Fc<)B(Hkwn!N6Sz~-#u+T{7W zYjeOwQSxfXuFVQJZOOECvR*FFW|ha1b=>{x}+?!**Ns_65J5c|2 SJ+kZuwWRpBFE(Z@W(NRns2aEc delta 2506 zcmbRCjb++5mJN}Nh6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtgP7+;W~ZL;Hvn#uAk4WwHL)Y&)yo*bO zl{_OhcL;1?CsXSu2_5o6taI}XDK<6oY@IyAF`7J`o7Xy>qb#6VH~V@tkm+S$RD73y=}Yn?+L!$P0(A%?@cF$@4$NX8x=cGHnJsJTT_~CAPoM*Q3Pt--SCUv3+7` z4Y`q~;KsfAa@8qLvXV{rWIbQ5&B?7CJVvJW&Fc<)B(Hkwn!N6Sz~-#u+T{7W zYjeOwQSxfXuFVQJZOOECvR*FFW|ha1b=>{x}+?!**Ns_65J5c|2 SJ+kZuwWRpBFE(Z@W(NSdTqNKC diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree index ab1f1b5a90cf86d53a7155ab0ed8d97be56fcac1..fd8756a73c0ff4b1983b9900442f17382a94f686 100644 GIT binary patch delta 3034 zcmZ3sg>~5$)(xJFhKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3Xdi7%z~aZL(oY&UA|iM*hj)mh$65c%L(kb#v4xr+eBFp_b+kvg?cZ>k8FT)`K delta 3034 zcmZ3sg>~5$)(xJFh6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtg97%z~aZL(oY&UA|iM*hj)mh$65c%L(kb#v4xr+eBFp_b+kvg?cZ>k*V$YHQ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree index a407495b950d343124abbd6622247da257e66a8a..52a00d0f6d754573eddc1f6704e58d0d92badf94 100644 GIT binary patch delta 62 zcmaDV^HgR-Fr%Sic}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= RQj%$MVsfIv<`%|>JOF%16TJWc delta 62 zcmaDV^HgR-Fr#6CS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) RNvff7Qj&?$<`%|>JOGV<6aD}I diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree index fb7211446f524de3c71ba770ec56e39ef553e3d7..afd1ba366940cf4907a7740f7c712a59df383d0e 100644 GIT binary patch delta 2706 zcmbRBnq}5&mJObahKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3Xdi7(bGsZSukRrs)Ta8TlvcvJ{eTD^UB)$pS{go2yw5vXHG^e6uXK z7%O?&H~$sr;vi4!bfARxdV)IhX z26DYEU?j1bUFRYb*^Zakyi31_OxrgL81XVwVDo>|6=d2BOy&}sJ1yQ*;_oP%4oYl4 zVz-tO+k>4dDG9oDZuOLC=l8xtp5u3HzT&r=ytKY|b8Sc{c_F@c^MSDE#$9ZwdUHqX0<+f-Y?o* zw|y&lQC&2-ZoAHAracRJ$#OhMyY%Gs*SR<6oNDDE--ylYuQO?qrG3`+8b-z+)rCPtg97(bGsZSukRrs)Ta8TlvcvJ{eTD^UB)$pS{go2yw5vXHG^e6uXK z7%O?&H~$sr;vi4!bfARxdV)IhX z26DYEU?j1bUFRYb*^Zakyi31_OxrgL81XVwVDo>|6=d2BOy&}sJ1yQ*;_oP%4oYl4 zVz-tO+k>4dDG9oDZuOLC=l8xtp5u3HzT&r=ytKY|b8Sc{c_F@c^MSDE#$9ZwdUHqX0<+f-Y?o* zw|y&lQC&2-ZoAHAracRJ$#OhMyY%Gs*SR<6oNDDE--ylYuQO?qrG3`+8b-z+3aH-A6LxWvSD`dbR;cIN9C65ngRH`- zX|h9`;O2zWo5|Jw;1ciV%U8CN>v)iMkGrqPwcTMk&*qQMn#s#9K)(aE8^7h@Bs1s~ z>e(hw_{zPR`FkOG9>|#P$HJ(w)s(S;ykaI}J5c{xUb37Iwp)7pdLhPq9tsSY4m5y! z`(1g)Z5m{m(lq_QJ0sh6A5X^H>i~uW5!+HP! delta 3021 zcmdmUn03!#)(zf_h6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtfU7_XC|ZL;9Jyy-XM8TltOvJ{eTD@gm~1zf_LRaoz`kf(j~03aH-A6LxWvSD`dbR;cIN9C65ngRH`- zX|h9`;O2zWo5|Jw;1ciV%U8CN>v)iMkGrqPwcTMk&*qQMn#s#9K)(aE8^7h@Bs1s~ z>e(hw_{zPR`FkOG9>|#P$HJ(w)s(S;ykaI}J5c{xUb37Iwp)7pdLhPq9tsSY4m5y! z`(1g)Z5m{m(lq_QJ0sh6A5X^H>i~wp@&#C|b diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree index b2234e9b1dc9c6717ff22071ea47aeaee82de40a..cc256a83615b93f913c25f953083753cc77a9399 100644 GIT binary patch delta 2688 zcmbuA%_~Gv7{(jsjysbvB3Tf((Aa3^GI#EbGKgYIu|blLx#Ql#Mj4G7`AEt_qjAWB zd~8q_X1o$BDH|Ir6p^gi2rK!z`3pSr2mJay&w0;#&efvvYS9>PFqDhoK2_B`PKPFm zvZA;>s^SW&qF2=vS(2ro+oQQ8uS*scMG6Lj0jGo2rn=aMouhq22PgiUXOEQwY}uSe zM``s27@vg5C|z69jS$Lc%a)JTjj$Slqv0Gs51DnGr=fyk?6e|OBYfk*vzgFb&8qBX zD0`H+(sT5{7s@8F%8afZD=AQ5MpDHbYMqfzP%H1<0j%^`*^~B zdydxEp@AnF{U|!ubcX~!O^h*LYXMcXbR&287bjn96glNTa}lk!ALGG`sH8KHjJ>T3 z+9JL8TJL-^=1(P&Oz&~c4i1d1^;@ZDa2I**^q~nME1iuTT7ae0@dtM%$x{v+h})LZ zsWA48y!N_X$Iq7Ek#9=JH`X&Kv^kC1b$oyOAPwq1L;3L#aO3DSah?Ym$5?#W#G;oL wYPfhp<{i6Vp!b_W;7z00y~zrO7G5~=KPAu^_rCJ9_m*n{?eQ<3A|JOIKQo16{Qv*} delta 2688 zcmbuA&nts*9LF1b=CL*t$$@wZ%|-k1Y-Y+3#Y%A*Nq+2^O}MB{)E1MJgQo3E4&=uL z06UI%qHt zOFpB=>k(~rqTMSQhpisB&mtO4lF#I}o4ppVNt7gu*}$5!Eo@uQ(UGE;GyR)q>H0CY zmRmrlIjss94?|>xZq*tkgfiOIm11>+tV!U=U*zN=vyAgJQC5kaR*;&6Z#;M*6FM7N zgFY9^9wDy!5~F1qEhg6_-%!g7qA=^7eheO6mHJY_R8xV-Ya^ZZD&T z<|(x2bZZM5c(&b%qDvhQNZ@25#2l_N61xVFJN%22!xceJ`OjQI8$D-u@CvHxb7x}j z?uWKW?!7s*oQe5UN@UV|#-|4d#x_T_G&p{MJa_WYgn*VVCQsDBQp)&aZ9duN3_1|k zt!k(|`i#8xvR%e6*4~kCO2)Uh@+hz~kJ@GYXzw@&>b^jw@c?k+D4Dn{fs7#*_p4as yT1{pF)snYcgb<^!mX{76B6Y%zX@GWMlm%R zqFs4&Npv|mAq~@hB=#AZj^CV+IEzf1H*ZKL*H&PVDR0hC*CW$ru=cXd-DLV1teq>j zmD~suC==KmTI9)2Mj{18*aSzm$pr>1o8zkdILI`@-%Mb0QL{8Bnc5q=MK?$EvU8EA zeY5C{Cu9cQ=Jj*+$f;yN!6vXqVDsX|+sO>|%>ru@$Ox{@3%3@N7fI7L-`*!ehK=AN zVS*$7<}D|x$+21SW6NaAE4-UOUaTgkD1>R(yEcoLjO3yCv43;NlMEixwQg4Yn5{{= vHgIt9d$MkKmu4h4RRVJ)>-J{~jIYT|)sy7|*thqoF&-i#Lv05(k29D6FNdeQ delta 2967 zcmaF)oaN)rCPtg<7_X6`ZF-|2WBp_nmO|2PoGi#SeX}j=ViwZ1ZWiQv!%Dig$&5;! zo3{%{3X-mMGo#WZGJ*$aW6$IOm0hG;2-V3w`9g@j)mipF)snYcgb<^!mX{76B6Y%zX@GWMlm%R zqFs4&Npv|mAq~@hB=#AZj^CV+IEzf1H*ZKL*H&PVDR0hC*CW$ru=cXd-DLV1teq>j zmD~suC==KmTI9)2Mj{18*aSzm$pr>1o8zkdILI`@-%Mb0QL{8Bnc5q=MK?$EvU8EA zeY5C{Cu9cQ=Jj*+$f;yN!6vXqVDsX|+sO>|%>ru@$Ox{@3%3@N7fI7L-`*!ehK=AN zVS*$7<}D|x$+21SW6NaAE4-UOUaTgkD1>R(yEcoLjO3yCv43;NlMEixwQg4Yn5{{= vHgIt9d$MkKmu4h4RRVJ)>-J{~jIYT|)sy7|*thqoF&-i#Lv05(k29D6y05e> 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 d48834319106ac1d90e0cc3920a77883b7f8540c..ebadf77439f4530846e60c157934343c3decec50 100644 GIT binary patch delta 62 zcmaDV^HgR-Fr%Sic}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= RQj%$MVsfIv<`%|>JOF%16TJWc delta 62 zcmaDV^HgR-Fr#6CS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) RNvff7Qj&?$<`%|>JOGV<6aD}I 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 37b38bff1f7504a58d0910b95fa87a56017e12a2..f6e3e428de570b64bc86a7c0f55759ae75e60236 100644 GIT binary patch delta 3483 zcmbuC-%C?r7{_^QXIjTiY^4)RkRT!|&tkv4D7+ddl*2?UiSit`7Nf$23o2~r0x7ld zFm_Rm)hdL7tsdM95_wT{CApjEX3+?`=q5ITx+q5H^(Jrk-5>CIzR&mjJkNVL)pc`q z-JI)V4#(B;Kp^NN~nI%*;ceaXpO=z>IswwyyP^C)f7<%Qcmln`;??j3~ zv&QD9^xhLq`as-fJW*ECqG_c0|7Gm->$(p~d~=EJso(XPu~Yl>XdPY9K-`K4i(<+K9J`3?|WE-e1 zaA{>;2Jt;NEqZxPqATwoA)f^ipHYI!YeT33BYnO1qlNZ;;#h2kMX1?^04R8Mki|Iz& delta 3483 zcmbuC-%C?r7{_^QXIgU;Tj|6SB#4m8vmdU!D7+ddl*2?!3ER##7o#6e+(m^AT_B|v z9>y-Jv08;tu+@WmAwn;Tt|WI8-7FeG7v020P#4AMyx!#PzWW0{&-eL$pXYfGr?RQ9 zZ0fUp%zD+SOnD}{$GdDkZ$MW3iv9Qrdyn5M`??iRz+soI-hkE9WAi)wR=d~hu+cSE z1~8S$vue98TD_nkmknm}N#AuC{mQVo6ClgMmujltNH-eC5aDt#M{9d$kgNoHu;tGV zsOcO@ZTBPqWpbJ>c1)P6M~TTV5T8tbriS_qbk`;|SFfrqhQQAPq&8Yg^_!5C#BAd3 zC}LNje+P@`SlKunYAa%L`o!uH2=Shg??RkA&GA<(KYkCHZvA#ogJ&83mS--kV$Me z;hsNMLkHSKEHruhN3A&wE@i-hQeLGx;7Jr2??M-TYN&}B_46H=S_GMiSh^=!g0QDlzrHP@rf_ftevl3Vi7O|t`&HKt?!%= dZ%w#ksQE3PPu^nFEs%ThYt+bB9T-Ckf0*AmN#+iOATriAvEzrB)y1I6temrByH7~zFz(Tzx#JR*SVi_&h(&adQg>U zW1Rb2Jsz*kT<*1U$>plBd0ZB^hgW*MF3Boc-4!;k#ad~RxXWdAx42u(;tpE|sE9o* zx)gdkmhVQSfAJtBPSR)PP6dR%Af}6A=mX(2E2!6SZ#`tr;p!=q1}iH^8$^vsLy9t= z3J!1rB9k=CzhI@M=%Ou%9XUnc_panYfm5_t6GpAA2e#*f*1o!N)Vk^LA;3xzyM$Ks zwEjF`6cKxhb{sv8o-=T~1hs}62T-f#c z=Tib^9-5_&u4_ocIrL}uexN3iL#ulGQS0+d2T`l)N+;IxEcNwY1+2tFwWzT>YzB;f z+8w`n0d2QLY(%ZrC`YYlhSs6hp4cno((%v}+CB0E`KLz$Do1QS8T|CQEqFpxI9$Ae(6E|n}IE2X>Nl&YGgCm`#vtRGvsrnY+Esyq5Xzd z4oHl&tKf}w%#SWXx>^}sIKtMBU3M^JM#S!qKI>tdqi-8nq3mpA50N*||5MUU?7a@? zIDzLJ0bTO~dDo9f9 zlfeN_LS&MLg)dlXDY|G0Vni|#_&!JVl{iyZ%r9-GycEyjiJWGB3R{<*tp<2|~71ja9 zKkZK3ynwb_$~U4`Q&d2$XNT6I)}Giar-2BWDtxgSJ%;^kJl`??9t&24@qEzP}K4L<(iIvT!WSscprA{1@f6CX0(h1?-m^)V~?n!k6YYh+`TygT3$LJUc@?U&gkTfEn6u zD8+!8k#ZHhrjB)^OOURXMi-8;N%)n5tcXJiCy$N0?Mx7-jY7LZ_=GqI!}wfPA!hipfP=f_*Fb E4fGqt(EtDd diff --git a/master/.doctrees/cleanlab/datalab/internal/model_outputs.doctree b/master/.doctrees/cleanlab/datalab/internal/model_outputs.doctree index 02d7152bcaf54cb836aef2b29bf6eaff84502391..1fafdcd7cb73829433c668791dbfdca3df3fa067 100644 GIT binary patch delta 4920 zcmb_gTS!v@80P5Axz$#!a$y%j1j}+8xE+-;g1|^hMS5r|=+-8dhOu=aqqRy(6KDR? zx>9ewlv96lAl@lL>}3XGT1F+5B@$&2QrVeDf*$+U!ydoO|DW&w&S7(tin&Qe>p{U} zI(f+9aHbm#PP0k0*(~V}TbkWr%5XSsqFFTCE$PlQb4HqIvf0e`QhTY9>jWhLqqbco zoEUvL`rj49P}&fC4nkItN2BBY%H|jh&K`{&8@GZz%kM>Ds*>q=ey$qoN z{$TC-NYvjZ_##nH+E{=-u+>T85^U={l`@Uatgz_FuvNvr)KO5K6qZ8F_d>+A;#edL zzmxe2gI{DrRz7gm+_tL^TXrp{?5@Jh*7AYuX~3*hLC5%AP9~PgO6n_6({^7Hf65oJ znJc-kU>HU|R5o>-oemFTUZzlT(~(hR02w|2d)LwH z(>pV}4erXS@b8~rl@xMaBCV()^|xwXd8`4*EQeg(NM1v(dd@lZ5>S*(yQ!)^j^;g5 zqh*b7&l!q za|5T)^ZEhk9Vt7|^SZ}K&yoaZ>r)GG<=j6s4ZtMDlXN?zq~g#U;7S(JJTqL5TuB&S z@+6i-Yci$2=l~5!4F1t|LJm)iZ9`f@a>4vYg|tM-<)pkhP1ENXE}NP~R{&@m9 z`ak+gy7HwH*xz3SYWn$g4^F}#XSW~+zvzKJ&gv*HJBCBwP5*#o-J}4ziItSQL+^@a3)b0R|7ueKcBS0 Ja-*;!>NkErJ(BB26A2$tnGZ&N8F2#lmsq=%-FZf;^}OlB7{TC<`wY347j zEA`e(HSMPcqIsbRv6mT$X&GIhED^B?;JMsQ8x2Y*0xVD zIx?-!vWkoY`qXlZ-C(s@(@a@u**1%zJi}UMPfs%#Ep}sBcB(DiW=ykK(o?xnPyjG$ z-&V>Aiw-VYxMB!OYobm=$SQKFwY*!=D#zgLQd_ZctJt;lP6VbZnvUa_+96PR4!%G3 zMmXyCR@R53{yyFlj(XzSBJ_c+NHj%aTj%lQ33O&zrk3;@mF!6w2GvQHS&aFfi@3%# z63)VJ<-Eb*XIYi-&!TVtE zT6%k8YfhKGw%QT;E%;SQ0q1$rnAD{DUdPL8E&-Y4fU^t9YrxsW`Nv-ailXUSax_KL ztV?XP+#!B)lER?jS=S!)*-0*R98-TG3qnv}f0(SpJap3k7L!LykA zn@iBcQF|5MJ@GA=1R1n@D{`iqqvt41(jUg=}%ZKtV23R%DC1)nIMd+03Xty MPa1!vL0A^?2QT7LVE_OC diff --git a/master/.doctrees/cleanlab/datalab/internal/report.doctree b/master/.doctrees/cleanlab/datalab/internal/report.doctree index d94aa6dc01d043e5c1c469f6b7847cfcad7e4130..a029b43ebe0ec311b54b3aabafe2f22096466797 100644 GIT binary patch delta 1062 zcmeC{X6ox^+ThJ-Xjq<-nwn-|pr2-HXr7d0W|5j?lALO2nVOblZfb6tY-W*WVrpq( zZkUv0nw*%NXt244@je^r+9q%044wRtV?OCRH*e(pMTR@U78>%@l5XMTj{(t>_X-Mc z_T_!TOrG}5djtu7EXtH#cZnp64AR~Y` ze++m?PJ;LtP_?->xR$&Ss@l9X%#?*Je%@l5XMTj{(t>_X-Mc z_T_!TOrG}5djtu7EXtH#cZnp64AR~Y` ze++m?PJ;LtP_?->xR$&Ss@l9X%#?*JeU9)ZHO-V}n@P)-)3#%(imF&C%}FZhq$-=HVi}epanbj~?;RsOK1v78 zC5+^gKopmT>0NLE5F47qAKKm`VndtbpO>1N0jnN=z>g#S9S}NB{jp>3B!q__!?T}0#xiAZ(R=pMiN;Vzi|(>W(HyFuUiGw`X`e_ zd)>V~g<8K3Uqh`g@50vSqa#QN>$xXXN`&~?{aBOtL~%XW0D5KL!|%v+>$yc#z;`ao(*8!rUq^Suga7~l delta 3077 zcmbuA-%C?r7{{5@p%a%*b|W|tj13ApKc?Fhe*`15$T%@Zr6}i|og-pML&YH|f_RZ4 zh|gsNBO-}{B@fYV>}CYr7+wh>)YgST5WDKGpz})p0q5%<@OeMakMH}wCuQ$R+52|H zBR}pn=k@8HscTBsuqD$n)p$ZpT85PEF?BnpO0r?gdQ!1smaG~^OyRueo7X)?T6Bz# z8wwc7zkOj`8l)HgIY6u@Y<|`J91-h@S^jaUz5%dm(fhm*>T840aq5elx+htSera@% zIZL%^TN~O{ngCdgx1fAS#~Y3Zk@)I~QXN$6EWhjb14a^A7$3NUT2sTY_2=~^)cQ9S zLwh~EF@;(`jb25qJGWu$*4Qm1gw@O<6_fx!xfiK-pD3Fof@a<&~?$}-ocRkY8;nPH&Vvk{dZ-9%y_=M10w^f<;eRg!+9T!`a& zLOvSj2)?`e1}IwCd2M|Tn{t}g)|+{CqbmgVJ(vn|@6$=tF7PYQa@c<-QDv`_J6>9d zU5iRw**iw|?Vygil_N!H>gTv`-$bJFAE#oeiMkIiQ+UP4f%g(PgR@48r9a61KCh(q xp^>`}9$usZ=Lj(S)G`|2%`#*c7gyj*A4iY`3S2sB6E~3zMN?c}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= RQj%$MVsfIv=B-S}xB!ua6f6J$ delta 62 zcmew@^;>E~3zK1iS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) RNvff7Qj&?$=B-S}xB#NN6m0+i diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree index 23497f24f21087c66d16bfe43f540a55fce881bf..b6a6e1f92208ef6f575644131012e153d49873f7 100644 GIT binary patch delta 1253 zcmdlng>Ag*W3#8}h6-0u~hf+c$SGwlV?$lOJ}~ delta 1253 zcmdlng>A)rCPtI%7!Q-KYkGhzqtWIiOb^+})I0eFX9Ia!jVFH)FxdQuYXU2o`Zs?N z2xcNv@8k_)rkn2w7m%xe^9HfqWJD4uxQ)Sfvq;vHYd2WGjLa^I?9Z2TrO19?#pf*K zhW!Tt+09$k1cfQl?{0O_icI~}*K0FMZf-5L7oo@i$1cZu@(c*t&dS9oMTYmc1Cvji rBzcw^Z?`gMJWhtC;50iOs9AV>g*W3#8}h6-0u~hf+c$SGwlV?$?s|La diff --git a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree index 2e841753cbe12c570b70c852d8ae3701a5ca4840..1b2d6509f4f84704eac7c79fc0444bbe5a96b898 100644 GIT binary patch delta 13101 zcmbuG`*VzE6vyYhm#~q|=IVZFLzsxj2AhkPB$B2^2|6%qUBgB`Y#IyPqYdUsOb9rX_1~ z)?{<|l-#U&;q!9m&&{&h@_WB(J3yx+n(ZQ$BkXmGhmJ|b^&TE3+v=8cFu8rPVV{ag zZHo>5w%Hk){kZXp0$HTk!rQm7Jq+*{cgK*oJ;Apd7K+oy{xIN}hJ|4dZ@-}hBpqi@ z4pO|DFSY0QUQW%I$zGXYn=q)AeXF^TFDxGgC@K9N$08Z+qZ7*gApHzGk4Z*yrt}&1 z9cfmi#T?R|<9h*AJEdPjiSsjt0YsZ4*h!g#Q0hSI9#pz~>PS>NZ~6-~SJSMqC{>-4 zgi`;U3+GxnKNY2Zzi<+o>*>7NsPz2O0#y3wgA_E^nhz6E>gg5ZP-l9x>w{ej zP%7;WM5XqxU~2gRnEKv9c;^iNwhpcE4IYS=>F{BAHkBUTic)J&tVgBoPM<-gdFAj) zH{D0OFI+*XB^NiKxt3QwL#Y!lZ$YWz>RpuD<;FCW8eB6JrF#Fq z7p11&f={}>cQ&KcoO`iou1OD$q10`Ek3^|Ob>~p(lY00W@sEG2P-@z9xSQe|pMX@Z zBpMBtn9EiA2j!-=AU{;AdD3cB+tWy4sP>VG?xWge{uGJUIVF%PQ0~#Tl!4qH$(MF6 z>qM(i?)6t_Et;>PE9Ily9^GjZs+|x*g~&ynqgE@!Xf(=QXr@D`_Q$@o3$3#$lHvg_ z=SR~Slv&fCrUOjJB@)*Tpf6Bv&o~;4a!m+OLODF{-^WjL=sC zt{hJ2^AukhNwb0dC*t3xT$Y!(!GM^h82M1(fWFJc6CtQeJU)|*K*TPwF`Hfo#S^${ z8pXom9WzO7+5DSwN7$(xx`18-jw6x3P8}iAmQbL0o<~;zy2F5_L5T+_7P$pv62|LV zHrISaUP$?zF-V&Qt2;jS(a~WBB z&4GL19@7ml2_9NT69WLTvvbWAWPy7p=aMYnzC<;NV=Ac>I7?iWlQyNu)^qNj#+&3R zzO1HSfeJV~R~jT6L9sADrj|__sKY}lL3@CuS18pSlWDoOhe}|kq!Z(y1V}W zd9Zwnat@7#rPnq1M08dpd6DAHar2ecXbjH6vb0$ghz>YsO@|gK!mSvDbnxZ{Hsw?| zG?0_W6+@G4(xOa>M*EGWR}WY`^^CFyEe5YYtC+pP5^%yLB^Es=S*=#s43b5WpXw=Y zrK-Uq$5-76y0CPK1AYeMdPo@2O>KwHMIpsL+z_np?+C{3aPSbl6VyBI81QF_>LDMu z9ImCRKJJvkLr4ctUN%;(ZtYGW;^Wz>QPeX9i#ECHeRr%_T8-2h zQ5~YS6d%;88ZWI;Z=uu5mD5YFhP&!!i@R|Jit0kmhZBM{f8f`HO{yg0+7#wiG;!%# z8_{{U>LVT&YU#W(O*5mf4bD=rG<+8BZb;rDu2^ygQ5F<3g(qYoRd#m&8=Epw*?1|c21dBuIL z5q?W?3>h|(9ts~m&M_q&0=P=iPZ+=j;nqgI3%nNJ`RW_smxIPo8M}Nr*b3vNLkT<* zcSY;IO&)-AEcy}jFb7|;#PdI3BIq}Y?^RO%bi0l=$n>mhLC_qne>0Cqg5Jkg&bIZIs{ zUwo!F!WUX$J|5nj;ps6HU2&EU?Sa1?3JmOsl!`acbG%HVEK4JNr}^Ji)6G;h${Dn^s}ryF$T$*5@%U= zCuAZmW|Quj(hHzkDe*E&T#zybAX;p}CZ`QYsY5gOqtX@A$Dz{sGhd>)n&wPGsp{Nl zl=|m9IM=EL@hJ8Cg~@2HW%;?N^up3YRQhOn9GYwGha*sG*~-Z%wX`rArEXvQ45fyy z8;nv78^TcPm5oU#^_SvdDD}veNR(Ri=@XP1uss8%cKzIhN`Kz91eM;|GXu@_!QMQS zN(cNE)VrrQq0;tc=TK>WIegO1 zI=>dBS}Lxf>uRWkd$`lZt0=Yj(q=T*^6KX(HR;NBlq#;>L#h5ZXQ0%;+R-T00k3gw=Co5rKsuSd~FRC{v_p|1p7 zHI~rlDZV<6a)JFPhP_KUEH7~b0WnK4@}a;6eUCRLK~RTyN*Wn~h#g{M7QG3Ir*icS zih#wtXOr5hc(ke_&wPgeoD&48sru?r{`IfY~r#v57| z*M3CqNcrqBNSnOYXbf2v`U%wXH^sE8EhrX^N609mPimgx1EbQPgAY+G@LkWoj4Zw8 z!aZ=G=_Z&2k1nMoUqEc{TyrIb!@ZMzS(fkM2sN4qRZ$6WmN+UWZAy@>XWu9>RVqtzvt(rAZ$A?sm_5e$-o^bbFYotTy<`q(GV4s(C0j@*7kJ6fR zjLKR#0qHOb5! zbr&wdpa^9>vI6#9H1aJa8WoER(-lp09xGd@%R5Rw+{QK=mNuUt)6hOlq=T=h>8}5O z9<0bvD$rQ4M0-VI7AYPaxj2FLe*-!qO!Y_!*4jAtAP#+8&*YLW(`PF;G3!35?z5;3E1&sddg6@Mj~`qn=JV zT#r{hohgHhkPdFVY?4~j#+g9G$GNIeJYfnHZFAImXRKISP40p@BsxM6MQUlw4Oq^6 zf~(+vQZumoKJ^-KAK^IpZ4N>@c=L(_>J4-(2)oVF)XaUWQk}uyo1p?u>(| z3D#PP<#$z$m)5Gc(P`z#>6KUGJ#|~SvvK)}nsu5dM+Im;z^@0JRY}IRwYO8zL?&u& zMVEc5r+BzdOXO7vni+jFyv5+HY*qm zj%9b>L62KoZG!mlS?!08AYS}cD~lnoM^e#ZuylBWK5RG^H>XZp=1d6;LOOWx%6hE{ zeoL_p88(vM6Fz+GV@f*saGlZH&46_t~gQlJ`cKLF!1I9~-Vt6F( z9jJRXdjQT3*N>wo4@<8)v?tj!LidKdK#GlA5~=6+0vkXaoUK=aT5g=9Zy1G&TOZT& z(V__{HnPtdy#jsw08hNz^m`*!Eob;*6R;|9nW!3^=C-VQkTG& zp6gBUg;sA*7Y|Nxa~X}UI7^2Pz+VmQ9~4APro54>?&MPF*79z({X>Q2-%PH1{Rfo} B^osxh diff --git a/master/.doctrees/cleanlab/experimental/coteaching.doctree b/master/.doctrees/cleanlab/experimental/coteaching.doctree index ec099fe9ff71de9792cfb00043e250a0e266d36e..b1e4bb4766ab1167a485e0d2e2c2e406bf744104 100644 GIT binary patch delta 1676 zcmeDE&D8swX+tohtzmgeYHFH=fqt5)p?Ol0nMG=nNph;8WolZIxv9BnvYADiiK(TD zxnWY0X>ww6qJe%&esZyXaeir0a_Z!RjY^X@F&;G}*%YA8pv}LSI@rk5I{E!>{>dU- zg=Fato;*QNX|p!>Lss&%Z=N6+%}Acs$sZ)tH%}AZ$V7qmPhwSM+P?XN#A!-wPLpvW z(`K;0gXPvyV*47!UP^3#q8v_%?a$O4DY5;MMlvO~KhU;hAv4KL5ai#y#$Y7}1=?R& zGE0)F-62$Jb7!zInL)SNA=HVBOq2XFqfu7HOutwEdL@1Dm90Au4EmjD0& delta 1676 zcmeDE&D8swX+tohZGl;Gabmh@yuMLNQkp?>YO=Apg|TI7l0k}Ta$=f^v4LSynqi`) zQL0I*p>a}@iIILvesZyXaeir0a_Z!RjY^X@F&;G}*%YA8pv}LSI@rk5I{E!>{>dU- zg=Fato;*QNX|p!>Lss&%Z=N6+%}Acs$sZ)tH%}AZ$V7qmPhwSM+P?XN#A!-wPLpvW z(`K;0gXPvyV*47!UP^3#q8v_%?a$O4DY5;MMlvO~KhU;hAv4KL5ai#y#$Y7}1=?R& zGE0)F-62$Jb7!zInL)SNA=HVBOq2XFqfu7HOutwEdL@1Dm90HdxWasU7T diff --git a/master/.doctrees/cleanlab/experimental/index.doctree b/master/.doctrees/cleanlab/experimental/index.doctree index a3e5829dee874f7dcad049287e76fdbe22345d79..1b1ac225294a6ef3adecd4fddce85eb1bfad46eb 100644 GIT binary patch delta 117 zcmeyW`Big6IHRFqc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= YQj%$MVsfIv<_<WAxN&QUg9po?O0FAe)p z_{9MI28OPlJaT~KK`@zl3UtajE38lMZ*|Vug&&$rA&;0TQU%mO39YcP3cG6L_;z4Ga|wEP`V#!bcI5TB8VPZR4fRVY_hu`N(03l0@5@P zL6B%&bku?(K?}B^c7#DNZPf+^QR$_4?LiPcwUDUb!P(}J^S=8BJkRg9GxN@DeyuIP z*7ni@IUP?V+(N5jp$UiD^O2C<9Tlk(8g51_!c-0ezCoC-?~WnU>3n1Xmu%VNJqqoP zzChx1yyvD5wPa56e!9>{T5vdBk4ODT^wF`exYQiodn(?AO3l$`q@=bD$xfx5gH>yn-@f$Qy!(6#fccPT?g6 z7IN;2>~wFVWX5UX1L7WV1dI!wBMu&CO}i=v KSa3|(?D!7^XBuJv diff --git a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree index 618aed9f95ad10e2ca5dbbacd52013c4920948c6..b739d1094c32418f4a39945f943dafe7954e5cc7 100644 GIT binary patch delta 15985 zcmbuG`&U%g701`PckaC~2#mu+e39a#N^9a^86ItvAjSZi6h$J4sJzC2B1&p#F^VoD zCTdy6S$a}5VG%1RtuL)jjH?3A2Q%A;P`MTucqvv5cyX2N1v|)=? z=-4}c3o8BSNBv&zS6Y-T%aIbkZ73~3sjX!*QR>-O;apqG(@<*bhE-^;cV2@7 zezf^FD0TYQIcToae*Gd!joS_lM$R5i*pZG>XYPE6=K3!_8>L>Y`Uy&H*s~I)eo-Bd zQvX^r7p0#0?N=x@ukIC;x~bkDmBt;cM5VtxQh?^V?bz!mwc&UWDlLB>rXD*1Q%g?4 zJE!3EeYC>b*%Y))(mD8SQX3DW)FsUosPxU2i>TDx3g2|*&-b9zjJC_@x_TZAa zdz3n(KMu|Hseu-hx_>Yeq|zbAz67xpr7(0%q$wJ^iE{s9WN1g#ogBiD*BLQm(oz$f zhnCr9W(JfS6wJ(MsXh&1yHRb(C>DllyF%F@s;wNuqR=`Qk7J!Ew|@d#g1zRHk@6&~ zLb=HnR*B}jaSF4e+|yRJ4b^@f!>ZAKo<5Z&qFn1U>|<10pTKH?I@5+{*?E-tFp;GL zOy}iC1IY||fYP<&USNAsZb=GTfaY78%B(1Nei}u`_`XI{Y$w!ZU{rrEC-E zP)xOJ*a*e_XK)fLumg=f!m4KjFIN?#oJjBe^|+`x*xP|K*Hdxii-Dh z#*td3ZGiwKvAYt%IARu-&F8_0pSi#_HECp_$o^CcAI4xbhFT}pI>lUjIyI6MotV6sU%gKlHm5h!)| zx}P|SBHjlhUQFY{r3^%@w{1s>=$p{Vk`->-Hmh_47=~OMenyP+8t_%CtLLvgFZt8L zS<)jIFC4yx6JMAny@k|W-!VdLq>Y)PUz;BKuP4 zUZSFM=@N3k=@FmbDjh`At} zyt7^CcP-MJNT=w%CdA(K@#j(u>Oh;@r3N5p*B)=WB2Du{6R-TYlmuUCLL5jdA4s_t z5YN*m%9lYcy%#Re9SVq7t&kUx{b%xUvC!+kgmQpU)nGG^1)iD*!saB3>uL`O{X7; zHF#6QDfu2ecIpmsAIhER2IsoN!B1pgWC8x41s4w9d|9?+rrcIJ`iJOxh-ikArC&CB zd*pCIHe^~s2NE&N*FdqeoS{@7Z%BL60|z5j78@Fod;al)7`bo=rh#7=$|idfh||WG zn@$?MXv984v&SKQ>$RocFd^K7xV$OuGeaMGBkDcRg+nNHwi%j6f`O2w&rl9mM2G_^ zvEOhP9SU4o)DIer$1f;$&uSrDd}zR+^unJ7^kZaAh%(W$V?;Ov^U?bZW?uA};d!bZ zp;V$L0evt|vBJwE!~s+huB-#5bl2GR#x&&wil@c|1`lm&?HXrYGKWTk&>9@Q-d~BhUnT1j1z}a1tCps%{55ULZ55Nl_obJN-G6ihn%X z8mRi9(-VyxtL6dg?0P>DVm~s6tA4;+9nG<->yf9EPx{n_wCHIyCfNNZ=el@ostTR* zy0~qg+PfSib0H4k1s|%3m_eC+aF!nxo>7~SB@^M7(?1*4Xyk^|#m!CXa%9ri#W(+^ zCcxzO3o7!JFG()uFb+EIwcB>`8jo~7{b6dRz7cAngiv0G$MLu+2y$6s<^!7wJ-2)HReZW=6 z=g8V}>UpFhgDDB|YT&AHmMLpjud3`iuPU_2q;A zR*jJ3GqpT^!=haUt`4V`vZiSN0VZz$h(Gnvi(ld8zv+^U~`2)V+&))m& zbIzVKGj+W|b-h7HQ(4H$i0oCF`PPi7p*dN3mh9Z@u*j&e=-ezzjx{?oFFec=l9d;d z86BD%o*NRDl@%Up+sXz2RgHg&-CMR8Fe$Ikb@R4e)=DtBt=iki-sP4L(uI74y~p!M zNVOb!S9_n8;J^dyvQLpbbh*98zZ;O*YW&yPOsf_9w9(O6zD{}Y*acY5PPyd=ZP;QJ z+V@S|ib_BJVZYnZD=kWvZchyFz^LiA#PHGf%h7*C$T0<3HcOmhpAuVw(4F$zK*~;~ zn~mas?}A#LlLV<(+Xv^|H$&1g``(Nsq_JZP%IsB{YmlaPNKfTVL8-R9bpX{KQ*a(7 z+Lu9_*OBM(6$gP}Z4RkgnuALHRu7`NHmoT?sjX$RQR=zZ;apqGlTm8x#u7BwyKle& zKi={ylsa?UJT%wozkCIy&e#DBMvfki-I;R)Q+qtvs%`5L9>)V+pMH`jZk(iw*;QR&Z*7NEIqKmI04Z8+hFO3OchsmD*k)Y8-N z&M7$a0IjfgE)gx0bRIsN)W#zyb!l@2Dt)`<5-K&f!Z)4yi@hi{we1SJu3vo#_pqs> z9i{H>tV46Xb^SjmwerSclzOwf2cYtxrEINz{p~UoyYKE?0IQFtRlTQBD(i!_7`jJusLCQt2>bUx8SPP#8KUQmDpmq1-SRigQB3S~Bwd&bJPquOtxST)+uGpDgQlxuySeS&K1V_7XwXWIAzyMQtu#jzBC z>A3u8Ab}wdP`Y-)%WNOYElp&L(0tb-)9`D`23i+X8=0jKWS)?lt0o1sR#iI@+V)!X4b|er$M^>??TyxmChHVBN zim7%j8>I~W8JxrltOdSqaI9SR`&(HCa69puanb^wv4i>XN0sc_(1X?uNEn<`QSkxJ zI8tjpbRqVp);+A)1H|*E4l+01QpcJ=0}=-I5uljAb)I?hyku!M)t_ODfUa>afUwyN zFaNg}n34awnYDvSkT7`77Z5udm;b3qtf2pOmIyacPd6J0oR-es7h+G^eVbJyJ5O)H zKlHI%K=MztMt*`bGoQXiNv3xmvirkOuMI}YjYcR^7upCU9A1E1Fu^39MYl2S^p!e1 zhn_fzB0c~k&Y%gwQYs?W+qR=r^iAMY$qKh^n^n3A3`5QhKPyUl1Nf@d+4EOllDuj0 z9O*HP7Y^URi7!r;-a+cF?-(IA(xx=&6~tj)nUq3VnbIDlQFU=eu2cmabtJ@_kbNn1 zuToLDbQ!td^oTEPlMbQjav}Dj;X9;X_zit?^4^0|9w*LBd|jrLOck}#kAXl)*jOh5 zct^X?Z(F3dkxtQjO^Ds;lP{zw)Pc6NOASEI&OP3ARhsUFCSLh3DIUJkgxHr>K9sU8 zAfBg8mal+XdOuj6KO7L3tdJLx?I-d`#(13)&aT6xa zq3|Mk7?3xKyjLL8t6s0d3V9*`<+^y(n{rYhkgmQpUp*ju^1)iD*!saB3>uL`O{X7? zHn>y6Y56`pcIpl@K9W1p4bF9kL!Zi?$O8O73oabo`LayOOxdk+ zyX0_DHl$fW2NE$X)IhPLoPksyV@Q6+1qUNl78@Fod;ZCS7`brpr-7dt%BH#!h{MK{ zn@$2c*=A@O4F*D%K0`TN5h3=a zxPHSubSQ9UQ9o!fp17#kT&sm}@t^^N(hGkU(2tR|0m@|8juGMD&&TdJn0e9XhL@;z zlv0VF1oYtq#R@Nv5c^O?u(BSQ(w$@1Tho=3D4rT)mG{uGi|)-;#=!j`#KY;mM5Pn% z%hUnMgUkyR{6^6oj-)76Q-;1ZcO6rcdCO*{6_e8BElOPirsaig$}ldTZB112nX(Ef zpd)w^E?0p0O;5b%j^gf&;P2haCZGxQ2!zWn;3PP4Ox*!=yg;T>lcGL^clwJa75{j& z%~$n6rzaXcUd;j4+4+7V#9m|$R=t3?I+|xyHy}?ZpZ2MXX~}bHl>gA1oa^H8Nh);8 z>*BTrYVUH8%!Sy87ks3~VFqQk!8u-3cvfvfmP~|SPXB0BBas_U7dJPl%aKW67vK7; z8Vi%#FRI8dmmTX}PS;!22ADkmlDZhqdC3(u0Vb!kt2<$GRfpONlgD0Dk)LTetDWDa zBHwL0#dB|{8{nK@?^a8J8^cL{_l|lUE?CGt75VLhlYIDrdLJN@=^Cn+pa~-#0^q; uQh?ho%Dbq=0S|!Rg^_+kiwtog3=&~{=u!kHpLNTNfFejC@z?Ed&Hn|p6t@@v diff --git a/master/.doctrees/cleanlab/experimental/span_classification.doctree b/master/.doctrees/cleanlab/experimental/span_classification.doctree index f9295b7cf1d6e41b2ed43cf7b67877b0637bab37..096df1ade59048e83886dad40fb099b9e75650a3 100644 GIT binary patch delta 976 zcmX>#f$7u)rVXi#hKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XdeG4he2ZSsDll+BjRH<(Gc5u|nUsW$$}TiFZA(p|RsEXOof^0ZDq z%OSj3mcO1nFPDL}hY8MRqQLgmB4Ol(8pQV95(boLUn=vGjG)`RU+D&UK~}lhQ8nF= ztl&!BY!|LCK%UmgcHzRC#WHNz$kcwITy`^SzA6WK+BY94FXbakYs%(RZ4Hb7s8ul? delta 976 zcmX>#f$7u)rVXi#h6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtg5G4he2ZSsDll+BjRH<(Gc5u|nUsW$$}TiFZA(p|RsEXOof^0ZDq z%OSj3mcO1nFPDL}hY8MRqQLgmB4Ol(8pQV95(boLUn=vGjG)`RU+D&UK~}lhQ8nF= ztl&!BY!|LCK%UmgcHzRC#WHNz$kcwITy`^SzA6WK+BY94FXbakYs%(RZ4Hb72uU?9 diff --git a/master/.doctrees/cleanlab/filter.doctree b/master/.doctrees/cleanlab/filter.doctree index 656e4cdbc428ac29677510d4566a32c6b56e780c..261ca03aa717526cf701d76a891ae9745ff7f1fe 100644 GIT binary patch delta 1139 zcmeBrz}oeIb%Qsfp<#JSYHFH=fqt5)p?Ol0nMG=nNph;8WolZIxv9BnvYADiiK(TD zxnWY0X>ww6qQT@E#sg&N+Nf@@c`j2o2bo$sxeJ)d((1A~kH3VKJgt+@Nl9$Z7u`!v zC~Z`C0c+nWzJQ55+c%$+>LXJt*yfkAB4h>`SbLY^H!^~4^G5XroMidec(R>=_~tW) z^U2e0IQ>F6qu}Oy){K&5Sr6486q>S)eEqDG8zh9c8}KlCkeANEjsP1n9cT#a_U|H$ r2c*e&j^On7HjK*KeGM7)xhXJZJJ1w0L$VAonB2+Dzr8h&v6T@3qNQOI delta 1139 zcmeBrz}oeIb%QsfVS!n4abmh@yuMLNQkp?>YO=Apg|TI7l0k}Ta$=f^v4LSynqi`) zQL0I*p>a}@iP7X5#sg&N+Nf@@c`j2o2bo$sxeJ)d((1A~kH3VKJgt+@Nl9$Z7u`!v zC~Z`C0c+nWzJQ55+c%$+>LXJt*yfkAB4h>`SbLY^H!^~4^G5XroMidec(R>=_~tW) z^U2e0IQ>F6qu}Oy){K&5Sr6486q>S)eEqDG8zh9c8}KlCkeANEjsP1n9cT#a_U|H$ r2c*e&j^On7HjK*KeGM7)xhXJZJJ1w0L$VAonB2+Dzr8h&v6T@3)qG{8 diff --git a/master/.doctrees/cleanlab/internal/index.doctree b/master/.doctrees/cleanlab/internal/index.doctree index 8058306d15d0816ef6937eac94ea892fd407b840..53f670c8d82159ee96a217fed41c111d97d5a6ae 100644 GIT binary patch delta 117 zcmdm@yhV9~Kck^xc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= YQj%$MVsfIv<_5-YW-_$duvYK@06~T$3jhEB delta 117 zcmdm@yhV9~KciuRS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) YNvff7Qj&?$<_5-YW-_$duvYK@0CDRhjQ{`u diff --git a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree index 0b1bcc68bfd0ec83e824ddbbb4a20dce6e51cc0e..2b5fce70f30a0c64fc1d45c159c4ad5cff7d6985 100644 GIT binary patch delta 480 zcmcaKo$=Ci#to^AhKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XdeF*1>%Z8D=o%w|>QWn^ld9OK15Ii0nTboT-+F5Xne}*2y(& j!kbre?)rCPtg5F*1>%Z8D=o%w|>QWn^ld9OK15Ii0nTboT-+F5Xne}*2y(& j!kbre?mhg0W+vvkY{1<>y3WlHxqHa5@S}X^W_1yFCem%3 z4AQD0ew$pad!?$$i4u^lpJdLFVe96P@}gvD1N-xU(m!&wR;aO(tJPeym0Yc5I_xZD z#;Lhs7rC}po6I365kBND+`Q9fA{!YF25Oxw*TAv)i}N>evb4ucz9T8Txhv)!Il=Xi z8?1e@d;|Mt`J@O@vRn{7nXf@~a$k(VW{rv{@`5jVGhai7JejsnP?g#&vw)j}0_|s4 zWUG;-J!UfhUAE1q&u`};Py1&6yIB_GY2D5y##m3Dr(?DY$uk=2kY#TWuzcs=e$|_? GpAi7{VG*qW delta 1690 zcmaDdi}lGY)(z2&h6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtHc7(bJt>mhg0W+vvkY{1<>y3WlHxqHa5@S}X^W_1yFCem%3 z4AQD0ew$pad!?$$i4u^lpJdLFVe96P@}gvD1N-xU(m!&wR;aO(tJPeym0Yc5I_xZD z#;Lhs7rC}po6I365kBND+`Q9fA{!YF25Oxw*TAv)i}N>evb4ucz9T8Txhv)!Il=Xi z8?1e@d;|Mt`J@O@vRn{7nXf@~a$k(VW{rv{@`5jVGhai7JejsnP?g#&vw)j}0_|s4 zWUG;-J!UfhUAE1q&u`};Py1&6yIB_GY2D5y##m3Dr(?DY$uk=2kY#TWuzcs=e$|_? GpAi7=hZxBK diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree index e0c18601c88a4796fe5864ed0bab2923927c5b5d..fc3a0dc65410dfb129c39505a306f01ae5557d4d 100644 GIT binary patch delta 1932 zcmbRDmTBHwrVZ(ghKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XczFtU=NZSqH!#LfE56Uozhk92oVUZ^`|^At8A4zje)*qqOAL5^D& z>dx3)Ah@21EL&%8ekn4EJX>dOb`?KNuC14(K9j4pU+x&WTIH2)kn7ijDqG04)m~!< z*|utt6R(>W>Ruq%vDSu5$)rCPtfQFtU=NZSqH!#LfE56Uozhk92oVUZ^`|^At8A4zje)*qqOAL5^D& z>dx3)Ah@21EL&%8ekn4EJX>dOb`?KNuC14(K9j4pU+x&WTIH2)kn7ijDqG04)m~!< z*|utt6R(>W>Ruq%vDSu5$r0bS9LG6FXUplX#~=XE}*5EUB)&a!C9$^TFowxmEDPP zqK6Sl5xwXFRtG#rMOtsVP$3Z!C8AUiW%Z(Z5oMichEnhLU4Ovm`~CiY=XWlg7a`M& zkm-ttEON9|DN1#|J*V2`$n$!0^A)etr#K3fYHyw^&*jU_uXef$oOuqf*X8s0JocQb zI$vW>V_nl$pTdIfEOwVXbVRHo$qJqt^MzojcIpAOuyck64CklgE*mhE^;=WnL^!EO9h>bzJecq_A)S$NyD16bEpeq+WqtZtRRnN@++ ztnyE@m!q!3bGkv-b8E6el25d6MA5W`C(%+pu7nt9Dpa0PP>xMR4Yw54S|QgOW+^i8 zw30Ne-Z0LxSDV>DX(t{)DwIQXE4P;&#CD;|{grK4)hh2;e+QT?9(0=kOkNKvU+En| z(Gle)GLoLaQF&0A`F?*YGM1h|kZ-K-M>?#dKez5i>ebQ0<`C){+NJ@n+!I&=U`!9I z?1C;K9qm%@p>;0aw;c@}IIs|Ptv}R=x}HC}4A|y8Gq^VaI9NOZKQHQ-4x%hWnZ{Et zIZT){+u3bm!(Cr7n(yqMF$qIi{bM6va=#v1UVj)bd-y&ja(CV^bQf`L7%JuBb?k)5 z9a+OrF?W2}fw>m*%#Wv$Ls55iet|lyqf@^v1$IHW*5}{1ptauk@e6R(dSghl_FAJ^ zqMed>z+6a$!**b8gYLDgBWr?qqQ*t3ViXrE z2sAdb7h_qHL{BBzWQj7%x+r+jSxXimkCCx5q(CzJY?W=TBd?I#=5~`3$IPO=xHSeGu~E{=58WJt$mP85iLhZ9C9-K$ucS>rUuClAdq%- zKCKb+&kzf6b%tTB;~p`Jfy?9saA1rnB#Dep^2QcR^A=g>Ic6&gsME`R+8S?Ec zZrvx>fXVn<$|mMMA+f-$|10yZk6ichx!8>PHH(!_>`J8Vz+N3~gD!9~ks3r!GA&7uJcf|t+6Ok^h$wW> z(wTs1?`rCekDSva*DM~mY1;J2Vf~yHoA=NZpxQ8-*hVv_MDDAH-oO^;W{E+K769hs S0*^wp7<&m-2>Cl)NPh!0j5SsO delta 5860 zcmb`L>r0bS9LG6FXUpl^G7tqqoF5}sp;jEku?J~POR(2=G zi5^BIMf9QzSRL>f6=}WcLPbPGl!#J6l+}ysMU-`(8A`p|cl`mM@Av!ro!_}|UPMeU zBBsjrgb)c>*m}DM(3xrb6Y}g%#LD)N)I4ofUGeWtL(C z&n(Tv>W$)T)(SK0Eo;XENQDZBZszv#1K2K9xxcCft6Jr4YiB{keG;Qm>8{HAPU@$W{$-1c;~7p-%_-fd{$(0(WC8aNn4UC$j^3~Y0r6W)^o94t!6&x_k8fhf!NP2}kp z9VX0~?dUYIfsU^j&3AN89)qDQ@W{v)-V0#M>yP5)58kK8?#>(f?;x%X{bgLdP8=1x zBWvg{;f@d6G1n5F^YIjNDC(}RFHnbdbi%hqz%Gc^`uzJQwASlCegUppcLHhBUTZX0 zv{I4`mgE(4B)TB67jN(Ei zfyPGmVmM2h=&B+cEOBO89S1Mk>qtKG7#S`@3XEl+t+K84OMjwf2$mV#{HYgq%!&vJ6Ry31PAw2&7$^ zO>4#M)5HQ?ol#h8yGx9s_Yye{92mn2X(GFwys^dJ(P0H($aU7Q-RhMq(eqqRhI~7V zoA=07U^4!evWXdwNg^=o|H{03LeBpOnWfJN^6xT|M#%o;09$WFLGQ3lTzg6CP#GZ? zYAQHm(N#8ygMH-b3~a{yn#D>-ccju*V6P6fK^HiYN(~}!EG^B7J%*6u`UGsiVNv9u zWm5pt?iJLN96P5;u1VZ?)67Y+!}>WZHtweBK($dewT0%4kKI=fy_PMQlPmf(S_qhr S2t16?66_^ZDdg{P5&aFrGEFo9 diff --git a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree index 794cbd0a152ee05e0fa34e04e6098051c6d2614e..658da65882146fd255435153591c50b9fec2e4db 100644 GIT binary patch delta 1199 zcmey>$@Hs}X+u1tp<#JSYHFH=fqt5)p?Ol0nMG=nNph;8WolZIxv9BnvYADiiK(TD zxnWY0X>ww6qQT?|j6ccHRmc;zS%7&RITix7PA+KXpB%zkNS4J_n+w=3u#&EIb0N=b z^4wd$nTLNq6X`Zio+wqnIa;Wh99uzJ`$YW7wY69LG8tMoPn5buhBmN2h2>1hh?L2N zJn5V7D+iDhL?ErlRYir!h?qj2@X1|f5}Ru*nAj-LE@!`#gG}vfM7cJHdgQQEpglNX iGCz6RH|Hf>CNHwWC+8*bZ@!TxC_|RbVVet@yBGnMoNhz_ delta 1199 zcmey>$@Hs}X+u1tVS!n4abmh@yuMLNQkp?>YO=Apg|TI7l0k}Ta$=f^v4LSynqi`) zQL0I*p>a}@iP7W1hh?L2N zJn5V7D+iDhL?ErlRYir!h?qj2@X1|f5}Ru*nAj-LE@!`#gG}vfM7cJHdgQQEpglNX iGCz6RH|Hf>CNHwWC+8*bZ@!TxC_|RbVVet@yBGmxQFB-T diff --git a/master/.doctrees/cleanlab/internal/neighbor/index.doctree b/master/.doctrees/cleanlab/internal/neighbor/index.doctree index 51383148f3a7b6d3949847d75b6973e79db3d7af..c16ea609bd22146525b63957ac6d101f96e9f45f 100644 GIT binary patch delta 122 zcmX?Va@1slKck^xc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= fQj%$MVsfIv<_5;oJVqpI)1Ry$Aia48-&$?}CSD~! delta 122 zcmX?Va@1slKciuRS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) fNvff7Qj&?$<_5;oJVqpI)1Ry$Aia48-&$?}SxqIu diff --git a/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree b/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree index bbfdbc3955fa5aeea469e2ed6dfb413eb76a74f2..e91dd9f84c6d49c0db20940d4bee06ca387eae01 100644 GIT binary patch delta 1928 zcmbRJif#5Qwhe)dwua>?si|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3a*9`N_rl#rdU0$*Ge+x~gs7!dS~_NU||Ntx=QrFgK8=bMtYQBo^|t zPCm{ey!kk%D|yWXiQvnh3jok}kSuGpNQeu+{S+}B*rOkr#o>%F1W6=w{dQ4kKm#4WKo-XxNh zY>#kn_P6TdBujhl=5iNLGW@^!qpPGY={8Px!8Ao_hi9FZa2{_Q~h xKiNsw2eNnZc0Msi^*Hi0*KB`O!Wc?sh}Ud?QpU()L%Pk-dXIm5(`Lq*i~wt%So#0} delta 1928 zcmbRJif#5Qwhe)dwgqO%#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPw-x`N_rl#rdU0$*Ge+x~gs7!dS~_NU||Ntx=QrFgK8=bMtYQBo^|t zPCm{ey!kk%D|yWXiQvnh3jok}kSuGpNQeu+{S+}B*rOkr#o>%F1W6=w{dQ4kKm#4WKo-XxNh zY>#kn_P6TdBujhl=5iNLGW@^!qpPGY={8Px!8Ao_hi9FZa2{_Q~h xKiNsw2eNnZc0Msi^*Hi0*KB`O!Wc?sh}Ud?QpU()L%Pk-dXIm5(`Lq*i~#$&V3+^^ diff --git a/master/.doctrees/cleanlab/internal/neighbor/metric.doctree b/master/.doctrees/cleanlab/internal/neighbor/metric.doctree index a9dc5241adff6a2050487683212194b6b61b589e..ec601c30bcb80b97da54fd748fca95d41ec5aaf6 100644 GIT binary patch delta 1023 zcmZo!!_=~dX@fVTp<#JSYHFH=fqt5)p?Ol0nMG=nNph;8WolZIxv9BnvYADiiK(TD zxnWY0X>ww6qQT}G#${w^n|wheWOCIM{>j^z3rV*WqC*KEsC~12 Q?lv_tYzF0`swrKJ0B^KEHvj+t delta 1023 zcmZo!!_=~dX@fVTVS!n4abmh@yuMLNQkp?>YO=Apg|TI7l0k}Ta$=f^v4LSynqi`) zQL0I*p>a}@iP7d7#${w^n|wheWOCIM{>j^z3rV*WqC*KEsC~12 Q?lv_tYzF0`swrKJ0J2y^hyVZp diff --git a/master/.doctrees/cleanlab/internal/neighbor/search.doctree b/master/.doctrees/cleanlab/internal/neighbor/search.doctree index 05b60b9e62b285fa05d6b7698992e2357292bb49..7b4c45407bb25c4c7140de3d69ba304a9a148936 100644 GIT binary patch delta 527 zcmX@{m+{13#tq(#hKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3Xc%7&nliZE`I071Ax6Y|B}?`8ta!3+Y-n+j2^?lCBMCW76i=d=>0u kYW*hWCr*~u)%*mT?RlQ>d04{i-%m4rY delta 527 zcmX@{m+{13#tq(#h6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtfU7&nliZE`I071Ax6Y|B}?`8ta!3+Y-n+j2^?lCBMCW76i=d=>0u kYW*hWCr*~u)%*mT?RlQ>d0OnVt6#xJL diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree index de4c3a0db6162c3f2ab736e37aec9a3531b3247a..2baa9d9f72b5c7994991343069a1d7f6a2164fab 100644 GIT binary patch delta 731 zcmccgg7MM|#tpuVhKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XGF81IpxD_+!P^F^jPg=ATrG)rCPtI%81IpxD_+!P^F^jPg=ATrGwua>?si|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3a*9`N_rl#rdU0$*GeArSvA>W4vofvME5FS)0R{7ci2ib^25uM*hhQ zSPRM0-nID*n;sikTKhKdr*V^+tfWxB*2Ua*aC^Oh|NWZJ&DVA~S|@@(Gx;kh}Pwod-=TyXMiH=)hXUWzD@ NWqa0kU=cr$5dgaA4nhC` delta 1705 zcmdlyhh_5|mJP*>wgqO%#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPw-x`N_rl#rdU0$*GeArSvA>W4vofvME5FS)0R{7ci2ib^25uM*hhQ zSPRM0-nID*n;sikTKhKdr*V^+tfWxB*2Ua*aC^Oh|NWZJ&DVA~S|@@(Gx;kh}Pwod-=TyXMiH=)hXUWzD@ NWqa0kU=cr$5dfVu6OOTwnZ6A=0wSs zEM)K%Z}w8RY-1sNVRM(|41>`)S>_T5>d*wI0w%^EL`a;oBtrbQ%>4lmf57v6o^$R! z=X=wREgHubjn_(;&GuHE*Xt|HUhm7Z6}a8`gZ-~ zztpGwgi-ynWAzr4>dUA^sjsfx0Z`?%bqJ6t(^kJM${a?eA3oEAN=LHWQ0dP(7f|U_ z1zuG8^u`YKOiMQjfGREa2tXD-)0@vbQR&$&`_MWEU)+vT&y}_VR9WtvL5bg%wC?ymx=mwS6Ls$WitPPD_?lM5&{ z5U56}$IskEsq<$WQ0hY8MU=X^AEr7l^`q3DD@ACn4cGocrT@wnRB9yn4m*Zw5Ndf< zvn(Dy5djTdrK}x!Pmf8J+7Yd68jX*I)EDigHJ~D`^XYXJ))FHOD zvQ$cFV}sa)JTlmMkY!;%3E4&3M=XZ)-RuIEz)q?`R04DWc4f50OqZ^(7Oelv`csni zqtiL}H`9r^A6T@Anqy|Vf0qS}SScc8MXHrPZUqL)FXcvRon z%z!O^d#IjN(KgGh;>U;VAH1eistK~n6{OJJ$mDBR(vRLu{-n5bm&FRTAA0(O@fdIm zQgb9<2i{eoY7;2M%+G-a%2lt4o{Q&Y;PMUYBhz>*I;?N(*Nf&HuBD}1UWgX)*yQ>~ zemn}hR!Yd^voda=(NcZ@+*=_NQcY4K;SdI@c!eLu`39=0;XSB+{6IAMJv;&&4JD)x z*6}0BVEtJKQ^m7=JdQHn5u72-?)mkDv$G^}d;2IK}JGWsuaz8$hi% zEV)T^MshPn4{#kgOr*M81hq=>i3A!N;t#;h9ML+;&wcX)O;Q5DL zXwN;~4#W_y8D2?^ll&{72c(L}0jT<`&GHLY{5j8Kgi5#2*T3^mfC!JRNVRGNvnYW9 z_2XE|<7xIGzlwfZR1->C;%m@HXr@JzEN+=J;5&m<{l{Q-v6!YwS{_ALq9zM$BdIP& P6QLzmOzQu^-|PPa!m7B( delta 7878 zcmbuE`%l|d7{+r>*A{7|E%*BtoHZfn3S}3TN$WC2(u@ts$izYI4F*CPV=)XRbE0HR z7BcvXC%fRxHWsoAn>#G;Fc^)KWiElB4oz?>U}F41gv2>ZBE)aY+#m4p2RzT`dEay1 z_j}TgEgHubjn|4;?!FDaW>3v}_qsg4x7y|l`0N`u*$V<*n}5B}Q=M&?$oTG#}jnzk0oVXottRD?EYOo~(61j%cqOIKupqz%%RydCM%w6#H|rEbUS z4N85+PZ%{Q+gEQxsnuEKDD}0qy8x=3whjR@W!f5)1=+)>^n+)+QR#@S6_x&Mzko`g z+898k&unf-&$M`p0I1U9hyrAhGrjqO3zeST){NFU_|i_4dak$)pvqF$3`+dA#10T6 z-95i^KT5q(27N2Sb9!nQa*HB?zuCP5s8yC$Ordp-SGoaWByX)d`m4#z5LbyN?mB{M5)%p zp8`~AX&*$1{*D@es9uLUS$(8f3%vz{vZV86l$v||{wk1qxu*xC2IZ9KKs&5GwSZE4 zdn-}uiH~oh)cLb@D0QLlB1+xb4^v&2`cZ24l>)TZx@&)-(tl+WDm4;(hn+)J2(`4L zQ5FuLjDm))T)Icz)nih{J))Hjqlxj5+@ZWa6)Uwfwb-^;me2n-VFu~8H3sPn>7Kyl ziZi68+l*BvVjkj*iy7%mGW!)fJ4eJos0LcnWU((+uOx%U!Xf@Q5ql?jJWZ4_vk8jh6rX!o0=?m58Q3@UkwHu?dmFo5Z$RRR5WO1bLVi&Lk4pI$blAr@{D5FJYx^#s#Vf|m$pO&m2 zozA(xnNH08z+y$r95d7XyR6rUl_J7cq+00{R$!p~Qfj1@S!P0KBb*{^BKCyTtDQN= z4A|m#gzHHct+UK3etgLO!D~vVst_w*K?>cCY`%6S{piW&Pl`KtS-epDp{G9>j{~3OSNSoVZ=i}Q-i_+V55`i^$D_c}P{R6< zpC3&H>(4oqE}rY-36%8?pF)dJO{nvIUTDBV(3UQK6g|Ms_ssO-Y3@gtK}sL51GVCa z?si|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3a*9`N_rl#rdU0$*GeKQ)MTwW!z^-vMFGl3BtOYpE0#DlCOR8{ssJ# zzp)gOZ9>RqPPPy>vb9HSX6C)dM1l5tfyrcQ-<%+Ph+M4&V&}=!Iypf&a`Q7uCGz7e za`SrW9!hL~B$q~s_V)rCPw-x`N_rl#rdU0$*GeKQ)MTwW!z^-vMFGl3BtOYpE0#DlCOR8{ssJ# zzp)gOZ9>RqPPPy>vb9HSX6C)dM1l5tfyrcQ-<%+Ph+M4&V&}=!Iypf&a`Q7uCGz7e za`SrW9!hL~B$q~s_V06pc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= YQj%$MVsfIv<{HMuY-DH);E>@40D0^qP5=M^ delta 117 zcmaE(_C{@kH=|*JS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) YNvff7Qj&?$<{HMuY-DH);E>@40IE?V&;S4c diff --git a/master/.doctrees/cleanlab/models/keras.doctree b/master/.doctrees/cleanlab/models/keras.doctree index 37b0821374e19e030cd8f268bb3bcb4d5bbe183a..339fae28f27dc48d4f441a391c287327d45b6f3b 100644 GIT binary patch delta 4131 zcmbuC-%FEG7{__aAN!S=Q(06xsnIaa_sz|1F~JKh2RegAbWw=!eBUgSkY>diq?i|C zg>ZsnAu54%l?@JfuB5Dlzyd2GFGT3Z&@LKuVUa?|xw`i`f57*9KF@Q`^L#m*GdY_x zImr;H>dkgtH~dPK;Z=Q_R^!(-PgGX}x}o{JK5w+fZ+N@`k5AP!Z?rAirm#5N%HFw* z%r*vDYwlV(PD)!I!(K9XB8HNONutiq=BpoNq2&(t$KShKLS9h&eHTv75_N^9olDtl z<4^O>W#XbD%pWPj)f!>q)H z`4TbYHD`)&u;x3bWhrX!HpP8j-1TFNy+j=9%-a5vhdkXo{uMSE^K+C0tvtdpQdn;6=uJl zbrs@(i87l{t2*EYyYjjYvA@%6MR>5lyR#M~f^%sPO(fs~>-kcFT#U@Q)Q^jiFd17l zlVdAik?=yz9)6q3Mae_-ObJ*>Y9pJ?|5(7a|G$Tc9sS*ao4suhmfEO4AXkTpzH~q< z(j(~xH?j2W;~)3KB<>-Es4%JIC8glP?ZS+~niKkQn=oTgcm$BilrdO#!FePB7dcPj8i>hR0efDFM*+hbnnmLj<42UTJSi)nKZQpf=k%cPjU zuZ4A7j))eNK@n-7ZhE^O-XbB=9;*|Vc;FN)VnIYDR$3JS6CY~=8!l1POU=rK%T-)B zhG@PD1+=#ttYrPwbw1evE!Z8s<-~~>6J2$um|Sf*cyjUKuW dW>UH0qKiZD0{={GTo}#SPzFIR{@PPne*wNm9>@Ry delta 4131 zcmbuC-AmI^7{__a>E1HaDT_)cH4@YO?M=6s;Dwe0-GW7QQHa02Xql9_Mc5$4ybvpd z6C4ZC5lC0r;6Ud}%1Q_@k$wPZCWfY|-&B4=p#dKkmW367u54?|X1^mS{3*cQ0l0 ztv_|Umx+rCGIy{PS8I@cIRE@G?o?uksVJqNq$UhR87N`;_$2miE5d_$-ksAU5iBeFXd(d@S^P^4axpT?$}lcQ{A6lP zM^3DMMZya;d+=?x044X)v*n;CnN1B_{IP^<|9=lHJN~-`H+wrCEVJ2sNUjboeQAac zq(`a>s)#;*fPXv)Gq{J~qr#+?msfxlw+nLy>lPTsZNi*^=@=lBDQA$j!UZG&7d?m~ z%je_O@CkYDPVe<60hxwAZc*T|)Zv#MfDFN0+hZ>1lOnk52AjkXmeF=EWRL?SmPr|Z z-v}GH91$(3gi_K%tLUv}c#DL{daNb8xaTx1V?jhE23i*cEuU%wBQ8<1OU=NA%T-)B z`e>02is@h<7|6z}em>I;0ql<6cH+XR2N#k#l<6VRa1oUhyB)I_!es;(mf06#q(|<7 cj?}JN>GBx7z&{fs7e)(4l!2FvzxHh2Us7Z*2mk;8 diff --git a/master/.doctrees/cleanlab/multiannotator.doctree b/master/.doctrees/cleanlab/multiannotator.doctree index 58fc781e7727beaae06bccc06ffa4565d17524f7..f45d12a6ec1c69cb24ccf05b7a0d53a2a2c4347e 100644 GIT binary patch delta 1709 zcmX@x#dWreYeO`np<#JSYHFH=fqt5)p?Ol0nMG=nNph;8WolZIxv9BnvYADiiK(TD zxnWY0X>ww6qQT@I#v5ela#VKMe3i*vo;n(5iCt zW9|Fogb+w;j@}0*GHqoudPJ_RlT2ojYwI<0Ei$xjHnj3&CexoPb`Qw)=Q@Y!WH=Ta zr|+HbkZWtH+ahvpJ?QzEoJ8oToCr$#tedTa=82IJ%uxO8n~O4k>?BYBWCsJT>D_M` zMYc<+Fy0U(-;nKnW{gfrr0d_#IFpf^i*$X{f%+1*&t1$I;Ypt6=*jW6T-$+OZ{VcF zfQ8=~?^=*+^YX-u}V NWa)R<4ov&=7y*uH3v~bh delta 1709 zcmX@x#dWreYeO`nVS!n4abmh@yuMLNQkp?>YO=Apg|TI7l0k}Ta$=f^v4LSynqi`) zQL0I*p>a}@iP7X9#v5ela#VKMe3i*vo;n(5iCt zW9|Fogb+w;j@}0*GHqoudPJ_RlT2ojYwI<0Ei$xjHnj3&CexoPb`Qw)=Q@Y!WH=Ta zr|+HbkZWtH+ahvpJ?QzEoJ8oToCr$#tedTa=82IJ%uxO8n~O4k>?BYBWCsJT>D_M` zMYc<+Fy0U(-;nKnW{gfrr0d_#IFpf^i*$X{f%+1*&t1$I;Ypt6=*jW6T-$+OZ{VcF zfQ8=~?^=*+^YX-u}V NWa)R<4ov&=7y;PN5|ID^ diff --git a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree index 60255825b5a69c556fe6fbe5b0b928a54adca102..b33aa9abc9ef3e6c21f0709a0622ad4b4577c7e9 100644 GIT binary patch delta 1200 zcmX@z%W}GxWrHuHp<#JSYHFH=fqt5)p?Ol0nMG=nNph;8WolZIxv9BnvYADiiK(TD zxnWY0X>ww6qQT}mMnN*PO*WKE+pNaCgqd_3L0Ts#vNw>WbJFG|96@YkX`QxtFaLD% zygF^Ow9pSGvTU8c`L^gT@@$>HIacB|xwZ<(d?3Tt&4zMu{H0 ZxOjq(Jli)nJkU}hOKaM8M^?rzMgW6ua3BBx delta 1200 zcmX@z%W}GxWrHuHVS!n4abmh@yuMLNQkp?>YO=Apg|TI7l0k}Ta$=f^v4LSynqi`) zQL0I*p>a}@iP7ddMnN*PO*WKE+pNaCgqd_3L0Ts#vNw>WbJFG|96@YkX`QxtFaLD% zygF^Ow9pSGvTU8c`L^gT@@$>HIacB|xwZ<(d?3Tt&4zMu{H0 ZxOjq(Jli)nJkU}hOKaM8M^?rzMgUeXbu<6~ diff --git a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree index 205a019af71ae19ebdaf3ed2b2818e34a272fbed..9634c16fc6f528e9d9dfcca0e942020c36b09cc5 100644 GIT binary patch delta 751 zcmeBL#@e-vb%Qsfp<#JSYHFH=fqt5)p?Ol0nMG=nNph;8WolZIxv9BnvYADiiK(TD zxnWY0X>ww6qQT}GMm{pMO*aT+Oy8`;JfE3#D?xgTC#SL(lBYFgasZ#|<_R3ytYqrn z9Kd&kOfQ4%*4lho;0Y6%b_4ZmZY~v(WFuFYO=Apg|TI7l0k}Ta$=f^v4LSynqi`) zQL0I*p>a}@iP7d7Mm{pMO*aT+Oy8`;JfE3#D?xgTC#SL(lBYFgasZ#|<_R3ytYqrn z9Kd&kOfQ4%*4lho;0Y6%b_4ZmZY~v(WFuFMWO f+U!%3ORft*kq9y(x2)2hELVV15YOheZRv~vKiudV delta 760 zcmaF+p6Ts-rVZ|lh6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtep7&*w%wvp)MWO f+U!%3ORft*kq9y(x2)2hELVV15YOheZRv~vT4w7e diff --git a/master/.doctrees/cleanlab/object_detection/filter.doctree b/master/.doctrees/cleanlab/object_detection/filter.doctree index 2624a169f6a006de0556c86708fd7dfb1e44016a..7ebb2e497a18089805ac009652dc6059111908a6 100644 GIT binary patch delta 474 zcmbQRl4-(9rVZYVhKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XEv7&nrk%Yi3o@&=|w)rCPtHM7&nrk%Yi3o@&=|wyg_K&uC~^o|2lHW?`V8W@>1jlw@X+nq-okYG|37mSk>fZklXnk!E6QX<}}e Xlw_Kmn4D;^c>-fEIokY~_i+ONBu67A delta 117 zcmeB?>yg_K&uCa+mRy{eZW^y|l#-NYker%qY;Iv}nVMvfVw#+oW@2n$n3QIiXlayc Xl4@w2lw@MGc>-fEIokY~_i+ONRIDSY diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree index 321a65e78b8298cc956e9f12910b79bb03b0d7df..736da51bed38f15531dbb4eb7d2e22583ebe4f09 100644 GIT binary patch delta 1704 zcmdlyiF5NL&JFI2hKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XD^7~hhiYoVau<|j;cOyp^uKGmIxfAR*_Lb9}1Z9c>Hk)159^_z3~ znaObU=7oZP$l?61IWQWvMb58^@)NKxmvd>&m~tY zqk0OtT1B+hk*l>(cRefViEwkEu@Nun+9n4Y=WJGRH1H%{>*mI;iR9###;&x@DA z8E2B|i0K6iOyb*tfl)0$o&z8T)U9On$s^Bz?Oqa0THNI6oetD1w_QkuiA9+#1N^oF IOZ<6^0IJ3cApigX delta 1704 zcmdlyiF5NL&JFI2h6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtGh7~hhiYoVau<|j;cOyp^uKGmIxfAR*_Lb9}1Z9c>Hk)159^_z3~ znaObU=7oZP$l?61IWQWvMb58^@)NKxmvd>&m~tY zqk0OtT1B+hk*l>(cRefViEwkEu@Nun+9n4Y=WJGRH1H%{>*mI;iR9###;&x@DA z8E2B|i0K6iOyb*tfl)0$o&z8T)U9On$s^Bz?Oqa0THNI6oetD1w_QkuiA9+#1N^oF IOZ<6^0OhX{Jpcdz diff --git a/master/.doctrees/cleanlab/object_detection/summary.doctree b/master/.doctrees/cleanlab/object_detection/summary.doctree index d93158e1305ed46ba8ec7a364925151330006002..32142260a1adf9776fdbdbb07efaff9d2cb20e19 100644 GIT binary patch delta 2429 zcmbW&%_~Gv7zXgp^)&_&vzWLhUr|iVW$w&nLKr5A1+KEv-0MzLY&9ESWfn?ES6yXc zF)}NqUYwGhlSe2hOu`CP|5T!-y%qj+iV3 zH7%eRnqN1hZX>1z<)Ewwl$c-c_6H?Rll7<`^_i=T0F@0*o0-B&^G?{qG(F`l3mCPF z*CDw^<&N)Sh;GnT-fdEFxB@93C;c; delta 2429 zcmbW&%_~Gv7zXgp9iM|tV;16?d_^%aGj}jd2*V_?V6L*#+&gzD7D}3puQCfIq^qv7 zuo#(@QZG))PD*yR7N!wP^Pcx~I-3X9%>(P%DDy0|tBaADw&_-H zREfD&O_e(QQm3Z4qit#==9AnWCFY5AdNrTskrc(})mIq-Dia9n={&oBC+uOGo-E4( zM$O`NNUl+7!FM4x;j*q|TBEq2rd#mnEZ)&z-_Ctzzb?gS;dYA#V~)IXy~ zZE3tmTTh!-(AKo;7;H`Y-Jqm5uZGh6_I#Ay_}>6ZnLyA2J+}ls_Fe;xdM1#dk%5t1 zsCAyC;kXr~q4422L}i*yB}*`>&!+0={9>XN%z`)o+u+M+Xjq<-nwn-|pr2-HXr7d0W|5j?lALO2nVOblZfb6tY-W*WVrpq( zZkUv0nw*%NXfU~s@h};>KByUOUc$7Bg*>g(SJX1{Pd>n2NS1cj&C5Ah$+OvY@^TKL z%_h9OtYq2jGTA|jb@Of^0e14VZ+4K1ASaGKsDW(e*eoiuii>ocH-At&L7uH{oAot! z_mghpWT=BDr|%3VQ}=?CvYUN&wUHOiE|V9WRN9=m|0a3fhG@TeFqw&L$182Vd?b|; z+s_}1A=CEF3r>o#k?HUWd$~3nUpPQUA_GS`NPE%kc0=-PpC-o0y1kf}(UeT8_WQ7MAZ}k delta 1486 zcmZo@W@~6>+u+M+SYVc1oS1GJuWyu+lxC2enrv)tVQiV2WRPN-oS0@}Y+#s_W|(Mc zlxmV{Xq=Q}Vl=sq@h};>KByUOUc$7Bg*>g(SJX1{Pd>n2NS1cj&C5Ah$+OvY@^TKL z%_h9OtYq2jGTA|jb@Of^0e14VZ+4K1ASaGKsDW(e*eoiuii>ocH-At&L7uH{oAot! z_mghpWT=BDr|%3VQ}=?CvYUN&wUHOiE|V9WRN9=m|0a3fhG@TeFqw&L$182Vd?b|; z+s_}1A=CEF3r>o#k?HUWd$~3nUpPQUA_GS`NPE%kc0=-PpC-o0y1kf}(UeT8_WPvG}|ly diff --git a/master/.doctrees/cleanlab/rank.doctree b/master/.doctrees/cleanlab/rank.doctree index 572453a648bc69a289b3f6745559cfd415467341..f2d671bc92611330f8043c90db49e8cbadaf6a40 100644 GIT binary patch delta 2066 zcmZ4ggKhl}whiu#hKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XD^7P|LL)!w{G;yoMr z`VBVgD`b%oSks~Q8*ElrTFpeJ{hJL`Pf%ocn|dI*b_2uCU~{V08jAGK*DYZo)9+yW z7aFBdT1ZF?xP-_!mBMfM+eVq_&h{v+#sg(lxwEVy~wVp$wgbz6JKUt}1C@M}jLCl;wWn{WWMqK^%dP2*3KZln7MOAD+xzA+PLd(rVcUUK^F2lY D8#ri; delta 2066 zcmZ4ggKhl}whiu#h6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtGh7P|LL)!w{G;yoMr z`VBVgD`b%oSks~Q8*ElrTFpeJ{hJL`Pf%ocn|dI*b_2uCU~{V08jAGK*DYZo)9+yW z7aFBdT1ZF?xP-_!mBMfM+eVq_&h{v+#sg(lxwEVy~wVp$wgbz6JKUt}1C@M}jLCl;wWn{WWMqK^%dP2*3KZln7MOAD+xzA+PLd(rVcUUK^F2lY D;RtZ7 diff --git a/master/.doctrees/cleanlab/regression/index.doctree b/master/.doctrees/cleanlab/regression/index.doctree index 3895b507b0d934a22f4490b492a4e7a6b40aadb6..4d42b745f0a63bb78770c5aef2a8bb90eb659f41 100644 GIT binary patch delta 121 zcmbOwJ4<#$Fr%Sic}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= cQj%$MVsfIv<`%|wGPF&ez$m&oklB+P0JU8s=>Px# delta 121 zcmbOwJ4<#$Fr#6CS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) cNvff7Qj&?$<`%|wGPF&ez$m&oklB+P0O!giYXATM diff --git a/master/.doctrees/cleanlab/regression/learn.doctree b/master/.doctrees/cleanlab/regression/learn.doctree index 83a68c502f579218dc9265df988464b0eee90c2b..4903009917f5d43eb7df9703246c6e42f1f0de08 100644 GIT binary patch delta 4106 zcmbuC+e=hY6vjFGj80AJI8zaae5fo8tegXmqm)l+pas^bV;9o7IAbM_HEI`nP{bb0 zKKo(tgx*)J96iG;8Ub-1fZ$%irSJ0k;F0beF5BPoGT5GSp*4_&XB%nnPH*y~f2} zr!ib(6MJ@Q+2-B(F+bbIpO&bF!scDN*}Q-}u5=7-5P4YeKF<;ejf#%JF$SV^t`kM4 zhXdG2MaA^_2I1)brOg2(f+`Vo?(Z(Ae08|8S!;ZE)uK~wQ zo~Q2ZN4qF?{|M@O`q3G{Rp^g%uxT0<^YjjI%#^6%$t^U`%adDC*RE$V)OFxRCF<&* z8bw`=nI!6Jne9ehSG}r6U4P8MW4k(;kL_lkn13_jgqDR*XkXhp%%k*huaZO-vgBH5 zxnGIpnK$K%k2UbV4kerNJC)BD_cSFxZ<@hM&+QL zS?TPU(t7|>sW)6VEqu3~Irvaq29;`JW%Sv~Qoz~B)G~4;HN;b?rQlC7ko0`?XU zLZd5%%&UPx939iRYdg!LkxHgwcdFiyA|yqw0T+v8qizExNV+}!G}pvpKyQwdSG zuUPtx+(Xs~YX{Ce4Y%;g9;VUiF6IOVZbpHmLc0x!{du0cgRb_mabSh>G|VMzr}qP_ zITx`1^S#(eA1|1Hs!6=C#VL3;+eyq?cL;P-uNt-bbIdoRvrFV1E^ z@G8#EvS63LwbWZw+!6>of}xysvy2ghoXB;5Y+OI^Th! zGsAxDq#|PGLKtDs1te045tLBJSR>plIXG4*)9sw_!)io!5Y4 zI?vPh4xwEXy?-2aJ@e=+;3}Mtv#@Cz5exJVa7>q|;mH`9=as1)sB71=DC#=!q7rrW zO^>3k`fLJqHO+OSu4`UZqpm;Z;jvwv$i;TEM=ZRVv_s3nBebur?Z#1hctA-Y3t4hr zTIo}wImS)7;$aQEuU*Nc+z#a=uv!08u$@wp8;r*wFK;8S%U5!F+{{){ET*`vOHnyu zWo9}zuJj&;RO)rdEfe2oWi~z(lR+h$SQ&jbvm|gfGPSfENx4l~a5x$ts${FFAdkJp zgV5+|K67he5S`;1ckE&rG*Zb_>`v9|QiP<)b>L!=Y}B2=1WC20kLH_L6zFY|Z{6=E z;}uK2k$cD-W^KTkr(iFi>R}qK?P7Ld;HDKwDzsUE*q`U9yXjgVn*dfgPr+QmR(e0c znzI4>Ki`YZ^zkC=cPv4nkj&F;u-3Q&$t?Q8>>KH&U2Q=o@!!O`Ox#MJ)~i0Wek8wV RdE1O9F2_|B$=`T9<2U6v1IYjY diff --git a/master/.doctrees/cleanlab/regression/rank.doctree b/master/.doctrees/cleanlab/regression/rank.doctree index 42288f642253cd5196e8032c02656ed864400cb2..2e1a5e158a00ccd0066080a2bb45e69bb3238f27 100644 GIT binary patch delta 479 zcmaDpi}Cp^#tpHIhKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XGV7;lrI>w&n_<~vN!xX9Bw`L{R!WEZ|dvUEpJz9%faIfVZoGkMxK i-xIz_hStpw#J7)rCPtI{7;lrI>w&n_<~vN!xX9Bw`L{R!WEZ|dvUEpJz9%faIfVZoGkMxK i-xIz_hStpw#J7)rCPtHM7*`sRtV@4#Ah*oq9%h5dk!<{vH!<~)X~gCpW`8C!^+K($C11Pl qjdq#FcIHRFqc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= fQj%$MVsfIv<_^YMMkA87=}(@(BDUF=xt<#U2=*kg delta 122 zcmX>jdq#FcIHO^KS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) fNvff7Qj&?$<_^YMMkA87=}(@(BDUF=xt<#UJLM%a diff --git a/master/.doctrees/cleanlab/segmentation/rank.doctree b/master/.doctrees/cleanlab/segmentation/rank.doctree index 533e902795dd04d2cf8f9b1c58378808125ada93..dcd6d1d668294eee065aa386f210a3819d65b85d 100644 GIT binary patch delta 707 zcmX>!f%(t`<_+$QhKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XD^7$1_MOP9@lPt%^Z~iMalaEZTYFh2VWNCGuT;D0a*{1F^7kSz@*LPmCAw#P? LYv|_N$I=-AMGxAt delta 707 zcmX>!f%(t`<_+$Qh6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtGh7$1_MOP9@lPt%^Z~iMalaEZTYFh2VWNCGuT;D0a*{1F^7kSz@*LPmCAw#P? LYv|_N$I=-A`n}!0 diff --git a/master/.doctrees/cleanlab/segmentation/summary.doctree b/master/.doctrees/cleanlab/segmentation/summary.doctree index 8869813607fb04f10e325c34156e7a2128cb7bef..44fc39a87e07c56f014afdba8c37c6204123a5f2 100644 GIT binary patch delta 1026 zcmbO`n`Q27mJPm)hKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XGF7+;aDYx+fQM*qz(m>ikN(;Gc`D{CQnT74$_bMa69ASk@~D4QZH zWoC$P?&R4-MxcSb4|9k3=6if?WCjD+jLjbe=TqngiOt3$6_mIE7@iWF{l%ZMkQ<)$ zQi7Wg$?WE*L_fFgPcL%y+X;$po>mdeONsvKwmNTe_2-KUY;L>c!A^<(=v%Iar0d@f IOp9k20ZCyye*gdg delta 1026 zcmbO`n`Q27mJPm)h6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtI%7+;aDYx+fQM*qz(m>ikN(;Gc`D{CQnT74$_bMa69ASk@~D4QZH zWoC$P?&R4-MxcSb4|9k3=6if?WCjD+jLjbe=TqngiOt3$6_mIE7@iWF{l%ZMkQ<)$ zQi7Wg$?WE*L_fFgPcL%y+X;$po>mdeONsvKwmNTe_2-KUY;L>c!A^<(=v%Iar0d@f IOp9k20T+iqj{pDw diff --git a/master/.doctrees/cleanlab/token_classification/filter.doctree b/master/.doctrees/cleanlab/token_classification/filter.doctree index 462bbbb3ddbf751ec04c7803977f17f1d8f929f0..03ea37d363729b9850202b16516d31935b1e109c 100644 GIT binary patch delta 483 zcmX?gh4IuC#tq(#hKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XEv82^)@>n?ljW=`hCEM#fDMV79p$q9U_o6|Y-$g?zRa{}KEW-@JV h;1=4vRcJ9e;d_@oYBEUsSCP12vTV=X{5B(&5dfqzmuUb1 delta 483 zcmX?gh4IuC#tq(#h6QHH#fj;r@%lz7NofYjsmaFX7RHvTNd_sV$%$zu#s-E-X@-fG zMyV#LhQ>)rCPtHM82^)@>n?ljW=`hCEM#fDMV79p$q9U_o6|Y-$g?zRa{}KEW-@JV h;1=4vRcJ9e;d_@oYBEUsSCP12vTV=X{5B(&5dh`PnQ;IB diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree index 736561a58b3e9e7a4f79576f09a0b5d6a6b1c546..d46400c5b8e6dea75f28f645e52d46e1fa00dfae 100644 GIT binary patch delta 122 zcmca7cTa9ZI-{Xsc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= fQj%$MVsfIv<{6CZ8I4HRrayTfi`?ev%*VL_HLfM` delta 122 zcmca7cTa9ZI-_BMS#oh=x@o+=QA$#pL2_!cvAKn@WonW^ifM9Unu)Q2VN#l5qNP!) fNvff7Qj&?$<{6CZ8I4HRrayTfi`?ev%*VL_Xq_f= diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree index 574dafbf977df7c42d2354199e7b87cbda52096c..f11c1eec98bd477834a44823cb6237bc2a9ed53b 100644 GIT binary patch delta 699 zcmZp_#@v35d4oHnp<#JSYHFH=fqt5)p?Ol0nMG=nNph;8WolZIxv9BnvYADiiK(TD zxnWY0X>ww6qQT?}#-C*9y2u{6`8U&27P7QnBSV)Wf79kD&S|WqYu&8KKbwhkZIc!G z+cp;qEhJa#X;BRhvTV)YJX!7pIWeiopTGIHf@m)3wr+OpTS1PEj(s_s+a|O`kZ$AT Ni|nbJ7rabo1ORwq*MR^4 delta 699 zcmZp_#@v35d4oHnVS!n4abmh@yuMLNQkp?>YO=Apg|TI7l0k}Ta$=f^v4LSynqi`) zQL0I*p>a}@iP7W=#-C*9y2u{6`8U&27P7QnBSV)Wf79kD&S|WqYu&8KKbwhkZIc!G z+cp;qEhJa#X;BRhvTV)YJX!7pIWeiopTGIHf@m)3wr+OpTS1PEj(s_s+a|O`kZ$AT Ni|nbJ7rabo1OOY?+Km7J diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree index 52cb44abf1844152cae2f965b5fd82a75da6785f..04bc6171ccd0dab1a4ef51a29d8fe166ff66208d 100644 GIT binary patch delta 1014 zcmX@}mgUS_mJPm)hKA)Ssi|oe2Ks5HhUQ60W)`VQCdsLWmZ@n;=BDPR$z~R5CZ?7q z=7vd0rpbxPi3XeN7@5e?wvlNK6X~{1-pCX`-QSdvXYx5JwS%vfu4Z5LVWXV{<-9Z1I!4C&2NNSDR9MRUeVc)rCPtg<7@5e?wvlNK6X~{1-pCX`-QSdvXYx5JwS%vfu4Z5LVWXV{<-9Z1I!4C&2NNSDR9MRUeVcm<~MJha?YHyPk+slXWYt?XRRt-I<0#` zLXV0u(LG{ISLoWce8q&W<+>%5u9VQDYlYYfvE9m7>`^YZQn?DHyLOH3)}>pQn3-3* z%e{@Z*5=mQW+-_1B|Q{ntUNcwic$lZd9|lm+Ph!(gdu!ZJ1alFtA($5L&xVu82DxG z5}XCph)oaS;}iY(veBjVfvJO1lLz$f(sy7qX)`-B9J~ZC!};@(C3t6KG`}f4jB^3k z=oA<4=vkT9m5Sgq+WFHCT;XM4YOSxV2BZRg=4 z0GHo50u@=n=;DPTeAX=~BCJ5W`K6WDlqwGzSBjSMImO%>wXf%H$%Fc(&ISb?bTRy^ zUN(@@s*N9C`esdDTdE4@1_v}P-TdZGX3haz6)QO~9N)BAg*qrjDOjy80SC?TKHO>OtX6W@gdzJ_=2gJuM*(g5_2hjOi2S* zd`|zq#Zgfh=>NODI9DEwe(8)#3R4SJL9;V8KBkigrS|QO^u==p5&W@) zP-O3LesS00;NzF{U{FwVv>!h;JdEeUY+%gAG&4Ud+N4(bS`X;buMe+rM{}k4(OISV zQ9YwUTvW5rt#_lNOJZ$%#rA59n+;}O3oFAPEEepS)TL*Fk9FiJ{GnoI{&8po_cyeQ2otMZgiR%VWkIsLL5-8O-P+li|FRALyi^%}b1w@o zDhQtyX4m}Ne?efIu|^S!i?~{^Lb=&|jT?I6`pcujsnJkhqc5gez=1ZqT%t@Tc=>ZP z?4g2RpiCrKvZ^qW&nRi)LcnOvIK3oc2yzLo?JgVfHGWC4P`*|NH?ae@D0W9M>&LNX zuNR_jwe5=YmBW;vK~yuu4KXMQ2kiGkq{MU#;WK(hf(*hh^_`i~X0Z9=*igQDZ#xNn z%peJnbqN8$(s67cU!k3o@7BpJxRHc}xw=<4|4V|4BZGtg>`b3+d26_ve-&ZpOjr;T z3E-`rjEKZ>g=7^>ey)9SuebgyzF+pTlMp}y4<1)cd`?#@f39Q%a_T$d+7?r*zK$L} zy7WihS&9?luS^&B-~ASK^|x`SbvxwE#?m^@2)N4{GiL`ig3H^HU0iK0a)0VfuDv2u3s)CHy(845&H}ZFQ1_Ar zDnFf&op^y%>zlS?4i0HL~8NF>xALhWlTP!|a$+cbz=+eoNl z^@kAZ2%*w84I|WfNjjGP+&O{VKrWUwjUX2n63Q4RP`?xEqkR;)_L@-rKBN=sFrg}K z98IY0gxYXSpso|D7aU8jts<1C>_kEpNdA+Ky8lu~Aa9e4#;%je#XLf-*Uu!>uY}5) zJDX5*2vx2)Ak=I^sVfQ8Q$m?)%qQ256YAyQg@k%YsCOS06Y3nI(x>?S`AZHVpOcHX zl*KLKA)!IX@tsvBZ+b0P1 z1ykvMjklg8IR|w;|1yjq3UJ-POkk%C}n!>=Y(8B$f?6$5b6z~ig7Oq^_fte zBLwOYp&t9aBG+aSYTyNd`ioFio(ohCp_cyk2dVi7p+46CK&Y35x+6~ilaQAQ$y+}W z>L#HgstD9wLb>L1NqB_VOsL&aB0_B=RHr)v^@LEXJ_yuWLd_}=lbZVoWjh=|sJDdT zYpBu*d4P}`+p7q*fKb^@)r5Las1ZsHp>`AMz%YTzCDf%nf!al=X{QD1B%$^%$RX`M z5NcHB$bMLerGz>&phQYKCT|h)yOA?fF!d*)iYwm}Dw|N1w+4;C*X9xG-YJ2)MyL}* z7n5s{Q01?8ABk%&CzRsOiBXtZO{lxO>yrK+5Nh?AKhovt=;C@pHaNC616Msos5a&f zqcQa}p{kS>sQrYh7`~TWyFjRkGe(ZVHFpqd-SM!om^w%{E^i1eBG(V z#aW3fNkhZXNPgM&{`~DU3u%~;Hf+{XEw37?q}49*H5MB9l;uv&$(J6c=TA=z^wh z4$pMrrET^wgVH}M5zi>NEEIKM;H!-YKp4>+_zFK8wC7jOsLh*4mE`Y@IQ}>2i+`_F ziZ4F97FU&b*(>myNA>2qqFf#R%P0l^IX#%KGc_FXvhiz!eV~jevpuO(W3-7MJh2wP ze@Yx$YGVLpm#*ESQijgXN<4yEl;&HE`Hl~nQy-;Kpm2f1pMRZIi?21V5agQr$3P+hF$>~5uveCotH*pz&oHD;^`t`lEl&=0(5jvk5f zkYAjAh&xAAosfoIR0}ft#wz%Ipqv6vy%?$*@u4zv%PH`Li%U${`9a59Ut`x!B=Uc(1@ER&)=NEH-;M@HajJmvvIy3N*^DW#l5_8ycf6aFh z_Ku{+q8cj@RC8221nrYBL|o508ss;f`ImDW^5F|Z_^=({<2dGJKegnm%nRZB|CETD zec_)kHt^3El6{zx@1Cn81lkQM__Awts7qD0tw{)fb75)Gf24fiA}5dI9fuO`AKS6P zA`>6INQnJYgYdX;mfycPiu;71n=9}PF9`N>iNvLp%hH`mwV!Ws{E&^O$a z;=3$&@#bX#$g}Yp0rA0SrPbs2&yn)$mf861+)z+hxxIp)og2;f$c;h6S3KW&r4^4( zs0m(2;3gYav=L=?#pEMRB~*if~_&Q6MbG%!h6+!yn4E@+~$O!xrV*lDPf2 zDbQzILE)E?`TFzqw;D+cRK|;-8I1=1z;YqYKLBxt*Gjnmt_Y!>F#Da^)`v`!4`yjl z)nNYU_Fmqw5-M!m87xSEF82Sa4|ZszD7?DRyhSD6nH&`IcMmAM7JDBQNQ9{2vALxFNr9puOrkR|Y% z3geIGyZq9fkJ&E=(Jj%EB5HS#0xz)erHOyPWJNOLwHB7SM&2N>ZW565m-nQB&ZB`0 zXZRZXhw&BjMuEMm*K$68Zz{+;4bbqJm&X6FcNkxL-$?%So{?a0z-tA6VqYrXWRC*z zyqvFgU=&~d&^Z3skx_irgQLOc=1UcPgTo*AJqJbul?K(WdvG{^=+G#zI1mxX4h{*3 zjpyeBfPNeZK&SEK!^2757gC@xbIlB}rF1aE_*%wCUP}eh2Gl*SvMCr<3A_FR^y_&CHyQ|Tv?iHU z-gbRBpLb~_Xm3JQY&UG&Gk)=n(%d`pKcDGi! z=0NV-Zyp2fYHbK#GF7!T>sDtD!MwaZ&F|Y`cvosDXmrYG0G$_c0bAcZ?8k9yfc8iA z_x#$&+rAx-umu)O64^mvu|g^T%kQrlKxR%7b>G_L=?4j0+mZacmpN=rJMuYSN^NcS z`j!l>?@NEY$Bt5N>(xJxiV(`;&jT1yw&r}9gb-kai2F^9%n~5u)-ggMvC6^We%uR$ z2l^9oMZpR7a09UJCKcoQ7X@ceajqHGS6~FEkRUu$B)6|9c=MyUJ=jND zm*ybBEz9kevIB;|HS(wWGLVIITF2>xPsEf6C+@VAjXZb%Xm8muD7mbFneQ?sA{}Q0jidEmWZNz#Hx@rg!|sHN^EKoG1^| zN93X_*iRQKMI6@HVvXoDrjv9cX;C!g4+_Ey-7wlAGAq57?ieA;5f?%G0wzG1GI$_T z)ClvhjuqLlJdu?|ZE-rOswfc4F}IdzD^43V5Ix7`-!&FpVYZF2xhMjjXesLE6$Sj! zLPU+8+*!1OSttU0s=KHOHnpvn=mbt5^$``vENfCk)v@WD3=%!VX|>^^0$lFM5Y>=> zyBZ;y#mk~wNPG6!`1HpmLBJHzrf)|P78DKliUYyU?Tx#GcPA2bVE8mqAOurIqgfVC zHTH_*z}Z(K2hdECXyA@%qTfYaAzYRvdaXt&SS&iup|r~qkq(!qE)#9Rbnz7;KQT(9 zR}1Ae*NR>gM|7F0UR)Bp{i%-PzL>7mO}q!Y_1i>orWB=PQ^dV+yQc%ifwjV|XJVYA)5P;IerH~wU(FHU z!PhU&6R%~l2626CSAgtl0MB2EI)Y(~#5M+HBB)+4X0dn`(gV1~Zx0I} z5ZQ&OVr8CqF)IrX4v2SSRV+Qs!U7)9Qb3jC;sAK`i1<6~*P>(M-8kL%i#QkSXV59} z&)?1*YS)7C!_Fjpvgye4b1sde2Q5EqR-+@h=Dmc;%Mdv{t&ohhqwxr>w*JE7QG6*03EI*Z!jferF1%X!p|a!}E-zk1vK!CpcWX#`t8jT^$xAs(tN$n=s}leAk}H_L z+g(x|WU{iNtJ4w@LoO zsR&A9a9Vb!q!CUtc1vV9ow!$W1E<~cB!h5&*A7WsnEvyKBua+T5~m~$aDRjHB_^EC zxF|8#^hq1xj;YNn{vr?|TWE ze1?9MOvLrL&yu}LlHUn2Fb+N_k~W?KTJ`|;%g8tHkg)jB$92JZf7h_oy&|7enu zEzn_$^gh16)+SwnuQv&m&SG4PwSYZ~NylMbK8=+A5P;Ikm8HF`C|%Y{s=}RaZX-Rx zvSB{ZOWG0io!d|kG8#rE!ik-w9O6~jT`cS_ea*lyM3~1hPgDC!i5n;MlTO15cs)QG zg6*OlB5lIRYb&s7V|SOpYu`~)KOe7?#z=#Uf~kgOCP~SXe$iBc>mkzwu4S{NHq5mL zNF!N-2Q6|M1I>=1D$sb{(9y8dB54T(Cun%lvZ7(?VaK)7xmefl)(c#p+bC^}b?w?F z9f-M}vs1bp`%UEo(n4Hb@~BkAC`Hr7c|gPk!hNTt6O3L7+T546V4P#Tc-0xNtP^{r5%?o zyM}%4#yr^!+<%{+Wc{$uU0)(=#jyN=NX*1hY(6juI6lP7U|P5z8B6b$^!tD{^lPM_ z9~v^?{W^Xbn6n-Y{r2JVupj(Z|3fITB_q&I$ z_v$8GFP?DJPH=plLv}G&X3VrsA(wI zNQ7+Z#_|Z7?bBTT3(eO4QQpm;LFc$5fI4!3uJOGrV&$Pznmm!k^9c{FN6H6N9&V+}N3!zpI79AX32DN^ zf^qVBl!rPK~UBALXZmfcqoU|7o#4_&j~7nny2z6be8L1 zDA&@u{A+ofFhIb2@8!p6vppZ>O=tz%XZdcG5OI(F6!4u)?doq#+8g_8JdYIAlVF^wEl3VXB68rzslBh0>*S6t9g8 zzBF@>qK+V-eWSvn6x<a_{2DM)F;M<k58V}tz5aA3GiX$3j$s-rz_RLwGQqFL!({H?MoBc;H3HChKBxRkln z)z`a~UK`ISrt~UjdkLjiInyJR>#3_ZDyw{>7G!T)Q#qV!@7LPOF4T=0H&%Y6uF8V^#vuk=Qm(XmxKFWoR3aJZ|2Pj+8J}(VZt|`Ojjuk;( zrd$T%4l3oqf1^SVKElK>Y$cD z|F$gms=k8x1^MUGh)oS^N1q$7#HuIX=2KAYz9sIq1dZ3fPSIMA@zgNkvy7{uf z3I5^Kc=3t;3YI++RuRk_rPAjz0+ZZW&SC2_}j70{{VHlDI5GhQWebE=C7ej%-!REn#p1QWx@rAsR^~P*-`(N z)Py@O`fs6q4!`a-A@uvzKa~bb<~{$4G<)fZ{~Q_;!wdbF(rnZpJ|m0Y`HXz+y?-pl zb$s^U#T-6aVF4v!k}RN+NwDb|M?f1^n?eLOjgF>Kr2}eFvBp#kxWzO#RteZnP20M9 zz-el(*m?nn{uk!bGzmZv7uOk0F26Jl@J8Iu&H)M113(I3cjCRke{a_1dMnMCXlb;i`cQfgmVtM5M~Av|7~sojfN8aRUuR- zO$V#gOwL{<;CrC{t(HHqwHpx(h85JR593Cun#%-_Up-ExWZCi}rzEg)SZ1QC92Gci zn##MYs6IpGU2(c*sk|%B@3U3jRe*Dz%6mWZ!$Q?~I%v(vQ7xl`R;@KE9hJSu2Gt}M z*?l)GVc%TUz+&7bu$gy_23>wtvAoZNZqk~8s$8KO#N1L{VOSLgK32uSA-7dcSR5KR z29@rrSk5)0wmpR-&FS}4=m_B@Q2LQ-{kKa15zqjZnGUsU3dJu}gIU&nr177Hs&y*F zI5`Eq=d)_On!5yRs?_VGD1D(*qn9Ku!DB({+DeoLIMnDl-6iAzhq0C7lc;{an?tEY3%@C$*D8E}w? z*?}{*&>sX(6?Fl9tBufs+smDDP?)Zk!z*pn6Ik>&Ix4Ux%x|ZD&JsenF+hzSph6*od(Nu0ApV-h2sVoAbb!vu zYBX44t-%k|)TcNhdwGU>In7R;ss4#(EAqbV{n=_uh)`Giv-*a=ko}aa?j{qm{r9R5 zvOInJ2cwycMijgP9TX3O&+^rC1^GbsCH2%I0NUZM%j(;VVL}|$4$E9qccfmh9p-lHu-vzyXwpYJq`_A)E}8 zG=XK9^-x_OIEV6ZKRA$as!N6zL*Nu<9_p9_y)nDb8aR#3JCorPTi_XHTeNWoZlHQF zx&s%n^3ci?c#)Zh6U748P#(rb1hO2_K%+%6JWwJ~O;;}kk%1}SE(%02)#*%ku1p}~ zaFGl@mJPhg>=c751h$~cxKW9n2g!GVYgulVAQd!)F>!%s8LinApvE%Lz-|i$&JGny zz@s$-+b}$$!AsMIprA>hKlsu-Pzrx;8hDzKB>@H^m<^1p(uQFF`v(Cat7D)P%;;c? zg1y=Y<}h=Eu^__w#B$4;sHL!1U;wyfb!~;e^a*U`FD#GCrUf!RAODJ|E*7SOwT1=G z3ltWh&n5?+r0dq>Qv*c?q4eg`z`6-Swya!pm9BiPD$OyIQ2M5rCOk~YMmEwMVmQ79 zT`e`yfkZ4Yx0^-};=T(s!&%)mOmFZYur{!uhXx(CB8zl$A5An}Vz=$D*+V5PK2Xz9 zDpbfGqRFNes-|mh(`@7f&1#x`kg3^B!B)@I^kTVVK(roL{e>JniW^-99-X6E!0b>2 zCilGD1ag){mVn`NHBFcu9brj9@+-Xp@bzQL!q^MvwKoTDiN-8(h&m^s8Su-b0TwGdWDoF6p`5OodxLuc6M z!m1ZE60s1EtuAYtGtL4H0fvPL_JA;<<^HPKME&%@EzLfq;HO!SHB0=2Y|WRNt_*=j zlnGI1djzI|>616w?4LE6MPc6rQc^0)U>#0tp;6P{PwNe~Y6@)_Elmy7ZlG~jHdwox z0gvsHxv=KDBy^c?c^zXH4g6l!QhT4*>tb>B$fd=p;!|4y67er`;S3H87AQ{5D zXw{(9ep6Erovtnk&NkIX3KEj5WW2A|Ogn?{fbO%k)Uuo$M2^wO#{#-h7pIlM!dBYF zRNkC6K6w+{`Q&}oQG1nf<9e>u|lXCAcuoYD4 zaZm^PZ`Lw?BF+P1sumjItSwq}vb%B1_UMUIuC|9r$d=ft-KG?>FAizZ51Nn)+-!R6SceTwa!l{SaQ|J{n47TPE?Rc?}t^Ps#npUXwS^J(NrP~|nVky8Zk#3Y$ zsOk#Rt)Q*07<6}ps_?i;_q~)>aOiGXxJBgic;Nf*ba~&dP5e0<4{TpWcTU7Df>G6V z3#BMMRa=K%xLgFY>*~t+bNTqGPCcM+rrXHa0pFyZ6k40>b||@gc&URfRf5t6U3KrB z+%kdtDPwiJ7{Ni4?+eXv>p0zp5OD2>Hd;7ffv${L$ZlVxn@zKAm+GQKLg~Eax{@?| zX{D|@&4#Vfb)i}CvkuMG1nkH4I!H^Mn{-Pk*!iux%Cyv$tLsOzKke`}>#$2Vk0SW( z(Vd|c9_-Vdq6mi$>bld?ibr(qX?Ddi-6Wc=c0$*iW_O(O;SN3HW2eSBT@D3%b1uP{ z{+55%)f3>hf6@cm-3p05oc3``ruW(_N})eVOO*loUufOCfqJiAf7Iz4(hA#x^yq*p zVNGh*@1s3mwCEe-p3$3dK_F+0UJ0H#^nS3uP49J&@echZTDgx)AM}sPa8anPj7PlS;b^ujZ`{P;C`lR8wDv3j3zE-s`6;>*>9I`m%w(Ev?Z02ff$5ZH@I_=lrXw z-s_y#TKbe2|D)a;6?tua%|^G^d!wR$C%xAVKX&!?Rwu#N+oqnrX8n8XH&7m8`s$}s zlbucWnO8o*r<)&B_2?-Ik@nOueVc!%9yrpGof?ktv9@=VzB2`m8m;f(tqhS-1E`7i zPVt&3ce>9)D`xsEbdUF0$eHcqB4w_xkKg9|a5Mwtg_JaObZ659*N*@B46_ zk9>7U{jMM4CxH3A(tF3V(Qou00)*13T#$F{doK>k*9)cB3_)H8oo@=-PrFF81f8JS zI<_EmfQ~qa!4Z_C60&nj1^r1&mqZ1jS3F3Cc`-q|X*Quu5IXrmO1~=?w3%i<#Rj42 zo0LXZ3_2GqWVP`@*OWr`&mV)lqrjcEL7{;{X;5NNck02DlY$}?Lg}HwLEd;BJ0xg3 z?V|p$pq5m>8#8>erHl#cM-g6(54uSaZq5jD)2wD*ke#}3^(8?)Xx-f9LCZ8kZ;9J| zU?pMDTWXdvdx8$qj-~s2VjkNcgbt4oF=reIYC>@fj|ZhwTVz}g8ct;kc;IVx=3&r% z+U)S-pjotwp3j4lC_DFG1_8>>G*Pfusf{JUU1^1be!=(rg^r;<_!d?5%8=kK6t}lA zcmyqdYYzU6mOi!yPoup}bOm#?!aR5IU0PvUSg_R~;O?pte3Ke$)%U^Z>4;#>2Em22 zZcL-#n>1^S4?aQbzW*_}H8o4;PQhONUg;W)4nh)y_KCqU6l`JN;GZc%n|{GJsBCox z1dpVeyfDls<}VY1KhS39CkKa9{rYDId(Ay%UhqC@<2K8Jf2FwJuL#~i>&mtSzoc3D z{$PI!<~a~tftL0?9o&>kapp>J6&eAS`@usgXDc2B`>O>h4!rfLdffZqzbM$uKZCt< zbgNIn+lZ<)&@2k^`pz?PNL7maoi+q*j)_X7`jB5J!mZ#CInBN^gm|a+m8Ot)wC)B= zh<657+CwhVx-Xm|XfaE`?u3S*lk6nB#S?|suO}v$dhKC_z-l&i)24E4p~D@xV3diAFAr;E+IQ;-S@pix>1C_Ng>N=1y@=K zy1hXVo(~DBNIR}DJS2na;g?AvF_Z@dAJUU%M=uQV2E&(}kW~~6t_|@z?eTRX_o<)l z-x~6mnq}v~5cEDA>8;YSkS|&xTmM?f_Y`-@-4H!pwH$dA@{ICu=xs=x(8cy3Lq$4b zcF-HPQ+eD5!yC#%xXW;r3al+*=vbfc`q+*(@Dt3S!>%zUK-DZ=Z5W(t$Y!}WfHw}g z!wmZ*e9T8JJUhxzMoeiVV=;o`(ncx;cBg=hEqaY=So9&p={1- zLwg}7`q?l^$SqiJ=q==eH+k_cZ}xJqXqzENKxw?g%fb9zUJguqy)CZq^KvjQ&u~`2 z>wd_PFSM<5%#bLQAb}#*`*8X8=C^V71H^fMZslNYY*eCQD^2LBw(LaGD2}V1x zOO1Zpry9!%)$>KhOG2ewi4m=yg-YbyJ zkn8C&q7^jN6KdT|fVYJkso$L~X{3H2j z+Tq3P#?P#R0fI2`SK~DixZS&%7W6MLu43c{BCH^mXE9z;4B0cyL10Q-`y6P#Z{#_` z5#0B{xRqwdJTjv7H0Bf5`Q6C!&3%aaLMP3MsDs}cm$QkQ2~q+pD`3GpV=SY*nV>MV zyaL|%Xk1SfKkt)~<#Pc@o=iUfPdWVj*_h49%tY8oWI`wHiB_VdCYIwhK0ap3Of4Dt z$OLf%n<(Hqh3NuiCM&?Sl+{Ge;++)my~>0>T0jJ=s57l##mYbBQNU$-(`<&J3?S7E zhPOfIMZcno07Fm*$nzbV3Xjm+NL!uLl$X#v#trfz)$47 z*}#Net|ZyuCZ;!3WOFmqd#V$6YttZ_UDM7~lV<mD4MExRp zm}xUB4=;wB{$lY9!~^&`d9+VQzi&^xB6qY2-47!E@yi$!df-j6Gsc-R1+M|6rkGB! zGJ9mIsWG#{?oKz|p$fY+)3p2Bd4NdfbcXI>|2d}NLim7Bb4@Hyq=>~Q%r`L|DJ6|> zFEs6<(yz@iZT)uX0f8yC)WmeOmNbrBVLDEE@LO%#`RzQwm#a+*mV+Q@7|8&Ip0^Ho zVxwsRwZ-7AOnUHP0fZGoRsx3Yro^JK+FzH34^5lQyhJ>P({?SV_7PWz{Jg$qEkrKI^oAA= zYKRc72QqYzR`Tc_8DI{E-;Xq}5Rt(V)ER|73S8LFWQI{0=EihJnLftM^5);y4pxmd zJ6Og>9|AxKYS3hoSqnPtGM5LxOfa7=ipT^HB0Rc?2Fo(dEI+gGmBB1vMjy+`G8;g_ z4zV9BHr>qh;}ib`ga~j44S+4P%zas6>K};y@bWx!Te&b1)>&#s2OmgC{l3C{ff>AT zhuH#C)|-3KZPk-4X7qFrgXdxt$VGC2H<8NItneC|cQxhl(%c{J!HgA9=+5bSwomV+B7 zBK4s9{MyRgYvx%RVHCS|%{z)+d~8M^^(4MH>Zy4KgR*_yLDU`7>-s(f%&;1~cwuIF zLD-8D0+K#>U8&tm^G)hX_5Lt3e7^wCK}4AZ)eeupH9uzwHy;c{%$=tSZ}OL!W$*06 z!h+i2rBCK!EJKqQ#sI$S7p4TCro_&I@lwk*PVnq1ewIosV}lPCmQq@l(&6*A%CeBI zsix^HPL{<0ikMa)=IUb$Y;3SpVX=V^0U!h^ct0f;ptGkIrJoRP_nel+l*id&7V0)| zbcE%Df;B2O=}dYUS8(g5!86bh5q^`{zY+l>qy{TK_d&~AB2&#jqgW1fEh-4%zmVoL;7WCQ$ z8R%S%Er*K25|+6H%xhx#k-B$83(F#w{l-@+%HroQWWy#~5@{&pWLeM&9#UcYEK5_m*(n5; z<8)hbVv*%2si1>Pb1X|}-4;tN-kWZg<(6%TRy zK5sz>=ZHdgUbJ|hYK^#TS;8`Mqu&1Zm=Rw1)q;MXeh+?dS`Uo4ZCS$L+vr(uaZs3A zs0TC7hn0fc3M?fg+#Y!Qo@IuFyC^)9z3s6V12-RA7@s8<2L*!)<-&Jr+H8n*XZNs4 z!FNwA?HSm;^T`qqL!MeHvJ4EkF*yC)!tz}t)V90uT_jDRB~SvItVWCYm^YTSY(n&f zX@6MWvbZo}q%Tyyw~Q1CJk0%QVOhKpn8SZso-*(NQRBBl_j4XunR^csS=%$j99-gO zoj?sxF~B;O!2k#$6?RrxMJ!%~8-utNKPaF^V@;-3$kJKIF){*T)*!5ddg~(wQvl2j zxgFd(#F{~wd1SP*d@aBSWj9-|iv{me*oy{L-C@mRurU^^Bb**){ZkCq{nR`dE{d?C zoiho#lBKK+UsE8603o8U&_!AYGOD_xfKVmc>R?8&#aNY~V>4@6P`tGD8Jp8|1Q-Rb zFJnE$tX+8}tDWVn3pNed)#!y5XeL@E@I)0W)BWIoz$Aj}RjqpPxQ5jM%T~Ak!7@1_ zz~4v&p%W` z1Zv2W&?6NTQ{@Z zGr>9mO*pG6JH_e;uJu!&ZdUyPlz+13u$ zpJDCqCyYsFv#lxAb(+kxGCV#&fL&nxd}{{=nNZsSpkTL613Ug?b+Xz7ik4bC3QI89 zf29@OIl_?u+pV_#PP47nTG5+Iq%?G$bt%nWT5nxUv*R~f(Q8knLhH@ebu=5a)mQh} zHecPeTr0ZQPwK|)upV#=S=~)5dPjnkKK#|Xkabjr@;g@atb^dRyKfCJ2-zWjS>+5n zR-|9XDZM$c%WMHKN@N>I*IrwsHl8)?Fcv^q!cl?OG8?+Fjzt8^ls1+R;E`eSpP+aB zZFO1aX)LP>JnLkOhjP6wp5;L$;V8Pv3l)55u`zvd%!dFL#C9{mPIep94L=0a7-VU! zQV?I>CI{_@*pgw32wQ)J5boE@*!t6OZy#$*Vl$}F0Yn`%We9q__WjDD8WeA2(+Yz? z^NO~uQo-R3)oh(<7)+{Zn@+REzqchZgu#F5k>$?*SH256A%EV~!WIJa$J>f?aCE$_ zC)MhQCbro{(JJs_+hLVvw!1~sFUEq1PxMYX3Ae_rZFBxd%<(_k5{hOD#GDSIFNQJw z?z+zw5W!qvIkn+K0CBBQ^qZI4%E6(1Y>SJgaf}7mCfS@UER`OIrcxBKHDp4tZW(1m z2epaiQbyaRFjx*D_Jp&>*!r$44r&i-43%%!9K(8)HIoBW86uYh)VbP8Ko z{0pYTG}|z$+($EPEH~l)1z3KjEtw@EFbqWcVv2Py4?3`4XR8n2&9!ARdR`mQZ#wc_ z6bDxsokc(Z$oreZ>nm;7Se{oQd0K#+4L0=UvCX!6G!7O(UmUdA#To|`w%=~sa7OtA z1b`4!VDw%a%M?Py%Q$2!PA86n{l4kA>p|AwXaNzTj%CH^WhWZeJ?e{wbHA`gLkn1V z+_s1@23mlOb2bC{gd|ALMh?*NtZgByr69t3$`}YOKwBXHsgq3W-;Ma(=Pm}e|J zp!?b6!GHrk`Q4T!0cAoX>Vc%HvE{(K7dGan9&tVF0pK_1=)vHHI#vs^YMJ~&x0kl= z3?qD(F@?oJ$`{*ruO+fUi|i@A*;Q(|X% zsmaSj?3`0}VWVJ-epY+FP9p<%2|X&tME8g-U7>5&@)Z-hmg|;Kx>7=qt`%Y{#C9uRu}8Vs zO64k)?%FlBTbFKKVti_$4naP6p3%hq5F4OZD?7`9ATO^3k~+l6)^?^BS$tR!`D=5m z*{$vE$2gP@?`S`Y)0&;_BFt=^1p7wT5cLLx0Timx>tsLou$N|;vwY0r;4*+bbzM2+ zd)v2RudUbDo?CSGV(Jx>!|h4-$^QfP$RX1=DfXS1y$u8Hu;}a&NXsDmouWh*b(@r# z9y#2aW*6h32@bO#{U5Pcc7&bjEv0`Mmxx+1-Tnk?&ojn;;D5y4g0c43Mb=(P5H!;q z4;N3cSH~f8d!qd+ngYR&Ogqa#qyHwkd%C?((dM5Vqge(D=h{v9u)sYSF~>d>t8y7A zVi;pwh;VNgojsVe*uEXRADM}MTPx-YY@#)H0Ul^uODaU;C4S{RBJt+m%-Sz>-w z@Lw%ns6eSia~Zg6qn+ss&i}$X-Pha@Hris(Qx(ZSky4c)IwC}1+IT+xA-Lb^>_QJB1>czKBU~`xm}HypDC~Gr!fCj-`Ha@ z-vd9|J$S(|_lrHgXr3B3+^+cg*3l8EBe-be2gUmt7Lz;BFMJ8!57kP?OFwR7uFg@4 zW#jNw-3Z<096*B+@ab zXgcnkVYk8fvX0q4A9+{au^5j5pt6JIdmevVcb({99tjHL9Ou2NzEZPrw)ZRavGCD_M{YlkQ1IhcO2_Q z1!QNq{*q%9PD@>PG{Wi7TaHOM72S2*!0D}rj#W6F@DvX%M_~9%$7Fw$Zu!g69@BxG zb1F_3NS&npBZYGoriZJY?Qy-UCTD9*$Jm`?aJn^=)XOa{RO3rJ^H|Q#d{utu!&)VX ztK#$nm7A6if~IoL3@Nez8B{k^az11kRR4(%5qdD@y;YnO-2giR6KXnZV7siUZP2VY<@~&PJ$jxUQK{Z+8o4EqwjUkIpb$9@f!$f#s)ezOs||;|nV~imrhB5=obW znD3LE%A$>G6c=qD@Gk?LOmU`SIpYR8V{tlokP}TOM_`pS=U6Of-Z1BQmf8Owa-etK zCl>~T{V7fv82&}6hvv~vJwg|@2H_b_J;)k?hNip-P79OAIYaQZ2@{%eYnM|E-{(5FBP6h4r;}xEfq@_bLN5<&fc^G6dt$CGA8=-4I_ao$ zev!BXh&wGV@iF7QKKaEqgYp`*Z=h7Ew89I!+g0o-uc*@o$MEaC=Cm$)2YLoJsE1U+(= zhklQoJN=RI4!m~8U?aYI=WK}6YJWMmvr3HnV0r$Fg#(=wt^jye>dM51Yp--|VKbZn z1Q3w$=;4CDE0NJT{{{uJa>^>8IM8(i+wiv5#qvWIJ~6*?*In=W5eNF-5Z6$aQU5EX zl1d#tEsLih7lSN{>xE9=ApIrwS6y(F&J}{K& zksAhpG2&2(cj%IZvho3uuJ5so9X=u8DrIOsRmdb&D8mO7!B?#EfuZeKHk1TGbJFDx zGHl`Kb1lYj^cK*IQmOEdkkBB3IcPS8?&64N!~5pYYAnxgNH?1<;%;_DNWlwtnP8!t z2_9cJKNs_LGdMi7ILk8&(oNQNS0D&XiJSqY<$T~J%KO0os1RD689c5=s19msg+8Ua zQPmAaZ))JEhrfLvioVoIvTN)6dK=WxS6cOlP%%Tt5J@FtR(ij#WzcDy3h2`~PaTny z;ET?oze+&+r)UbllMq_SvZN$RMzAKhtxsrcA(EgqITU>ymchCK&>xvp{C6xf>^H|J^}V^Fd#RqcEDS{- zbRq07UJ^Q(Df++mWA-$J)-|DPG%Vw?561a0DhSTm6`DeM4A~#r1^paoRB;`gd@S^V zpTKU$*--R}Ny2%9bD`+-8YHW|5bCBC>Rb;+zqvw6kK77fPqSn1hT4U-7ToeE^eUBo z!PC&D%CxosYAC521^hyFjC!6m-}hW(86GJxSZeZa3|# zg2+9S8TEw>{rc3tqhTh33S9~xY)gOlE`M<0eXn|;wZXlT!JNW9I8cbb?`CwXnI`=s zt`Z=ny1M~zTih&nLr^dgfbQV)k8a`6BiKLHgMOXf>V7B&ae=+j>t0Uxbq4AF0d0qE zL*1_#Lx*tKSJ)aAFX7gxz@yu}gJGizZZT!Cb7eQ<)`(yafS0~=w_zIOsQ$*nfNE|< zQ4l^CRmI49Ws6-xJJ;w7Afs`Z0+iYwI~x?%a{Iv$;g1K!5iWJAClH z`*2Z&CGKX26C1dC${b7E5yIW`S< zAEhdpILgg(%GL)}EW_QDb^J!Wr2Ko|b8ey=?R#*H!MBs$Xx~G!2d28in3mvz#0lAj zZdtZG%pq)gs?YKD(H;2uh@R&*F>DX;!V_yX0d!j8)`GxVq1WK6<-QIKtI&`r2=R2a z8*NaCJlod#x=ml_EB$_h`!maXR+s~rv%?)N_!lhL?M5%ala99R^`Q^j@4jAy$W8#a zd$|1}x)ncMDonH+PrBQ(u1<);Fd+DPZ$IGz(jS^HHDF|TWG0+()?G;^NOA3wdrXm7 zkL%|S03FYjw7{A--OI%S|9{?gPoo-}ch}9fnep15bntvks2Zl-cb}pT+~ScN?P`b} z%Kh%PFz#xIb0G%~2GO&MO@`}V`F!jA8=n^~eCzJV>b!WRSmWPYCx^(>o^k6$)=pmm z&9IT52kjb(9u6u!X#YgAS%Dt3e*7BfD6v8=ldc#cuHT748%C1P2h)tB__kb1P|I$5$LF%9`C60wwI63 zy@{TzqI8J-QP@>Mw5!UZU7a21VS0k-8$pwYc>cbt0>eCLvlh3hw+@~f;hD)AKfaxi z1G>h0WE^)`IJS{I!Na^gbFM(!vq=+?mKoL)n_qfTfhr$OM{TEgSPmK@fEizRW)SVn zo{NFTzn>QDo8h^^#t{Lu!?c;6-&p2z!qT&<=s7KZR0Ys}ECR%Q3Q@wI^E_d4aOzT0 zFfcCg=z(T~#|+Ob@t_}pA{=jD?qS)Z;r^g)rKdN`dd$nL8n#&N`6Q?gc3tN|KMjp} zg1t9;5?Ka6A1*++T5R2pKm$E2Uot09!sh$GY~MER^7z5VyFBO@lZX;Edp#@*TW=?J zxOSh1Y3GI!VAKH*`qg5>i2ksLWgkgkrXTSzeW&oN6fJ)7F;eNI=RlDH;p!<5)86bW zH&4!b&Qi6UJnv!IE`G)8ebK|TEkg*Kg)P~v>zjp*1{up41cemxpa*S%MNQ@7~D_ zR8isGU}{j<1ZJ$1^mi(tX@tJNOrE=BghpyXqbI{!!FtZHNg_~Ql^hHny29vGq;rQw zGfv`Jz}waTVePvEnoOSezjP2mide9K1wsfRfj}UkLlSzmoV{T0*t?>hz1Ow(diJ_@ zv3EsJJ@xd&cAlrF=;>Kcf3y2;!rlG;a`^m}C1juXnc3Odoh|cLq&58gezliI4-ci^ z5X{_)D`MN@`XW;4r}2Xg7|zy4Yi6s8@PqeL@j+^(-VYVCls$&UE2Oc(4^G)c`yG}# z1#K1Q2SE#eLTrMc4`$N!59@fSaARSM7b$+M$fFBW{Twj|qyexy@jVs{r)=4_&Ynho zjgiAMhY6#1oB2UR!i8+m$`2wEUaZs349-CO zk#K&T2o1LwNDtdOwImc+qu>AH*S0^q}vz`=z2y7dCTz$jJ7Z2DIN^zj{dRakt_? zTOaY8i$-8!1cl9+ACRuCABD+z4J(qcpnnP}xZ;O-NfF<~*|ru=xfiT2wWU+9`t`K8 z+D$*-^s8#;vK#Jfc5My^j9bnL$eQgQ6i%{rDu@bOj^v8av(?z2?_zxrS(l=M)^P3;$2ps@SBuA3`Mb?bzPyN=~kR!iC!V=RT ze%BGBzZtML{qu$2WsH4r1WTAg$zOgy*^#t{S})S+J^0ueUjE*6+h@P#))LQ}U;Qv? z^~_Ik-JR<{3Nur&0Fd;C{_b?6wZFRxt1pn&mBEbBUqzp0CuYr8p|G3uo2{gsTao^l z6Vhg^+O(bC9~E_!A$*PYUn$yZN1Q+A%{OIOQ3?L2T_T1+!OUT5%Z+=3$se<%UPdG$ z#s3W2YJf;>`cs;Jkzlhv(;qX-{0EzC|K*r50T4Dyp~vY){;P$_3~PopnVr<5h5z43 zwd|ySw(>tAG;*e`KjwY0AkR+Px}ENds{9*l;>!sNm%hUYUS-$;az5fWzu92iIb^zznPJdV}=8yPew*_}M^@rIc|903u z|K>;+Fo_&?lh=_>$@mue!+I`9Y~s3ZZ+ax%ft`PSYWH1_eWiq=CB1#{rPs3 zGdXhA(T!Z}QYnlkU-ef?69hW(Cw~JbQ2ZCRxW-cdp1(VRQC-^VBEEP0m82+98AR9p z>|bIhsc8Ov|3+w~7Z$v9C2g)Ks*}QWWml^E&A*;4pXbot&;18Vo``;U>5qyaxkUU4 zrK9UsBBh^mKb!cSoyrQYf<|Q58T|^^jph8B(yP zv?}?al((f%d&<@z24S*diY%C@T>@f>qPGllEY1v&Lza(}i>PL4AK60K#Y?Q%|3a-zO9sj=Vr;MsX2vj;v<*WCj+7O+kd5~oz3AsDvai;}H`@{R*k{NvTQ2{4 zfGh1iQ>H6VE9AiaZxvkTI{^2pmemTVPPW+?ThKpd%S<*zo#f;}jYM|amNTa6#WHFq z72mIr!E54}9Q5)k8KnQ`#nEe+>aFI@zC+pfvLb*&cyVd8?(`vu&1YOLthRb=W0) zDXMj#vWb{GuK!jLlME9zm!U8tqXkKdd~4e3h-`_u^^VCfY5V>KTboWeE<=T0|ADD~ zO14<=I(0^dSt$7rV8S^WDhT@z%*_k3{ji`p%f*ANzAVE`_x}S}|B4K?9rPa<|7)_{ zLSN5s{4aQQzbQk-ivQtd^|Ne*;5FlJIlTjzZEb3IPj=jfcSv&Uf$WF!yTzPGvNHlu z{+n!L`2klvm7#8WnKjpky1kUa2{vmYBTiMm}c+(-Y>3bRM!g2>6`$>jb zfiO!pgkJnCLv6*DAx!%!J1y9Bv<|>rA1=dsX%k?JnR%BXT(A#-t#YoLsZQl&Q^h#| z6@xQ26lNa*!iu}N1(c^-45826147C(QE0)VaSC_Nu3#3*xjb%l1L|USbj(eYOME-a z-N=m1iV?IRIA9WH)2|GxO+ql zE;XQXc`UIE)`90UpfxiBDvF7XE;j()pv$K)UJU|%5XIt#0dSU+f4j1A0KDau7dtcw z*eKaEtmq@7X?hf?8W|vI6obF;Yt96 z{BG)ehA0K#$p?XXt?5RUWW=eGe6j`QOF zj{y*l^WxL50T7P!;#C{D8g0&C4|$|?zq&WAUqSwRd0-l|Yji!R+Fkw%3quR5%74WU zHkQ!cht{nlceAo8A)~g&r;wp_(ft4xi@C?xZguQnmBKP-Sy!U<*mpKwcM2yDCPG0 z2lx#=z}zaOH)$Ly|H+0F4oq+-v0C|KgeC!tQs+qd6--x!$_v;^fFp&2qYn1Oxo(0N zJ!h0x5S$*T$T5o*W>{C+Ax+*IGqbiJKuTA+JKdZihqXhdCThx&@4&1N{0kAYGynjQ z7nv^zENvn`WG%H?_vUiU#V-J()U%}=wG6;3A89Q=C^$^XmmkJjPH1~MD&*%J9(9!8 z791{jk)ObF7}`ya3GXRzm?8{yL~r?V?BnHq<)|p0tKe3D`BI^R34`Q_yri`EVEIB@ z$(U-6kYg@faw3OE%6}8epkw5ibHE&?*;qL$Fo((uNTEwWU1`_({UrHmAyn~nY!Bgp zfppAF`2(~M0ZM_i=^QyE8{uA7Pvn@xz-I9R>5KVtR0LXvuw;?^l3+7zDYn5E>{%`k zLyl)cuK}y%kjaB<#cypnwQ_5n95q*zv{EFi_}|UxNn7QxIn6!(*6niGoaV(LJLMY8 z42D0ZP2B*0X%pG~kOf?O!~$-0RPK(Mt#fdn&T@Da)MiBuB2TJ&gr2@&dBXEc;N>Oy zM~mgKoy;Yfc-8XUhS$Wm9<1;hhV zFQ%WL%OMoz)GxiT6qlFEAu8tIwtFpysF)Xh-pa3uYPKKczhdUAT%ermGB+uiO#`by zvo#E|2|O+ynPC^0k8H3$WswzyW33A@K^1T;gYl-Xy#gVg;tJ?oJrLrl;*kj+G@^Fk z&*Iy9bpv}NCu!#Auuy7=Ja8o@lq#dkqbT>NK!~C^-C1#g5JmB#ZAu_SQM~v$I}oBM zUObf>2vHO-PHhkfQ4}v8Z59Yo6fY*U4umL*7r(X(geZy^gS!Mm6vc~Gdjw{n?Z9`i z+@7d>RNfTs*AZc4Z~F(D`_iF-7JA!qUusCF!RvkcF&kEdRBXw=xd4Y9=((@Kt+N$Pai!vTA35-Gyobl5GYEYZy zfpDLetDt0MAS{vaV$tfr`A9E3nnEa5!^)~i3hjDs4P1_xw(;}5C3y-jI&@oLC5(GJ z+Dp-s$ae-}Hllgm+WP}VYL?{}BJI}A+!bg`O2+uOla+e|?;=EEpcFt+7ek?PFH&-{ zv@R_u3M{pei1a=YxB^E+!XkhrrOseHS)lXv zqDfZ+BQcSj1(pJAKS}NM#O=UCR$Op8>yD+^=9j?D;@jFKffMbdAMAZ>`N6m+mSV%F zmfw0j18*h$wpS_h->hY6^EZK7jN=8qqiB+yLgm5bUN^p~LNw8QZv`g2W`M*sQQ^9$ zrUDgRn*n^_%>jz%LOf+%#VaASDnJ3Qp9}p#t|$`4a|#9Q`15avD-~_+rQ%DCB1u$B z(kZHA{DPSdvNIAEmD@-h$oDA<%t2(Ju#DQJDNyk^ue=~bvD})ggrsFFFnews=3I^f z6_4}E?HefI92j>)n#KyuE+mK9_niV0j}u<`a5Kd|!C^v61?B~A9LA-!0u_|=%4^yx zMhgy+?G-=Zyx=mrql4nK=&6&sC^lhX?7Jy02$+XG6^Nt16oiVwOdFuyD7vk`qC|{M zD~4b@$0!0YbxF>RASbFCrm#Vdxf0Cm;!lp$b)>~kZjMr5t}-y`I4zFXag5>_X0r2d z6y-R@E@3&ZC;Sgs6;4v1Rtx^ks_In54#Dd7bgUu%Ls$7T66;w{7@;1CcdMB7tpu+6j}#q zz7l^_k&3o^fc2)0PAK4>Djz{J&MO*VyEOU5_Ngm{M4>&cC1wwPR16cNc(0!ns2x$R zLWWBqGpGxVzomeQKWAF&jshnBy!iKB#b0QSiWx%1n<0fEg%SjcV4`p4l>W+=7k19@Xo(&85RV=J1@?R3WDIB7v1B7 zAb97+%Z4Bb-g)s$Y7hkPy!a$DC5WKc;j?btRvZ8y?V#M@7N;Z12v>M&qBgoN~ z6m=WrPEYj?s*iCqxif>h5{H37KE&_$ZjDLnCM`Xr7u2_L@%5mp{y{OA;n9qWnVUo1 zp(N|3OdQr8I4B5~E|`oYY*^4~gucvxwdu*>L1!`D5fH{mNpfoc(6Rz;JvL~+9eMZj zC@&g3Ip~YE#4cu95GE`G?1^;Q^dQWoQwo(E2m{zXH|QZIE(VB&q}GL?KKc{*t!V&} z+Ket=6!b}AL^drCLQGqU8LKv}w=xJ7S(PDdUmY}GG*7d2L72IC8CLQ7AXJQ1hR}Xv z&>q1?u_XvIc`d_Qw>1bAZIvOc-4V21u<5)z2owI6VLjXvgo?Y$5c=;68X~cocQ9ye zxq5lQp`ateW5gfD_DOEewdsyyK_>*(_)|d}%EjyB=^)f5DOXaPAA`gu>5z*-!$c2r zxQgy!lmQ|KjG+W72ht`tf*|VQp1IboAk0Mqvt)sE#qFRM$bm+%>2Nm)t~GEr9wqoz z=aS91AM~MoY+64G+9cT2d{R!`EdMR&E@tXt)^1tcCZv1^Tx5Ce=$F1H&vd%Y*X^5sGXENPVNYXoKM)m-j6QF!;FUinAr9^Fiaj?VvURIK>lO73IRw2>0aI}||=+AbB_1ElP=s0Lz}mE|yYlj1nS1-U6QSN{9e?@y`TM71nq9j(rJ7c&`N5(l5!% zr|52JhJpOFEX~ioG$o|N7AYYV<={FtyB41Gl7ZBHjN=y(2b-yQBZ@_VsY^`3Ijz6S?1E+0PSi6&6N0r40am;|V zsmF0;MNGfp2xWPIUYt`tawi9B*m%(=ca&Cw-G_ThOb}+qcBSp_E8AgwOc}zYhf42q zArL@tC(47$R^--*U{A92pc3N>%o-POk-z$@GSp6LLa*ma%=Jn$U~RhS4;XUGWdxz6 z%GK5qnra?~1|Wgl-%w1!H3Q zGOY1#!RLj?XjeHH6S|aPJ@N`J7FZ{%2V?GemtiH>2u6j7fUs6NLR;(`3`c0W1+DiB zMt1WpR4)rg-QNTvTWGDo;GacHIVpp8;jDNIT^x+!K!HO?(I6>d_{aYo9*o+A<0|MM z84OE3ycwdSum-k;o{bKU!Yo(v|CuxPVPSz zVm@X`q%6VhVjtB5O?e=R>b)Ro+%s zbEK=3wTcIZkj{}R>^;X>t*TQYwOEzYJ8d%cYEB^`RSp_oT+A^XIDSW84T=7BKN4&#tKYkXj8?nB{Ch*(ENec3-Z7q%T~vhE=K) zxSnv3zFnI{}c;{z&Gl+8IfA6Bgw98MIeFt7UHFg=f{{t$h&`zaOXnlp!~ zc1HDDz|^>)!rZ>(FlR2RP!Tm$URriDm9#P1+tOY?s#H-K@Q*I*GFuL8SeC6>161te`+@e57LcxtViTKPe}h7r&`cL3bIOmCsZ+1)Iq) zu;wRplT@nefcDab|Gb~tftJ2gJ;hA>xMXk%brAKnR-ea&D;&Xm({uz)byXil!&q*M z?(tT`yU8y;PV%Ck>Zm)55w4$~+8S%s1hBnnYnghqE7G7T+;tmcO%^K+UNl9go-atf zidLgSQLZhh|3dL5Dp#C#>S2W@*m9fMTf zQewVw+JlxHQyb7Q#Z~3~Xs2`P9Y`Y{LrQ9dxRdy%s*3dYt7?^lG*=J4t$vT3i~R$U zJk1Ytp|-!M&j@q7dtVK=_IPvT|Ehj0icOxW_lQ>Z`BPmHa{v;oNj$goFLi62>nQ_p zp>^J?;VKg+`s|~6saH7=>(Ey2As_7}z4oXUas;ba3N7_gD6+&SWH{{LGh^;mKO|ge zZ?__(T-vKnrl~?2mP6L+bgMdqWw>A+Zki@!Ocfkh*@A&PeV7)K1BCZQ1|PEu@79QM?=EMO%#ssRTKr8L<(gLNEcn8QYb5j0x$4 zR4ixbN+PF&ADf>a>PE+n4LOCGrCHc8tSJaR1HcV>en!Y6J4q`=^Fl5mV0d#)njeA+ z#(CwmMIkVo=PeVyG~^8Z3 zH=9Flef!f1hdH}7ulhOEF$ z(?e22w~*pzA$6p!{`{LE{oxJb%!mH=OUM-j8#ADs9xVw$Wyvi=xce}~U0CebUqfz| zi_N{?LOd{29ma-&f}#bHS})5<X z*?WYJz=UOHHqQSzyYX}NP(REXKf|K;>V;0mEGuv#Zo#2dC|r$xjYXj9n9yQGWeSf! zfT%|}?HTt%L>BN@jY7Sg$mi{Yy=aZrq5ZHPg_sdV0`T$qp>Q9Y`CU4qODHNc6DPW? zN2n*27lh8VmN;JS6S@%1k%KiK5b7ayZ#rUdC}dvaBy)yZigkyFLS~!gRR?>}`y)c% zB2LaoIzIB^ePw!fN+?{eW9lXQCWm^Gl6E29bk+3GMQD%7VMFGGih+bSAQt9+3oONV z3z-mHwB3tCM`2cwxc*CMXnoQ>HO7->YzcL;ml~n#u26z)1PX|`LjQ>eLLuQCCt2lC zDC`UJ;@QKYyF_tXQRq%l%sUpk#A@Egp&r!gMCfg_JeO|Ob0KiQ-<#gQ9-8JL$rFD! zR4+|g=;ZsMunfj|GS$Dm1eQdokJWQq3vMw95?OO=sI{;B*dGn^Of1 ztcmjCPp%qR6XnGd9-0Lh#}AKKF5OG%=B+u2umB1ORix07PEc!L5tQ@!7^#6pP+oi; zuQ9j&!9 z?MmCU*ZhQ05;uzJi$m7v ziD14|G~;8bJMo%q-IlcZvzxd{?>0;0O6E+_bU@50R+MVm&`DD@))>QOl}W8x8pO&G zfx6wvh%Fj%dtk(D4ZK3wt(~7c88}~qIVNugtWAGkp!pp$S1Uu9zE}gV5aw(WmuVK^ z^vcDnP3JGy#=#&dHb zB}E!Xa!X&`lRO*Qy#kqLAJ(4k-J_XhL#C{#??qeh*T7wG-h96w)bzkA44&djtq*Gw zu+2vSAr9UK1y)Vfr!@O8^A@wbKBV^fFu1UDO;efpCWm2W?B+HUXVjjY*ErcoTC%#V z!6XVX1JW5tsWFE-Sg3YTR7Q*ubHkOBBy;42{A?mJ1C;CJYHc^=6sNk@5 zNDWy?y-|f-LUv?Ess=64mJ7g>yo?KTqJKn(`6ImnqamS?7r%yeBg(k2>t;7qH9jn% z9MXA`%gJFbgI@HSI2-mm8I>NH2xYED!!|o#+TY{lLHlSPx zOk6R|I@c+z7Or6hnQ5KF;LI8yC>*+n{ZJk(0$5VU>j6E&b_>wx-eGJ1H=yhKpq(L? zwF;R&6|7{_NVDsQ`P&S^vS?_zL@$G`O(Tb+9pyi0QlM#$rMip`+fWV_{*zqEn6PU| zmr_Qq0_`{{>_+)uzD{R;b4K|(LKVVcPR$BSv^b|)bHY-~N3C=;Sjort8VyNZ81}kc zif2&t*`lxnGdg^GOQY zgVaIXfp#iF&$Cz~1n`O0)uUnC1>T&K_(`M%@6#!?+sr40#bKT_?n2mM!N|23FCoJs zfK{6mjUVJh$6N_JjvNIgUL1?7%;Q?vBr&Vpd;>kJ6sHYhgPqLYQpeA@OE4(4`SUVd^S#Rh1AqF9F>t1I2F>z-NV_dG(d4= zP_OX$-_C&oObd*cgL?T?JutkbwPcYi2Zy^OdvHDh97vMh(aoMV92P!TH2=qu;Y&pG zum3*$1!hf`2}j~@K6NJ9RcmNS+lk>HzgV@R0k96~OB{>^htC9Na!?9@uh}!m*BmKA@6mRu{HrW`CN?AAR zRd^N|wliF0BX7DTye&d8429~phM&N+jhOsHz)W(*NN{ElP52LcWj&>Eo zLdvMTudD6PfO`6A2bBwm_-h|CpyBdzw$r6RZ4t(B%VdgEYMU`C3sqXo<~jqSV1R*` z(M@9U6k@5OaIL+R0B&A{b|E5crGWJbQBSh@OGd3TeV(k%LQA2}@O2~$Dl`tGpR%++ zpqqrjz?oBRH8$Zmy1uO!ZPQRY*A8Z2e>K(KfUZq1w$lC$|D@;hwIeXM{&|C4>Y+vD zh~Nzdm8+5`{hhsNrvhyq#%*GMtsO>l;QQ6|+s?QHEE%mKqPHq91j zZ zNX{$^B6 zbOCic?zyQgv||j&%)8oY-!2vf5I;<`3iW!@$`7;)xcubsBkja*e>&kPzdqJ_B6Zi3 z&i_rjN}`bQOgkA%VZd{3GMYk*moVGnZSdfg78B+&E$sPI+Y~c2LERiOZLqt4IE8D7 z`s35;G`)Gmk5weHnm!SZn1KMqm%vEZiw#}q$pI0SLO8p}>msNXFKylsv07rBPa{@T zpLa#tfbe>*^zx5_;F_UUh}qjqMZuN`!!F{1=Lq%Ub% zQ8yGJUE2a(S7{w+yo=7p6R1q~)uDp=0$m?^v5xL%gqjGj=tI-%>tM-c8#(N+LoGxx z466VgYEh08Xc?%374mK5T99rfPL+LV^H>C$Ofa?pHK{;5HJ0@AGw-9O#_D$#%L+=+2<+q)TTS7~KW0fumh@xW!)98S3}Y zbzuai^};wr3L1z<3vcMF%VE!++aK=>uMg0jM-ZS_2J7y#XYU+_ky{$g-VfJ3LOffl zJaUw7G$UaBy$*BEhnrr;SlwS}+kZS>H-ZspKS_t#Amao!P1gO6)_`uhL}2esG=V*{ z%kliF^Q69aYd*g3Ra>Y#gHQlt|k(9L2k@y9N-71HoMx{e4dkQ_nmJ{{cM+(r%_ z&|$)5u0^{;y3S|QCKcL_ZXoWHGG&IokBi}wV3e$ib( z6Y#sQJH!Y)e5l*}?OLQqAL(+@+TZ;|cbYwW`0u*y-~Mc=17CSojwx{73tcfIQ16v) zCzinA*E&?jS#CRT-@qgW2;6?BqgVoAA9ScogPg$LPdYgC0G;oPZa zwz3|xYRNJF_R=@PED`d`Z)@tOGcAtw(W8gLOTPLVNK@!Tr_|HKp|x$KvcDcZDQ_jy zKVTRMf%@;!=A_VnWTwzgrC*K3_(83o&3MFW^wY5z1z~#b92!OFS7I?1M(Q=_1}TWr zH)KMbjL~D3<+)W^$LSxV2d7C1`az6x$7 zhSosUEU5{)=jhRkG#hjEtC#SY-m0tI^`Q z`tz7`bsS;q0n-e+X_>w%rfJLw9%N+Q6nolYwSEei3VpgxACI|fz~SO|>z&DvVpA}A zxKn=z(XSZ_1(cudflAGT`aO)&k4N+l?C+mX>JPc#Kyyj0SEjyn>NWi?W-*Dk^fHVA z0zHmYiGKW9?~j<8Q9u}ow3wEpYii6i`sk6~$pK_|@l1c)3O>*Np>OPt&PK*3w5=>E1k>L*vMMdPC+V+< z@}zTvqLwhRHR>poDVpIb&|gENlxT&0ii>il%k@#Wup&`FIK%|mmlQS46`^tNIMjoa z;eEAL(mhG(qvSjq)gTJqsk@Cn{0^raGiGJFtXWhNMmfB9YGh}eo6Q-)_Bcgsn(4_E? zD@bzrbnK+4ME3AA)1v;s7!Yr#fd`UZ$d;YS?aBPvQJ!Sl@+cpAYfcngve`z{7L>yP z0N9gsS`y_-HaClEOuHz?o#)>(W*IS|nVP0}& z=3+}1!0FWo`cP`3?4Jw1KMD;*ekwr(gWcbJAY4rW=C^=R~ zgtGz|cS`R?c`qvW^JXiiEn4(B>HwMxRQ8hQM^&w(SJVJ5`)WmJpj$2~D!L*$ zd)B)>`7JcslRAY(Ph?g;J2DzI25|WRGYsS5c;(W>==*5pKxJpzG9`Kk^FwYK(J!&? zpB6Tc4zB>lyr)AnX2p;93c#pJFLsRX&Aen>=jbiWYrg0jU6Xw}_lWM#SdJ=)W^pfV z(l^=-4!qGWBcm&^zXy+s4yXm6E0;$9!s?G&9{mWd7w6L{QKZh==n`zd1OOL0eO+`e z9Cf4jH%0S#(WNcXewDC@MLse1q(xCQINAsLV7mKov@dcNnnM^C1+2j?!0yJy=%1Lv zwp@Fz1SSQ*yS`9a5vojD%=A67_2|CL`g$pj>IulV~k-Chz`` z7+-!VF&_OY`W|DP|7Wxp=CYUNG4#vd(M`&wRRRbuW*VctYeURL%v69sab#$kBU!!+QYIB$Pu)c)?u==RSycG{vFz!DBQZ}| zEB84XvyV+#g3iVqfU$%A_G8QiSNPohAm%=kgZvu9C(h()OluGL``xFQe&vy;8fiTx z)|2|$#m>j1tTBsHo3^PGdy5I6caMF;2&;T!r?YN$L>c>(5#JCJTZw({3yVF#8mxbO zY-cp_<$crObqy0zA#rT?v_8~6Ep`!RwTrU{EMJz6^89;1f(p(awTt;#`;oMnpVY%4xAI~&U(hg`LVo%e^?m%iHZ1j zu_R)RRk5*5#Ae%LpE1!)yJKsy&-Qy`KQIwn9WIB6A5X@@U7KfVL+O{(v0X586W)eq z7<=k*F?Io*U#FW&VuPLF^XI3rHLwjp3tz`J1QE&gcd?iW9H#~tuF_ol_50YT%!clK zijBltD_v?8w;HV`sO(Pf+Q%(m?KiMO+#%*6!>h(EV}D=wj(dkh?n8U|#^oX$IRzCB z$1HwAy*x6lSvp*IZJ55AZcxW9L2~CX-t=L3+yiFyry}BBv)-^TI_?wu+?^0N3_U;n z@?Bh2a`zF$>qiEpcch7VaeDxhd}ti^J#vCU0g@erqRX4by~WrypV$2`HN7$oY#*nG zb|)P=#bGvo%|PCCdgnMdWi|FP5iY%##EXb93P75sb&Nh>ZFN(!#I5jkLOE=mm}gYG4op*6~D?2 z*{w)S-;9~GL1ugy>z@;H<1t&%oFV{Kq_gtkkFX}sYZ(6yGeP7?!^daLrY+mXXQ9;v z2+lO3V?1BU_`OT~L3C#rRv7P2Hk@_vByE;wR43WXGYaUn!SRdO;8-v+9&^i^$w(P4 zYrAP<;^UcpU7i&G8RJ6Ox;D+38Q%%x5}`7`)agUNTNp3*B4aBxN+D;q#;^Z&W1?^~ z74@=@D|h1aXzY%7UyJ~(GJ(qWr1VX^BYC+$9+MguPzFhN03#2^XWNnG%NoI((2vG% z`F1rBz$&Egj>TWZIC5UO|H=4$))I%fGx3PDkrIaqXX70(`v9Cn{DpYPK+pB?_oeu` zSUn^a$6rS4;rP|~-Gal!>+$oj9Exwmd!zNR=vI7B!NL1Z{J3xDKtY|Tmc|Pd60l9ZEzVbYIf<5`5t{F`0$r6kR zHmv(gPZ) z6K_w4CMB3$Bmr;aB%rb(a%FL_It>!m34^@VIAN+N4s4e2RTRg!N{A5_c|AX&ve`~M zTE0!`mS7{kZP6=XmMDJhn{ZYb=dJz;>qK$pz=TQS2SLLuKd>KRG4J~$E#^Icl*PQi z8sTDnY7}w`Hp?Sv>X4+bvCIM#>Fvs0FAHa|@tyhAvHuSU_O@;er3$;e@s|iZm`zZ5_%)-o`QTM8HrK9Bw!M_LiNS`^%Y<5 zC7@p30s!OauqO#OFi|d4?nDm%!N%j433-^vkt1yRlHfrA!JE2TB~He4(WA=}3%S+O zoC=9fH9?;|Q*%%M#2lvFBY}w+b16f03`(^3gkDuCF45WzK0UG%dpN;oVUt7;XZW1m zE-|12d>-tPc-)?S-!HK{`}|`_qS6jNYZNB-1-qrw$0VA75WO)zu_60gF*$J)`~S@u ziM1Tz^RM}d-4Iqpfaz#bd0FDHZ?|I#bz?B&F?%N6z9LbFuDoPbBI=MVJb4ruzXsc; zqsaNSiD$lDQ%uXFh;e-)=8gobOJ{CKv~_@Hp0_Pg#?0pMfkY)UgXf16pIO1@&7wru zBR@^o9!vbnKTjlT+5hjJPK3h~l=oKJ*}A~3g$iLkbGnhq{bJkCC2eoTZd zm(%pa^~6i;^UTe}7-nwvcN2RvoIxdtuIzLC!$cSM|GAG7iM= zp9FW#;IU3gm)U1Z#UwbHcAEC7lr)TezVt|PV4v^2lKA6?R!>r~zc)7Wl6tbCNA#Rlls#(s-&at!ch=iNRlVXIS3x8T|!be zJcH~^NM4P;ufnwgZw8Zl#TQWN&~n)yi&F*9}x1M(y@sVbWZVA3K1%4%sz zWc#wDSz!FcurBEoqVoaJCi-N3(iV`3%-fW-7>iM9OOlRZq;5~bOw%}z2RkGlGxsE6 zGGTEH>%B=yjECt!QV~{`hX<36FpO12NtoL@oJWmgl0F)pOgfC^QF5&wNkdp05&UD15q+Lu=j3;UO z#q2fveNBQ5gwrI+#(?Y(5k{>+i=ELo2|5^rTCeA2dLtEA&5B&@WFydSZ1+JwL-k!7|$PfgBp)}#$t~4}Z zj4Om15Q7c{)Kf0NYJR-o9AnweV5r4dj!HFL$FKyY51@Z%8#V%%MDS1pLl;I+*2I8x z-~$K%)}vFK8Z^uvjL(at*}QP@bVDTjd&eBZRE!Wjn!3Ew z0PhdW)q2uTiwv%eHy5Jzas%cT4q=!SFi)_V*PJzmYfRf+))_7$2}-LA6jm2byI8G} za+o$+_IoAr>Y!oSxBFgt^pK$*CIVTJXlz1z95J9Wq_fIIf6{=71o(qno-#bMAu~p_ zbSF*E8TNd;+=NpAm?~t(Z(1ix&l^r+)j${wUqz^Q$#BG4qV;F70h6rFLW`=e8Zt3X z0}z_R5MYo=?_x{5Y1k}jYTg~gRGgwXo{F088crc~BoI#CGYl5`$$nrMk7PqZy78Fq zV^)njQI|q;?2Q*Yd(tJp8?0=|v+Aw9$dl&=&v`eE-n8To!|w73>P`VaMbbK*_@}{7 z6p#F6=v0>pAAZh@@`k-Da3q@89#3k0XW#km@Ft>u{WC8I#e)jMY7E& z(cVV!GWccnzpFsL{$^-F&$$`jSxKs1P{}B%+Q!p3L|DW$FXQ((i{OuQr8}z_6Ohfu z5qwCoD#eZDH1PK)cd8jLmV*q^v5DR9*)@#yJta4CCfK+D$D@#XsEs8^eH7AFp~i8d zVf5ifq&$Umtk#Iig9{c~NMA-8;hnQDi!Y01k)(mF3#9NaJZFV zB;Vc^6gZeL3kaP1{A6Qa@%Wl)_-5#oZbaq6gMKrY@Ki}H-9bhw;@p^-%o zXTHPh!Lz9ml@XV7SliqBOWFxmOWKHqLp%zYQqKJAPp0;XL@Bj#ub zhuP4{h)RviD@S%Ub`czQbT=YGCP|bJJ&dS?C!E8K0t<%!Qt#Ul(U>U-k6TL zCBz>;b+U1cczm5{cop=VZbW6w0*!1nIX^NgsZxtznQ1;$x|gK4o5 z5fw_^a^4anCT}j?zTD^`O()5HAIa@hUEgr7nWurWOn6uQ})HX|v}vkoRyc3s{S4Jw#m4UBil4lX84 zieV0O+SP9`=a-!caXP%Cd@qy4%0Bmgvz(eD>u_vTE$gs zLJym*BTT52yPShUZ(1ap^rvXNWyoVq4+Koj1XI6n@9Y%Tq^8+QUA2wLgxp~$q+63s zy#x_enhA5Wn5(6Ay6L@u>6~RkFVOAIHldR5at;gfOq~S>k47fU_Bn_7uCWP~f0tMO zqlsyX;4rDV3B6c)y@d&tf|qmH)7sQsaM0wN5DS%3&@!~036+JHb9m9g(iWXMo6ze) zTe_H53sKs2H}ybt6RRGku3|(RR$xLejvec5!Y@oU?Pr=Or1*1y33CF1>ojhV373pF z(U?zirkbQ=ycCl0wi6mTHp+xt6X1aU)O(ESg1~w=4(kmCR_+ATA%W#H1?$lADF}L5 zz^G?pd!V2+W|pZQdgCic2^(LKlh>OzpKt1E%eTAeFN;lX$XN++0z?XVd4G}oJ{`2$ zS zp^&Ke5N264V5`37hsZAfz!<=J;s~dDWvaRinh#F{>iJ* zs)fpKbe=rsNZNa`JZj_FgWc() zugPA*Z&$ES8CwNAk*dLkVduO*p$ZTrnDsgrGp!$>=3;s zx@ih}z#xF=L4R+a5{HwtY%_w2F8V;Lt89JA{{d+dbt2dcc4jzC~0sJY}*I z*EhL5E(NnL$$JjZ2Yh>cN^jv*TTDz@DqOAel$4617%(lRgJ8aNX38n)+jaftq*Rnz zeBAPsuL5A+%9Lugq;HQLNx3AxU3xU-H>~m~B>Js`p5w4A!344dcck^trdSDQqrH@3 zo`TgYPLWqa_nm(y4E88V@kbLiCm-HNhdoHSAr#y0Wy)!x!l+j%D@F0e>y$&Hc<@b1 zibr|qQ^+73Q9jau;7TBIa7VJi5ys1@wNicPJEzn`LfI!PrJ7|};+|@jA;&ZIvXDXT zm0BHhJ)aveR~uvmj-r)(Q}I(_GmJmN5(~F1k6Mq>nTe^kE>cfg zn{83yh+K;bLmH$O3l(}aO3kQ@HG{Gw!>auzXA;yk6_aF`iw;mrXJSHk_Cmk2zEHFPO?SVrnX^ne)lx-s!2{Vw`z6JN1UJ z+H(t1Utwcm3Uphc8y_lrk-@iNOwC-C+Cw;k^IKERGu$^jQU?pZBWSAGQ*=0yYF_oQ zK9f2^{9xvV)Y<6SO2LEFT2fZr7dv7W(SA2l2VxgfC}7!(!DRfS)D2=XmGC@uV+F}l z{{AcVpsQ3oXP0(Gj5eE`(!`zw-CZ#)+e7++mrvS=@<`=QimU1z$#j-}nWZZxkgj+F zy%vyG3zM^$TNcPCp;AD7B20dKSlS~Y)3Btpk)ktK%uTx`#QUpZ+E<|!`zC1{oh9*( zwM*+$9`P!WFWq9?iE3)9BX2$mNsa4aZJB^%Wq^<%zHgdJYP~%})69PT=CHJlLXJ5j z)66}7^r*B@@$JbmY1@QS#*Ixg&s#T5POB>Pb8SJ|k3uPXm!$nIs-0S%_E_}CgKN_! zl}{DW=?Vm}>d_Z_(vk%`)4?=xW|ZzZoc2tJx}(S<>djMW z!9vuk*VBF$qT1X_a}mWlchbzEP>l!W5EZz zG0+vZ=`v~JNpHEN`=Q%}KLN5CPodSSriBiuB0UYNbCFX5`_Uo``O5 z4hxx%r_loc^q}%7lqXD%Ql!rijeaLMeY0@z3!~Dfi*NOD>7UB?oKGp~UZk6ETqvzz zOh>)iz&u3L}OcW<|Odp4s&r=||P3p|VdRRJg&7d2pSD5Yr7kcBOXzStWF~a^fk4*oH>?e)L z#;NeWFG#N3o^F_!{#-&NYp0}R_W41`Mic<$!qOhkNOwkb3|ePi`Yz1;2I{q^UFWBt z3Y12=gyZSfh{htL!ZI4ub63+*xlvhVa%W52SXz25{jOm0%U6qC{bZFpj2aPuai+4t8An9xz8jKZh0z%EauiY|j@4%UimhTK!mtbUk(XoecBUx4?TDg@|z3{5V%? zEXf#-@qlIoem%7k9sVHWuy7?A&ohRJZgJ~n#xzkJ^k>EZQT+N>#w1Z(_%Y*qQB-}& z7$}O=Dszn}_OQzwA)4s3L*_$w$=#M#&s;8AYek*R6QVdko;gJnJwq~gh~kaN%pXK? zW_;#cQH)5*d@qXeIhIFU&dc0iSvnlCv{U9?JE=IMN2bPJD&FjyiP}OmYYTG-3+fJ+ zk}Z86oariTzeZsu<{%SbjisGNWu8Db`B-{?Oy)?bK}hd$cn*igXBMM5Bu&cfBRHIz zk~tk`|6{51w9E|5Rv1^J%S;Q08M8BIV>#TKllc=u4-`1mle!4;T9~;6`}od_GS?$i zKogc`28hQGS&@l3{mPYCyfPDYtdCbtS(7QBh2aP?M*-^?SLHC`>e1Yg9OtYORz)luJ$PE_?KGZ9OY0>XJGX`sCKH8UGCiQ-Ir zf}T0j6ZTmFSSExakPh?Hz3F1dtVd!TpX-|S7Sq%)u?wfAm9t_n0

jDreG?8d;_6 z2-uZcSv@L1lB7CA?JJWd+9x2(fN4Aa0JYW7U9>nT3w5-^jF3ausj{Am z=wU@j)>IJ?#%Z%`h1oTX%&LV^A!}CpH73hR%%f@t0o9zIkm)R*L9i%2iqLG68lgi9TPQHD46x zuFcvmir=lz+9r-ORNHF#?N8e*zb)Kh`EBB^tS#cVRtGGE&t9-J}+U`&%dy~*g-2lsP@5?Q}oush*Hba?x zPt5tF!?V}^kJLgyEkDrQ=U|TH3PHbg>L>DW|4RI z2#dU}3N7;bj?P{vbaQ@!<+qb2S$?aTV)^acso6gY-Q1d+Jr*;JLo@UAX+G3`X?AKA ztFKZTII=5yKc;s=mj8LRaw?O3yR*L_>L=vBQjp~bv*XC=`}vh=N1FW$W(mj)^COzV z-RyKyv}Bl^CLPYcU<+DtJDz>DTxc^=yVj=bPGncX%=ILJ7%2`j`gHa-YapdLm%YCH zq?Vu0o@fJTftRz_lpk$QF?0s2uk)^D?*O zdM+tRw6~*`O0pq?_gB(;drL2x|0sKvJrJ{hmfge-K3kV&J3GK~r~->}2>c z{mnY(F#Fv)r=0pu@Oi-_=Q(PZmv@Z2*?LlR`=uLgRVydW9k}pxl81tGrm^at0tw+I_uy6%x2$PX&@OaAaHJy1ShRdE6wYAp)xSXpWjLY?`wPb6$y6 zCMkY-KIC5KoZe(xr<_XU+2Mp8q)o>h2l}-`PIJse7EnV-d_j(!FLn`i&sryOb} z6>r|mflDF0n)*>rT^Fg??p02)_^sh%&J1`pB*T5|k_%U+cyY0N?ofZJ7!a9zRkCmD z9Gm+Q+$H;M6=Uwi%2IJ#)7%zrQn6Oo+;D;0u1{`zC+XXpLv!J%BCi%YJ@<+DLE!w{ z1k8REC_+o(@4WPar2M%D9VD&p-Ij~BR{=J7S8kCIK5cKVS$!V+b4Ll`S0BmUAQjiO zI+-h)qSuAouHyf{U&+0QG}1)UW<-t;sbtj`-UD$vcSHqAsJ|ZN+F~YzprJBE2Xg67 z8+*FoX>I`|EoDvT{UX;!a1Q=6_mODOdLMHaRgivg**0&eu&?G0d56Uh0$uYai>5nO zCC@B%Le0Fjn9U^M$o=zmtIN_Hyn}zZ9sfc>Z10%sx1v*`^6uD>mj{B}$>6xWBY1&YQF!lZE5w|IaiO4* z0DDq>a*!82l$rMlG2Jzb)RDH&$s6OsMWXLp=D|^KCLKN1I`5{PRNUGjZ@9HojOm;Q zN2>V`O1u0&*4{fTs-t}$|14$cO+*nDu$!oe6?==lB5Yw|xG5_j5nblylCUIYBD^ zwD8QJ)_mEL+@OF5v?&X!0ti8X)BsuJwcFbUCxN>I0&?xE_ZU={-CLpz{`U{kVjw~#<;^l6_Bet?&Q5EgJx0!-<%GD z?AXF#tU43a8ge}|^mb4d3jBGpJI=ovL_Xz4B9VLv1>d|86aa{6OyCj-2(Gvv^vQ;# zTb>2g(RA(0AaZ573unCwx<=DUZ-Nfd)cIY|W}t3isCr|NJF4~#_QENjg8WU~cjI8t zwn9Y4dPshDW3&v$mV{=2P4IPUhO&6t z3@)V{7sds@CS5h2mK;2Wa;?q^-bZbiZ47<|ne zhU-=b*HNOin}T1_^q0!uZj^A7y}=i$_)qo)AExR01Hn6KDmxUsho-Jag2ynAsF<3|&REI0U?J3U0o5A;~5M3Vx`%=RLp9F_N=E+>N5B?ZzkG#4>xMN8TvDGBO z`x=u39&dwJfF&U4LP~X{g(%dXq~(sHw^Rb;Au4btsb^c!aLDB^q0UVp?k#?^l6|07 z*j;o0Fm};7J}R3p@<5}@Lo`T*eH)?b&Ox@wf4Qg$+TTN@0e~JY6De^;Pf=$`%+r$* zIa!F30z`{owX4vop`t!)W^=EPD9A?e3+g^ZbO(rr2^#2bJF$hMG6YW@E;?yJSd1Gb zssOT}HVwgNMvHuavhq}Z7$+iMZu|w!m?+xSASw=lB3Hm=A*xs9xkz-0QaLCUecvD| zLuDdu!&J!#6CI;e`l>{by(a5z+7~Vw0i}ZHsYMo+gvG)r5o9V{&%!xIbOOqP1={L( z(GOG*d13>p{FEfx1GpMBhe>NZI8!v&iBO@_GUPr*$0b>v* zmR8bm;)KI%Ov3G2E6M^|T8!^+5#p02wk|L~w2JT`Tb{N;B3*ycXEy z)XzP<&;e)h89@oVTdBHjD@0dZ#Fc!F?>^edo@#BkzS^kB zfL;N;)V=#e_3YKRXF#{e$X@CQb%a0SfD67BH(?X4V@RpemTC(h3sJghed! z7nvNS?I!-wno#K+Aco9AO=#YDVIQ$Iq_x%~OzJPrqNC1bc4GUciV`gw%-+lHy)Glr?Mbey;x7Au^)z45E@;x-MVQZ-3TzG+*6ZU%`D z0;ymUdF>%j?Wm;qrCdDIoe=IHi`!U>ZT0^;SO8Gi>UDj zwuu3E6mWTkxFz78KdXD%PH`cv+jcix_`*G6(AU;Dn6wkTbdF>!Fz#f?_@u@TXDpV4 zn`t~>CAk5%ii4A^mMo{lUagbtq%CyaX30jHPTbCj7kw|e0Sr-`=b-c}cLRHTJ(J8Akiu)Q1wtvl(hUCXhb@M@!kB zUnRA){#P#~TYx5&{wnEKQvJ~uhyNkT1UK=Q4<#*d-djlxi72eAlboXd>h*#7P54Q2 znf|`{m*fCVOTS3U!7Y!}Pge$7Sd=)+v^r8jm1;iO7A!ljuC0$9yqg`G>ta97d@nqW=8?h@&Ktb zcI_ptg>*O^C4#Vk+SO%%R7^X^;^ERy)HMYor5|aUJ6c*{L()!xQZX%8D3XqY1gVLx z54(l=NG1imc83YdG}>?dpprta(a7mDxDfw3Lb?EQsX_seZ}wMqI8P(pPQBM9S_)bD z#Q_aT4Yb4MG14q370#Ah@zRf!$xn&WQ-G##)Z`SYJf4Af*!vmGyT6Z zrB_LyvG)7KD9xo&r=BkTokrc{InrbFcjHCU3p6SpEtcx-NGe(-ZA$*XAz_UavKoWf z9*6Ru)i|KTSSmzYGsd~&PLM!p|+v2mgq_ct2$6IA>4O&4`~BYo$F-7zY%+)djkT)vD7JKEAb_x;Mp{!93uGNFl|wR zJWq&Yy+Y2@F0FT)5Cf&Xy<^B(8a&xwhn(VE&^^S4x?pVYkX^Lg=RqO6fU|8GN*W&G zjK_=!352+UV^0y#>lqPVc-*LvC$vR#L)pl&Ax_jOqQDSqYDK6x#6VeJk%eIJ*h$p4 zLjI8F znip~jvb`8l>pz~0Ve(uoSs+-#p7veTMha2ivJl8~68f~S#6Qc8uBdtm=t%{KLnQd{ z>X4C;0H!A+aJtq{P}k9rYumJ=Vh zt_e9I;3)q2GGq_S9eNd_g!J{4Eb>dJb;Ol*Ax>6I7n@HeUA}w@>DeGyL|}!u%0jls zl4HC`DBHythuF&2u$-g4tO=96hqLTF%elJC`avo#_m#yWhwXOm_)kxnGxIa{Y9W&V zW`dZ2En#%Vwv+KfyLFIlWwbAJluckU)7ejUm>v7nRko7lwsezqXaD`9x9lK0cA$@p zcie)0vVD+CQsS=qUiGBl7#k!W50kA0^ab4bppUgf<8-wS`1fyQWFz3}Q))N-WP~i* zie;5!WjU5C``ZNBc|Ln;vJ5nZ4rUCWg7Wq#NpX8ckHi}g+n?H!ut(CU~*b0 z9ugtDVToMj9o^7ZS{Y>34uwGkrU`d>R1_uihlCD?azrh&M=m8Yex*#ZPF5#m+_KYT zpf%2vn+eq$A5E9R)|~zwX0JhZ+k)|0pCelXl>R?>eae-EL;B#qiRO)63S^ggUayK| zkoVpH0XTe$tmD7H%V)Zbe43q$u`4rWr484s#cbKAe}Pw%xiYfr9mi|u0@>+?@WLc& z(Z4`RRw^TVesh#0OJ(aDLWwpcZ@hh(>}G@7BW}4`CWhSov!GTW3WFDS46SrxriAtmox;1YW`LRhsX?J87sd_`HMy|+$WW6D8;uV& zx$wq>P?HPSO$s%+P&7GoCGT!aY3NQTCdQAN(Cf^{8@5G-vc`oE#e|aYRdEu|NeuPm z#oC<`It46N;O{k!@r{De5mua)NINCe9l2)6ys&C&=-1%BfrIThBeaxP?Bd+eGd$f* zC85c@5uKKXPU4TwUJ?2o)4+PzyCHJE^-I_R;|zxlKlSV3z>-3UC;k zVGw~4VpU&xb6XbB^M=Z&@$}n|mLKIEQ8!+Gn9rV`EGJ)^;H;i0l^^5n3zNyW@mVjq zyd94a7$HBzAN5ho$-DgAX>T?11$+S%FDD-%;Et})%Rd8sorOje%FhZOusQnScAdUP zg++4kq_!ZX)*Xiy%R!4NNQ|^VfyHtgR4`xO4K42-=7aK<$y55RfUmm#8|Ems=FD}v zd>Gk*WPJ-aq@FF`0~=lInNGq7=E(mf+muki6Z7OT4FSZ&bn=}94r|gPIpj&{-%Or_ zr!1DIK`v7N2Gb4sl*>DFk6!V?w^#Dmt#*Yd7- zyjeWfit@aQY@z<8AVv3pRZGuAB_~oN+Vn%gMmcWhD>gqxk~cpU9s(vZGsG%V+aP zN7czA`0Uqa2b4M>*gn8jXHer_kf<&|` zOscxWx&(aQDQpngF^M|3xqFyNjt7myOmb8-3ERP2t8X6mo{zEYc3~!wu6z|XgD;Ta z7q%HVtNqW=CjIm|boFB9XjBmp2HD--e2eF0zc3qI(I@OJaByyc2*PDxn=c(5HWI}R z4Qs-BIpct^rIvz&sM(OPLk&R01aS;B=Jt#Z8!jXqxJOd5iD8hh573!c3u4COTkZcwlf;pp9?DopmBHa zsyixdQRd-P4XRpe+5{l{dYCPmc|I%z0J`H**l_gaVwezzRfYLMR5Kw!-kKoiMxsG? zN$mTtvTTf87x~y?<)yGGR>)WE>4sik3xhn`GogCppr68mfHrZwys`M_F!GH6j!m;W zVUTGNWrK;0kZ%HFa`FX8r$o++L7)31df+Zk!Xm9n(j12$5)~_W4pGSpKnIBlOft}!i*Zc4g6v|+x#3Nw0`dTZvtnYF;w3O7 z7h|hj#T=ePdA?!^)D2&Z3eXl)oB@Z56f1cSx~U4to2(qng=vcZkP9Yqawa-FS0O|z z3YC8N@f<~Wpq!bQz%;c)qQik6`1t}w6>DVp3{sFOIdwFiw^Xs3EcT(X6obiDmw5-iw`MI^9F?- zQS9Zj?T;z8^VuiI6?gfv*Ul)&#}qk>*PT<4o3UJ0RHYz0*mBtx7ZqiEwyIiT&!6_} znu6?h%N5vrQ(*&G+J{Z!$O^luejIQ0*Oo+tU{ zwZfavws~(h^YsU_nX5l3w(_>AJ}dePnL(SZlmb2o_S-6Dkl~to9#^_6lOYCjVM9)K z!e>2{t*khw@9|Pz;Z6C)M`;Sz&8?KLd8QTZ%}3KZDkt-%T=z2{{mx(M&X+yXQ#k;# z9F8MaQTyBxlTBdaz@~8sWD#6zW*S)?pzLVFq;Ee?Np?`>!s^Xs@tOUJt$enDsrX+73SD_C-DNFwMAVM5Q z_nXQakVxWA-hNwog%T*fr`!l7P<>w+4S5j95xD(}aup?z`&0>-4KoGyKT{qD6KM0h zl5AjGg|^iwA@e}ah~Hl-L6-#F$*W&0zo!JG@06>dE?D`evI95<;yx(J`+rsF{wL*P zAb|~DpOw)1cOO<6X!U(8Rp4GA%}Qkttsl3sQ7xqe4%(@}A^6E&1$zCTi+~6xRXHVK z>8gS}!{RW8x~Z;$JK}xrs@;@;zKNd*#4023E11jk>;4l1&TcNMDmN(EVp#<~C3uT`L}x44tbyQsn`0SkZC0;v1@ zbW^RT!;XJU|7W1I!ty5`f*`eTZrvC2(vQ zB=oouaL;g6e{lWxN2^jn1_p(@D50cRDGH;fTke63nc$^58N&7@=kHSpFs zO{)UEQ^8p>Ax5>4`sZDoY6Ubv`Xs0>fFpEEl4=?y&^-m-LF!Ufpq18~0@4iCd`jS% z0o+7!`)pMJSOJSXMxew9Cvd4i)eB5uOR-8r6>yyf>7m&)eav(fXxnMd1)pb=5ta~G zFjocH8Ir@eIK5>7M}npg zig(7XHmf8Q$xxvp`}tSl(UmH)pMMp$-Kiq``B&lnyHsR9|0xI+=Tx33=Zn2NPB^KuwR;2)=axf;C3QFu;H8W)SWoC%{ zDl^3SIy2mvoBPjG7M28X&Zgc3*)22$z}to4iNJtnRbWyD*#V{sf4elCY#UI8 zLzahgy`<-g@MLP#tn%>I)Yxt7!*9_4Pum#Y3(__VPe%+v_db$GBYi8veF!phscvVu z5Mn?*TA62b{_8br-{1L5OoLaftm3H};Xn zh-=|>CP(1Jo8i!#l7o>6(*~2zG=`Dh3U_)OevDH8lG z=ZFzV?-b#|J|A`9V|bj4N%;N_5r-iym11IoxqxbJ;TA!*_aGg)M?`Cy_H7p7MN?7B zh@mtsZ51($rZ?L~fWB`|DwE>DogyX!m|}ABOQIeZXUL{|1V0A6?jErl(*8LyUHV3N zAwtLxM+HRm0I))wegx)`ncC<~f0iB36B!De;SNS+t;r;1ZB>jHZs`_zkHE5SSL+(d4}F6gM|Pz4L^q9W z4{mLw7AsWI_C+JAlNavNGV)hbRk)LHWGJm*TAN5)a0SNVT1VvPPc}Dk{VMXQ$-~&O zOXPa6UnwRgm|RP!=pH!|%&xk)))VLUiJWMT=z9Yr|AH*rXT2tc(|p5-{W2m_1|~+X zo~+Q^F_Dh=;JC=^rfVkjtHk8}YtqQZ;Al0YBZ6*&$=);cQ3k%Pi0lDnjE(BZa-t$` z6&u+REFb~#Vq4+d)J1A3r@?wSCwdKPg^qlvaYcRwWRET1uyA+mk`b9^@;17eZPN2( zPUPc;=}GIsq#n9D1D6#=HiAlts{LO$WB(bELUVH&H#;)BL2%^t-AEfmKBpzbfeRw5 zC`Io@k>s1IRrvMdNN#p>07br`4&1RKQb+qkpVg60kkyu4OSr!w(i4BbF>)wih@p30 z2O|z4Z*ye*ZMW~XMP`CUa9yNojM1)0^2IxTo>;LbvJC8T4({^4$W`>pF6C$>ck5vE z@kp{?VilfrDsn7Ua@qMvZXTF@A@VU~IX+!Cg~__9Z)^o0P~r9=qfz+1$PIvDmU~6) zNIUzIW4~$9tR=bpemO7G)Q;%I{m4)NWW?LZvH0kNNN-3lPESUmzeGZoE|7E6*!ui! zk0Xy-qnE4XZpipNax=_`TzvgSq!`j+5hA(xMs4H{3&vsDn@GT;a7QP4`q=J-=Hbqi0q^>RzD zFU6m-)#S|y+O^|rx$5t%ShlE8{WG7{O;Zm7a7CmdG4fxiZi;mCW8ATPj+$&e!8MlX zdFpR~)Ww(}hVs;xFIJOR$T{kYWoq(H2bb;gotnJU!DVl+P&=|=3%_5jP6M`fZv6B? zHk;K)1h?3-aF;D=(EHl~2{~Zh%q5R0WXUMD(+jhTpX>;)7ae#q7QJSv?&_ zh6D88ZK)xz2yxO~wbVeNFyR)+!}Pb+Kn7VN4`YNib{g^m1*L>HIceM>O@M;o{*5)$ zZP9x>9}m>5g~lDt@1^y^RxLICESSBMd^8C_>2N<^O;_ID>^7R&Ko~?|obkf8nukJW z@6=A3r!0_hUN_AhTXr%-xGg$*#1^f?~5h4wFO_U4sOOk(smrIBS z^wRo2cuft}NO@krhiT@);;7yW-ngw&vl%iQ`3J9zaLsa_S8KHfvWEK~fNG7V4P*c{ z!(b9*b9qYJV>OVMOUzg_V7>8gaT?H@KL3Ep(`gcTULTVF4PL#HHLD>LbBG7bnn(+dEN(kh1G?``DdMxUGyy_}(_{g>|G{x3nuq)ZLV+~L z`DsPBrJ9|5_RTVl7vJ^!lxxoLN8hj3%wrLSm#))H2RtOe#Ef$`%x#iACbucE<}~Dz zihybG{{5O1-j+d!&9*c;YPRLtG0iaG&?Lbt&uSjAFvF!6G@ihoLV^g6E%J%A7UFL% zX%cw8N3NRrF1l{!E4`ud0nRNZp`>3lHmKshMu?V-@%KaB?rGjZ`)&$_3Dg5Va@Ri6 z^n&Opz}jG}c&=H%&v{0?Hu>HEjd{+K^A0rUq1cFE+v3zeHLLlFPy3IciBCP&z)zZ6 z&TNYFm4kLT-w^WMwA1+L_h_so_wcxx;L~PW(8R}F8`{=N3%UP907R&)zt)%CR;+8S zJc#X2>ut z=v^qY7<)!&Gk6};M}c|JX3!CzAFZ7ZnVv8*leDg=Wr)@fH<_rlh6X<_o1`VZ5WTd+ z(qL^jARRF>`f2UaJ(<=T6HcHVONdnr1{0WPd>4K@Ogk8I|A}*US)_IVKl!MQ(SkPL zU{XhF9dLTQ_9xQ}BuQ(Yfh23qGmsRmc?OcDHP1k@wdNT}p0*S)17S_scbe7-ZQHIP zAA6dieZX5acAoYNWE#R*WkR&Yk4m(r2}u+(o7Qis*|fiwX(s~*QW2_LueHUuR%@T~ zVfNE{E$DqCjx+^Bp0UH+ zUyrobkUh#xu(oLBbc+4E)(y4O*ecP>S}ow)kci-AvRTKOSK3iFL^Zb$TF9mb1jvYE zKWafc0dprCKWoYNPamQrK@{LKm6%{WgAAfv-M?B#k+=08qJ_3mt6|PI;$Q5dc7O?t zaEiK13AA;Mf_#dW3FsCT4JJ_9m?<#0X%ys>BOHObo>6Krf%4{26;y#h@2Is<7p(P( zilVK|p-t3iKo3V-Lj-NK&G&pA73oFDaBo{>4v#7Xn#G~9wu+aGifUs=$Q=lbiYE;X zcMORli#8tO&$6g!dy+!sitI1@5cjyy4x$p4zKDHbB`dgc2+VhW){FaWQIco&KB<* z5z~>z_oE4n_1;M_>nYoCSxhE%+YM#RWa_rBqhc0ND-R^Z458^?Da_i-Y3#g>88Nfy z@g8|G{b;*NG{!Wi=g*qLexIBc6GzqZpB=Lm;$G6(j6ju5qZ*@a%VTUXc^n)-XfrAH zE{Oqc&}7EM1e3gt{}9KOF>8jbj@d)4{&Q2za;i?r&Y0cw_n3V#GMcVA8j}L~J`kyK zD4KXJrY+u56_ZH%4`zVhrI-rH1f0TQ0;i_&x${9x2g>c|XEC#>@=<@pw1mVP0p;dm z|2b)LuZuY%D5npH$0OhOF_3rk$sExi4e1_hyP^Gu7|3;IxovFJ;`y;c@Jmizj9&_3*IFYV&tf;U&L;NU|K7c00&^MCddYb!@KDFtFBYu& z$u6XnvXL@?HQW~;DFtl$0k`a2^M(ALZ(1m z^_IS|rI4u)IoXKr7~*V8FSmEcnhvpEwxoG*^PiHgu^|A9s7p9VXS}Ix#Ym#@aHQalnAs1S_rx{(eZT1)n`NJoYl5-8?3iY|l<36gLfu{gprJ zCXGG7XYa~mz3f;4J8kSyA3zWtMX?L;oT;%nE+*SsERIbB*p3O(R0l>zx+eBlN0yyj z89SmafEQ6R8o7GJ`rrxIVwc;S$o+LMwkhNukQojUI9qgKxQ`Iud>s3cbpZJB^Vk?Z ztE`PRLZaz`W84f>;QF;Y-c%QR&y5fsTAk;CSK7xlf~ZO0?(%9@d&cc$p@f(D#LZ~} zQR5HPg!xJTO*IOco#6BLlZ(7`6khzT+3=Mi_?Tnur;xer{BXMeeoQDI; zE}a>tab#KN(zv7K4NKx1e1Aopk0r~xmd7<`9>W9H#jWR$zTOyj$&MAcwlglAFEA3v z6)_$0&AoAseA#yg;-dHhx}$OALzP?=aVO%8{Lv4m<8t|<)6d0`<)~bN@(XcJLY6&z zHO^?yvXkz`)!6dcXK^k0Y~IVbJRZT}bzBvX@awy{349f!KE{2+mtAfdf2VN+0&g&y zgvgf({hP(V;vEza5Wfu4YndFx5D`HX;(3GO$!a4`fZ%W9r}7&AIzHZ+7r<5;e~&Lv zCy#HzXWNCxZ{V%IqmDnypSE2WZ*q-qO1#N6yVBxKuBpz9H}D9u-1yJDgfYhW5dP?; z;&_v5l+)rL@&zW(jL+Z;-1s(rH(%RI6klV?mdBqEFe_5m#2?|a!`H=c4RB*$+K>65=m+sj1fN-- zOvVmJq*_wzf?CY7a6(BB;~|gO=rkQCK8m-6gajEX+alLLjjd6cAhsF0_{zc=O@9*a zhTHuX-%=p>jK6svpCl9hmI+bpKJpJA;&)gPLE^r|1GacZCbUFsBS-)(<>u5NeAJeNg_TXh zKA@tAU?k#cwh8&5ITn?bAOxa&0|JHU>@BGue(0QV6f#*bX%dJhxFys&5wqyL98Ggg za7Te%61;GMPePKN;5dHSF+oYJ#*;cH~Boi-5_}=3L+>%ys9u^sXea~o-ik&*nzOTgc4d% zYyVi5Ag8K~U6pW^{`OguFrHeUP{~xW*u_+7v?l@4a+~)h*ib8X9!XdYd1k{|X=-83 zkk!S6C8+qdaWo=x&aaVkbwUOpLNCqHbwIwqB-o%anMP;yC12kW?xCzA{&9_W!gpRDp{Td5zBpk66EJUYnB^&`-MMdKw=Z}f= z#{KRjfaW6g^jxhI-O(gTTqr*AOM=>-(BrPk2h=3Agmg@Q2gWsT5-v6pEX0RxbYxq* zg?OHwu7ak69CYMn_ChRh(k*CB(vq$^axZuxj_IbGBP8kD0Nvo0B<&^Ek*5<2_egb> z*5v1oFdez4z7UU4>DJNT&o#OtTaqT~bk#H+oS`$?3qG;oQdp?_9w0Lp`YV^$I-oD! zdChR|BHeOGw?Twxj$2OAX$4N7>R%t}II%>~u@$bIsv}=De(0M&8j(T0J^Ff%4$w+5 zq1qVA4HX>F^+3|Wxn4MbuI>P^r8H+SW8cR+qf-lXz?Qs~j+usA$>m5wZb;@AvYt%K~uT+e3e8r`=IV{>}F?h4Ol>n0s! zE?Cdz^Jd+rhQv-BuH2?O%(E$|)IpYp)w8*-AgDN zOd`+El7-CS!wr8Q>Za$~_H?07mojKzLubSoXv%~Sav_`x-u0CIQXZ;0g5TAnAw zGj8exjW|p3vgbPTDj;nqIPP~Hc@>b$HmlK*Ra#v3_DkJoYnFZfPIt+QWvf2w$b}~cyCoihT)UYR>500osBMft^hrD>DV11$& z{yaDlv}WS(*eF|`=ZLMpNxaFkuNj>v;kA-XN?gJp{US;n#oHv6C4yFlbM=;`B+9r4 z!+zn3r7kRNZYCw>fLjCs{LY1WV?vSHbG}m&$?XBoi8rU3oj7B9qA6s?%}o5lnpmL`(B3LJv%lEgY*5w}%|?|2zJ*CvuLA9DpBuS-nl3&d|VAMI0-ILnR| zxPr~N>-Q#B@TY0_C+fPh0*`)AG{x!SSia4c@xF(4?rMLc-IEz<$ zoKwA&MWKz1cOOi?Ho!!j{Q+pVl7l1b_D1CvZj2MJActW(^6xCj_`a_(6jqq=YdJo=y*HnF9;Pvl6X3lQt zX|Dc=;4Qs$8BO-n)t3WiFx;g}Q$fz-QJ=A zv)kl*;kku+&^5S;0_4FHIk$*iN+eFxPqrhH9+{^<4N#K+&BEU-(02h0e3+a}?hw93 z)<|Coty{lD4|&#)IN}?Z>-Sp_0-BY2$P+FO#f3?*=Doqj7+fngi; zNdWbk0tuV-KT`suw&_7@w5j`dROmq;vEmfS*rDG?33U5jzX__q;@u5!!JQxUGbw@S z{d&lIUYrpJ59mQ_C%KaykLce}0)pfEAD{}PozPE!jM5x|+|&9Cl)$)ideE{T8Uwq| z>kk4h5ODbw{X2V7H+Mg6>s|dwP$XZ`r(yI!->I|UEk5p)+|QS!pSvW#5t8(E&t!5} zjU4Zte1WD5`y|`auI;;_SiX6(mw{%xPw5pR>%+-_IrgNDcbFDw%A+{1)e5 zWXF44O5RR?Kf0Vefa3YyO#X@fuKPLp3Qf=7W@*{o511qP8M02 zq;UEzc`8_ndd!}`Cu=G7Wv`hO*Xxp((cj5mm@h{OQtnby+FPb@zWrpCG8_=fIN2$M zJe}bip#+1b*ws4y%DP)!VTm0+P6tXAzTfBRE3V9~@7N^flSwxh<_6t+E zwtc50g*-rbi{~s(ArBDV;z=mwhbAPotw`aT!2Rth<2}jmE~ittzT?B0lo+alx;n*@ zbKG@?)9*$K*T>Y{Od-32zQsS>O8Lr$qz@mm-&=l3X+eLVe$L7-|DDx4wkG8OEpJns zLY95M#T6e?zK1*~;bb7qp*?mMq>AV)l^Z59?Ng1w!H(?vMc@#DBWvz%hYN9~bLtgm zLUBsVRBn_I`J{5vS++{$r2DaT>L4nK^;fCysSCbzNhQx<-r}oWQv;~!jsdC8^mz0B zspMhHTYPj#D(8Yl!%{iv#tcv8q-!)XHI`ML?&l0iY zeI==z=RwmS}Jac~;1MdE|{YmAnPRi`h9TRSynP3XBQx zOe)bC12b)DHZx&#E|bt9pGi1&3X^d7OeSHo*-XO83z>xBrK#6xv;>qf9Gle)XU`fY zQR+G-QU49>_-`ATM2j}3KBDy-cBKC3D5zx@hxX^fI-#A1QUOnl5V=BZiu8w5uK@b2 z+TT*ULMBQ};s# zMuIXNyIf5jWKEdR28(W9O?5}EtJ1viyql@LEXd)Lw^DOq>JCQ)+LlcfPTWr226;R~ z8P>xf&pWBDu*bdBb5?>{{NPdQJ%H7iV4enY8HxRWPyNn;R9;n=It;SD>c0(gLk_1> zl}NHP&AtB|)PUW;%F{ILM!~5iX}gI)C~rj?B(^4(rzPNhE7Kq^r($yQLmD2}e-=m!fO@9lSjF^ED0k&Z; z01Q*5zak?T?v;>EzI9iN{nFE&s8Iv*(-ov@yliUvaufz z*>JhZThm*!a#JePC$s-P*_qxG692Sfl({<{(ua_9yP?2@sKzLKV(wSCa!>k6Ati{W z9Za7F@JT)1OUndrTy`kE7ScQY1E$^4^aGqm=+*Ib$UcMr!1Fkn?gLbsSBtam^r`eT zMr*>^^t^_sMP89k_p{`LH(p3rHv|fC)G{uni&^yDu1LiL=Z8-Z(?Odb{R3vr z_^-HJ!pMv&_TRCA8AGkPW8aB0#;|_% zoh-wKlMFkDWe8cWoiYRT-5P2(zOT-B=E1DJla=8Q4mN_*jv2ZvH>0sLNBi)sjDcLg zi=7r^M6+X4S7qF4%KbMPXB096Eca!sXNK-OY&tgnSjJE6SkKcLSDEq6sxore8S5@) z9A*E_yk-*V%+DE{xGn(~-^~~VS=vto>4JS8Wy}yVK@R+yA%#o=DGnkyO;PwGTOpqN zD&rE9LH|CZgwbyNC8Gt)4YkVb#LUaH&720g66LD%cYWCy1xn(Y;knM41rD46=!sLN zJM#aS-W*T%$^>0~nX$3HMP_Hlcw4K?bY@SFj+uMevA?=x9%Mc9xvrVKGmCmzA3g$%`65u2@@gL@MLG^Wp)CRmEl1JnG;>O_OoK1iRbXeCZ4&=O*}7@ zWtIbY()nID^yOK4C;VxBrj}=%u_bd1kTDN7z9O?L>;2~JG12aEz(hO#n2GkmQ<cDR&z7SQ#VB=7P?pRwcOlP{&@vip11E-W;2LS9yH47gsN88pSvN^}q2um`=?wwI z1TY42Q`Tfy2jnNPY>u{hDeUo`7KYi4kllS>51iN4@SO#t_;q&!WG4Ez3T$z&9){z9 zz(aqBLFWyIrp&pUdl@c4niK)-fdcza8i1P*G^Fz3v1*9nu7E|#++hYi8C_`5xJV4+ zZCExzVdx}e*^O#LGd_Dc*6@+f-qjlp@d(>e42vLr)PI`_nbVq3hO%2`F4+dLEk_)s z7a1UzVJE| zbmwDm$~uC;oFv(3Sl58gattopWB`2{px&OCt%lP)o1xnq$Yyq>;X%U!*lm}Ad>e}k z;F8@2$h#EvmR;CmaBP^TjC&0$c{Y9r8W_O(gNBU_V{`C`VGYkF`M3e{xS(E}Jtqvu z8s@2%r_3GShBFQH)Wfre6AfcCuF6o#w6U-rh+3`A?S@p{4IZdpwIR4c4XpbmLwUp8 zX1Hn~pHJaJ+UlnU3N`qK;e5k{s{Glog=dp+r-30|ao6w;IAP&tww=(tF9uswWoPA% zM?Nt;c4afQA%7Z9H)I0X2@}{eR-EKG)+&p9?~I%L3=?LNrPw6f2zRl~`eMh94s4XQ zpD$q7IE!rJ%$0rQk#&&I?r55IfzQrwmKDmT9{6LctQ-8%U)p5-#Ana9%PQxy`5m$f z`I(ffU)Ct5Igatq`o@7(aiD+JIlkt(w7Edr2 z&`in-axvM?+?-q{<(%obaTrS=)Fc+;(Q=GzbwB#6CPd*#UDzc0a^Nq#JH=Be*QzWTA2~~}nByt^TGlY$@278PJ>e@p{2&Xo zfWqYAuU8!*PyJlbq4Uri%Wu16FXf#Q&^6nUFDvSu?aB+C-Y@$Q zpB+9p`wCC8b#wJ*B&>tV*B1Xo3okXWQWFdD(mUcDZtKwr7Jnm;Xd|!Vyce>v&7zR%HLl(@$BG z-HUhO51X=0UG}0a*=2lz5j(Oy_-y>1Y-58u3Q7pItROQN5r zbU6D-LuMl#&G?w$3;Ad(J(b;TWjVU41hYCQy9^Opb&1vmZe2`xCGprAA?A?Dt!COW+KK13(0z8M@zmq7Z+0 znf;r9+%&w@a$;lr{#CZjp8RN)BW#T7K4rUM!(Z89z)+<%ZC#9W-0`1+9MFdpczZ|V zlU6yu@(uK`Fy~v|{|bj3E53hCams;w{S}iM1^@#;1chI)5n4zh(W)v!q*6 zbM7@|jc57nocVm?8L%LSyw%8tp@lh1`0Uz6IXn1lRB28%-{wZGFqajsGMC+6W-gn& z+FUkhlew(h7IWEsTg_#2D{@ZoxRZCA%erE7*}XrQ%jWFMIlz}4ebijm=D4|RoUC%r?~mmJgl^{qv&sSVax<<{{5Q`aK5hbdrM=b8hiO|Cg$ z+U1%9rbDijcfbX|+?K!|kUd<$PZr4R;)#8%@6MO|xYqb2pO@LDQ^!Y;FQz6g1a>NDY2y z$b?+Lu5e}$>@zX92cwD9LAii?t@TiYgL4A`VQL0Igv-$X*pF?JEH?mfo8Z6kVsVZ< zS7L(xQDPWJ=JiCAq5q-D>pzLMHp_Hp6*Kb78lo z{(q=~re*(y=ZVRVV-u~&VD9IJd{*af{8!BOMBJuhDQj|HfV#WC)AUWxYsPjS8@Vm_ zp%c1%yNd@NxIY(xhNJ%n_TXpadsRtCa@RX?I^gyfa_wAMcF5h_KnIpB|26jq0n5fc z&sEs4?2T9EqvdaMdkWdnCm(VnU07CTpQp8E*^4fDcbo*zSsQrMGH>huE;c6Ne;v?S z(AaERG9Npzr|2Z|9dXN6d0hp9=XgTvyjDWNW=4zKX6$yyJZRG_^~ze4;-5ipVGPYaF*LjCf-{oljLtV{&H+&_;jQlK{B#KC!FGhi+& z7?w8!Fvgj2zhN6n6^+hQxT4>7g?ivOA$g!L{Fx-<5N$D8^ylx$I3A78o5Z| z@!9SAJaSRa8F3;lFO)CawJ>j;H9PuvS{`Y~T!D%id1MO@F6%SjeDv9Zyl5di8ong& zt7a^_Y)2m1*n}&av@7o-;|e?Wi`*7=+?m7dFQ0+TCVW!oiHQ`=|YwwpI z2(t*QGX8LSh3_UC7*^wa zJ6Drbf1Jt(eQDhchX|YvlWN`B{72N3nb(I-M`?w^dDGSe)Km+Z*l5Jw}YSSM$8{AU+q0zG|{hmq{jaR@hS zYWzrHJX#q0Ql%%gHr}Mazv^Hd3mI#uY51j|aT)<*@ie=a5%lRwhSbM++LF-OJHQxE zX}23@=<$t{jhs5R663d&erc$&F-^B9jT>khtTt|@se;TyM0qCuvNj z@x2F0BY!kz)8lu}8U1WX>UhOSc1w7NZ(pVD^&Osl-AHyZc!x*dF!rZ7Q|=nC*^)H$ zsj(I^+4(z78Zx`k+9%6dSf6`sJOFSBw*;F+G~^{p2#(_XI^$%aVEKPsE)M+C zu=Zv8=!6z+Ee~mg4rh`kSNT`G)#5b;5dbTxavbCln}WXF1tiM8lHiDD&DFW!BZ7jh z&{k&xt!fA<-_`}=+53|cyMo`X(7EA_+>pq*V1I)_xp&B_Tne^D+~Kp~qYFm3vZH%K3ib(DwlK8d*LEzMQ&2#@FwR-E zeQLpH{e{|>X1!M&WCrHrS0?cdU^=ARuQjh2tuE{3zwjvW4&9VFTVLwWb0elu!+L`y=+g-2sE-)A!POZ(P4#QY)edI z>%t3=hRh+5T{)f6vv!4$o5Tbl9+L~mJ63`^^hc+{Q-D~c81y2v3E82vuN~HRDO3P$ zVKCg)=XC$VA1nx`Aw3Eq+a#JfabRnD7DB!Oi2ja}6i~Q=QgZ57xUL~Gag-AJ7v6>R zf`n2s%AalY!(KxRGXRYq6NFuE#0;)+&lD9}*$V3L+_1u*ZAcocDa;`@;>S^iw@66f zqw$4g;!}qg=nBcirw$KED*TI<8>T!l*-`tH z*;(;xp(FkO{#vGIz#FEg`+HXY(Faz3;itkIRF8P8B2LfV!lLyK=!(cSU>y!fWfs_^ zGYj@)Fbh%*MFEugmi(frG%~vt7EPy-xpyinKXW=O-)CmgkJPd+^V#u>3)%7fMGU8Z zY0+_d-j`)XO2~*yLL!WAfb5LwHW%#@?59)W7Wnj*A}fF%_^+)+IjxcJq(C=3^J396 z0m~L$Dmv}Vvhw>yPlPNR^psf4vcEkqT4>3#-_{hpw6fT5g~|^J-O@fV~6avI`1=0*4Y15*MOTfmm*Wg`$bEi)khr6-bD& z1x@mp_UQk8=FSeY`;dE|nE&%DuiqpYa_-F8d+t5;TRz|Kr@J`jx#)QD$mLTFvGVe% z&wPPd{LS%Gt)61fzeT;0tv65ob@#NmX~tB*4W)0NzjNvfT}s83E5y{@(^B%Ssk^2> z|9Iup$I_pZJ6BD8q+|N+=?_l@qgeX;vB}iK%D1}j@u@f?O5cv#F!hacg|Gf_>L=ym zNzYCFp!M6Arh=3%)gAfT)OX9@x<8+~d*}4qGv1u~t3tWLTT>4hD9qDWr%K{Of0?@R zU+)RA{I65LSy-DK@wcgUyB1z3&1o_DYlSY!= z{-r+@y^edVuh5=s{9yOsLhtemqVvX0xae{*c75Yuao)8{2Z>*$82ouEq=0rK3H;te5~vF=dz8Bhy6P@NajD? zIL$5wnq}y^rTf0)8kXa`mf~rO61a-4>#-{auYGWOU3MiguFJH6$v^(yxTIUX?x|tq znu_h&+|KsBAP!^0R^mvvyiirdrWfZn=hjNRF4Ml2N46Qewi4N{7s-d4QI}JP zM;nlwRX6QPUeUFiF_k8buVxu({&6hFjOscwDQa5)hgZb&j<0w^_ClHo~8Pl zZK~YHbQI4}J>Sv|C$<%%e8=2cr8{0{PJ5+WeeC;&<`{Zx=%!)#p&y%;Z}b03peUMI zzGH5!(jAA5ns(dI3hyKj^lMrm?`(vwZmG6qsfO;yj;gCXS*%5o@5F9c-lKNgugNdu zR!;Y*(LHZEaAB&Ou%sipaQB>WHELGDDBXCs5)ilqw zT|H0&$Bx8iYeAo6@_?=9b*hV*@S$qDzM&Xi7&?~hkJ!|0+tPy|q2@h>qH6h`YWSYd zVleDb_qEs#Ej>0h)`{+jP0O14BqQ%@e!WnyWjF1JO9nTeRIg+=>xjX3HIEpkyJ~}S z^Ua6U5Al4>iwr}LTplx2V%M?*J7R@~ff0)_BNq2d>K|=Bx6>d!_7&YT6jd=j!*fDE zFbxJeh&|SHvia7g4TYj*TAJc2j?Y{*BH!{NH3%#t_LSIjH8<(?vjyu4#jF-p%NCpF zG%Ll-<`hM!KU|=Qza89MU-W~JwXb-rI2!ddUsDW&#|z?E3By<{@7dfsBo^IkI9?ot z4pYl?RNHkNE7F5VXR0Ps{@l#tg^ZGJ`jPL^F{;PLr6{`UG2)u1X?9YuRzAf`xi@N&~}C#G39}Azf(1q3#4>?1wfp z&Q+8sO2(YEaD1oS-F3rlO?{L4-DgcNOv)J?YY4O9gj3EQH~QqGkNMiToQWB?t)0_n z{*bYUh@($v>Q~HpE79wyrlI1Uw-)SLXKA+Us=DKNYGg1xF4M_kSFxkO_tj*>Lo+9K z8f4o}s3|s+jP1wr{Ww+?U5RZwc1%a8{;a;q$h(_vXJ+bp6e_wAg!HDVX})gqC`J%? zdaRggGPvJ@4+{0!Z7Pm<<15W47rA{#tDzWk;liO}*||--i|dbW-o5BE9la=U)xZr6 zwgWGUoKTNUximb}O4K3E45aJorWN>1!&o&!ox1EOR;*~6V#T4BZY&cXpYz>9F=qF7 zl~`v&n1;`JqdIC3o3^7cF~UeJ@4c{3^6HIE-!0T-H!U`dYEtWSS}yGvinwfa^T49V zzKfv9HlRg@W0+2)a@MI_cyVm$$wZGiS!Wq)V4J?DM5=D6D%*i?xT?c>XxX-w6n@gY zo{i6QG+XBgH)G4t{K!=eHHk#2bmstA&s$mEII2HH?jlIFnL}>3q3#CG<>GqFIiAg zWVsFxY1o14u*_ms4uYHJ1!l78h}9ec2J2Fdlt5>vFpxIsAJtZ@FtRKqP=kbB^{GOA zPK!u;a~5z0WHu-!a!!ah#y9U<3^{QkHDaUV(Nx3qZNX_bAesGm^9qi< zoTlYrR#dWEty*W>u4~@^5ViU+vpf|^NZ~OWA3Yke(pn8E37)T_pqBlYg1SR-}nnLr&@RWOGf+as4RJ95!V;usG3J z$^l<^94Vpb^=4(t=C!|R==%YoT;d2 zmYL+wv={w^ptF!Rd~e(trBF;@ThH9Q1;w3+nsMlcE6bw2{QY|BGJ0u zb}h11v-zSZhDeY;YKg`a)!E8jROi4cpY4%UXH?&g5UP>W9MAJq4=K|QJtl0VBy0XK z^PWPTs)mYfD;`RK5%`wthGyg-)@Xt21)AJUOQ+i}3&reKP2qgEc}y{doul zZSKMT!k+HgOg>s>r(oGcEPBLV)K8NWYgrQ#it9NT892>dosGb<93AU}&I%0h6 zp2HL?21_eaeBZQ@+d?)A7N1aGTiiF9{mf!H9eovhj$y`ZfU3_i?%SLcnu!Lj*iQLu zvn@moM+>dcWLse))kD*bF%@VSnj*Vuqo~hrS@T8Vy@hH{tCh2@xA^sw%|nNq4OZ1s z(~wk5S9dsEWA;DH1Bi|xS_RUiWocyrKgX6MkN4JfhQ%yvnA)9GX7S#I zd#3C3{!;wPcqvs(*|6v!mY9u_%)+tw_<@4SgHo)c?jxLdsX{QwbZyl}-p44Q7_n}+ z4%1Xehf$Q+NhbNt%dy`C91el2sjQi)mmWyWD5t@mi@iHb#m&3{rSnNTRZV^QVJEb(b`6vZOpmL1Me^mv; znY11x-uNx+u+MO2W7I^6p)Z)7o~E)&9gGbMmd{X0CVsE^Np}70CdKkyXCX9ZwkYNt zu}~97{G$1QBArh6=vtsSm|1DnwM}VUjZq6RVU{&AJ=ScEHRal4q2? zlw{6oG_pG6M1}n#RAVbLP%jqzhd^A1#-Vktsg};4pp;NmV=6^$_P9clQi?xiy3TBO9roy z)*n?@JkQ5Y>SD`?QONv28W9ytdK`O}=yd_7(mPMDx}z{CP)*0um<6~T3p0@|&-FMJ0vpqUiK2uU zCSSNDUDf9_>!p)4r`c3Jd-AW?J}k_FzOGoFw1T>>=A)EFMjQt^N3UU~5 zHNSe@<&2oub|s;aXH3)BXi;Ls zng4T4{pSkh>RH_ruiNzFJMEtrWia>REA|qvEM7J+`CZTH2XqkMo}mvC%T_$tD;a<8 z+eoKredT$5rhk#_QbcC;7n8d!-K}(&rexrBJ~Q~-o+JbBn5q@tO%5AL&19#sisYwgr_9S*DeBptHr>9!O>cT$e0en83Y*E zF+OhTLl#tCqQrzn-cHT>b=fsS<1V>HuGBE zFU5Fw7xR8Kb1orCGcw!+h}#6bwVvh#ss!6m*VG)B7XfPQ zGei>D*9PM4;;2^@1|kMb8)|+SIeg(*yc{_GD(q%`cGD=n65mqHZdziz@d~ckI`e?x zrqTu<4f4(=>c-Z9qym)kZ6{!SVz3RM)4ei=3YZ;kWX;6q85)3VC2-%?Rj?kB3jj+= z)OWTlElkQBqNtq>C(NFkc3W3qoZVp(u30aBt}f_X#5tbzMkRc7(Ft?_Jr-CF83v;O z`|^oFBJ{^leBL4%_2>d-rv>&%1==4mSRtS+fOSNq#Ws-!p$+0$#_puvcYsQs2DAeI zw&AhjaD2P67jbr)u24<@`cwA4XE$t%8~!-+v?B1%%$5NaD`MY2&D^61x;`M-ftdt~ zz}3Os0JitQaJ$6K`If|oOaSDq0R-?}hcG$`X#+(d7ozG%I;far^Iuj0*tfti^7o*O zodCGM>VmeSL%@{S8t{{9$W>0GQfQPbEUVEt^}O$S%FLlffZUD=7M3VAT-1bHseoH; zwg8P_gov#ooFxGSJe6wtPGqs+0S)sl4FD0*eav1ZGOAY>YlsLXn<`-c@X3QJi2>OG zcmY47#0ii`;N3aR8mYfMr`giVvc!R%XAK*!TGaqeARD&gs=jNeHXv7%hj76kS@gfH zhNTUVw=zvni+~!10bvq6E8!}@1T;EviUwCbxWyP=r!T@IiW;iyFlD+Ju7ghMK-oCYzt zEzWWPj!_xuLJ&%*hJ?d#2aazJ3o8IJ2RKA=BF{=Vnwg`SZJL2y+WZG)HYg_Cyiyah zi?fCmiTz-Aa|jD3Dwgm$P_hA_D~(Vo;OfxOkZB|0Li#ZLZP}5Q@-HHQe4LbxxO%5zGkK$21AL^MrRJ z7}YeCE@yDsolQ9tvt3valRT@>EVili%=?CcKn&<+T|08Gy| zZ-hj_u*=xM!BUK1(PWW0rs7&M=72JNpqLgoEKYqHSDhl9OTl!y8QQT9;9mm*P0=B2 zjm1kC0N^vCf`~t94fV-sTNgJvvrfxx8?jLpb?&U5?1wH0IHKunPa}N5VA+6+lM$wf zT5dUBt7zGrQp9sv^63E)5}ZKrPRNNsbdc>P>>32fFfK$_f|)kiWDH<+o)1PD46Di^ z=#}NM6sn)otORXpb=i$p!UDUCzDLcfFKU)2;4iTTWMN_jq&g7@C|K!NCF@VA^2GUh69ISfIB`kdP~w~hS- zj4_)=PRpR7WB@L^Om?d!3HXGKy~WV4&N^T?k=|8yQX&)OF69H$W5H1j2*wH}wl4%` zAEaVOUBLdQt3VYcs80tI&WO5CgrhV69(s^d?AD(f`_TBQs+meX$Gznnii{zfD;!P((o0c zfjnY82fr`Fc1lYv(3Qv}00>N;M1c}+&R+$Qf;t9{*sNY^IZYelZ|BS!Q_OB!5fd-r zfG!2EE;@Hsk0N22RtRF$M~pOxKlT_=%R>S4WW1mwk$z=RU>%Y^N(u`~;yMwgTt-$Q zrqV;~RGnl^@2wjObwtD4nhV08Sjku?9fYdJ%=v)O{@7`atPHehk3#VE* zYe*5ODZp41&6v}^h~5=Ohg5=++49NOSCXin7% zv&sKpAqvvCRB}ZoET= z1oW9TKJ9=iIg_*9I*YTrEs*Q16eFJZYJ*2HMOPoc((X8KXtxq%vt}Qu*+**jkv6uw zb~0$qK2mL&so6(r_L24mteSm98q;g`k@g0enth~ZACa+kHTy`-K2oyG)a)Z|j7l~8 zNX+GW=4^ZrpJ@mdfA zq9EHJIaokPM7X9wzL2Pdq!50q!ve=GI1BO_5HT(t!ZKY1K0q?>ICy1~G{Gj8NPrls zD#O7s$dZN5CRe9|LEO_xGLYK!QmHqmSzY|wo7v5l(&ddfNY%C0ykxlV$Q49x zR5B(>hcA6Xju0D!ih`qBLJ7u`T@wdA&Ps9~67tTwl2gV4Lm{npMwG0w7L#3*EM;gF`&4?~F95u#q0E&dB~8aj9nNqfYAF&$iAAw#N0k__q8B*g^3;i|C>Y`4IS zNIfbMBRo5az(`q;x;)b8{hU_Ipmf}LS*@lRf(c)5?ktwH%-O$-Mkd(?jMlG;Mg5DG zMuI}=TF3YdR0=yi8MAx|%(3vlyVVBNoEe_Zp*=S>%_vOD8KZV-Gjk^Ae?iP;cArx* zpys_=s{!?BaomG*POL4r+H$KcH_n3p>E&jpu+ytvopVZ;!r#-x{v{ykC%>4Z{d;Hg z`TPEHVAHJ4bNJkq%-VeS+n3gpE{EJ$96KI9oro;Pf%LS+QG}bCOe}gid$xMYT(W29 zRZg>KUvui*mHAaG*|U>xoHqB7PW$KGd+x=3PEG-~r<^tS;|}7eHNA?-`RC3Z_V0Dc zTmIbL1&G?7$@Xxr=@tR*IS%MNgH-SQhb-;nAtN~^SOMI3zVNosot|Gw=4S77&)l_S zCs)Z9Y;gk1d@!Vh!pm#mWw*$QhA$O0wNEU#AS zj%zo}{j^)XXM+xkT+(9!KCl7ml1o*0@Js^{1>IAA>)cwUJI;A&?&=}+A(=UD-u)fx6;>or9B2Z-sdyT>cmYsY9+@b~_m6~`j!bT? z(p&cr=N-9cJx~JjK!>ISM3O9(u$@TqHWYOtIsK^A{Z}K5VA= zj4s|7EyjON@kdj{!}^8>y; z=w)H_6FuJ*MKL?NyQ?_=H+S||?z^I|`1^0fUSh_>YZ}DrcSRrZ`ny~{ziQ2J zal>!9{L=4RFJJmCmH*?nt(W)zp34)rrI-7P*)88XAepgEoLBgtZfSyd&(LJA_e66? zamH(qY;^d87$DR?@HCVEL+mE*{#Xp0{}ERsKH}=>p|ksnKX}VKi)DY{ZO)(faHkHM zB{uB-a3Ar`S5NCCwtLHW-4#_-GsUcrM1OJDgZDsqSo+d7bwv!{&X>L4ytjOp%YU|} zi>UL~br;(|;?@7RJV0-8)^_o{IQ_mAdyA((5&Qfvd-*4Rx2CUn^-Fj65O;hc?EgIv zFjyS&C$ac{ZO5N{#htr~H@2_o+3AGBF1v`6Uh7-;ziif(+CKlUYkDLTKNX*K5kINAgfGolr)QqZ6wUc@Im$4PgWV8!jOb zc4Tw*bp#|*(MyoK;$S37CLXf-9t=e6kRZ0P3lY@j+a&PQfU!wJ835cS38l+PiXoH9 zY1R~Ob6f#;_5h!I+E1~WTA!?M@Gf?1bz`E#aj_IYPf+M)xDGLf0?-wyeg@Rb@H-e!sVuH zEd()=HeL$8GK?=>bGc2+SdF|!iA>Psit8nop0KEI(StXHg~qlPFi|m(BQx@XWPJsa zuU7LS6baV?&QGFPvY)ekKm%qu%w~<0>qL#(>EcU4x+Jegykn)RsQhdm#4120L|`6h zUE3#pb%p(+KIZ{S{$3&|Gn+0i!J@M0z(cV7SFb?g2rYJB)Dc9_h=jdFc%i68E+I1{ z2ftLtxn`RQ1PPj37hldWwOf2SGc{<7gwN@-9h?^|r;K-kH%}sTVR7m_h$umv=92V+ zQ9%77{Wx)aw)Dp+#yoF&D+z&3@B@L7CdZnLjARZ%W+5Z^VqJ?+Axbo8DU*|=!*d)R zyFG!v&~z#IL=>o@h#tyX$#x6Lu2*FBb+a<8z{E>@#7Kv>kCoYW< zq`+*jAX7m#gqaJb79#k{*}{oUgOUj<7PzQbIs{;_U0^UV3BP9hO&^d+$_ar#h0OLq zt~vWn0*|B{f}H3IY4OQzUlmfB*K{dsczsUGGWTz@vSZ~zu|=$w%&v`f3D!fIM;dko z=_0c*OO!?@JsSYH<8XpzFodO!I4U*<1PfRR9ZBYrW{*ynol59N7zMboBx@PDyJa3^ z0{q~iU?Af(0wt$mQBOo#T~3>u2Q4adr~P{Tf?;6e+oa^@3^&>2nLkW30!rv-VEQoI zNn!`$Pf9tba}91uO6!DyCTp150S6&g$eN$+lK1EIR|#B^5~pRqk(j(;5z!|^g`k3C zeIz;~km<9bO(cp-Mtg;pm--rugaqT6HF87s!WMk;PUzzDkRY=|NOJ z9!E*9r!nxl-*k}cUTPwi~YYT!zV!(79 zvN(pZ4Y4PCHaW)0h8`r|gIf?Rb6PD6mf5Z9l}MWI;>hU>4l2TWV@u{u=o4W~P+^T{a)#hke_)w=9fQ*SWAmBU8B8);Y5z$uA@?7-=Us*)e=6NQtE3;N=N&BVwyHuCgA9hLjY#hh~NekpOkV zz*&%tKNEtOuVnL=nwH9NA$DG9o(PJjJ|blVlL=-oD-KZMd;xt~D>^r)U0n=*e$gq# zoOaWI+4ava>Ru$tJ5$Jl1C02>Ql<&SF_s)mpE!!*bl0dSiW%!0q;&+vvUHfZazn+( zAFFU)s7&C8XTmTePY06xLog$Q6A4_7AlGB%q7IUWAU*ubF8=Ih)7a&@>{ipam^5pV z1}h_0NFvGLw}Cl8k-^`@+#%~@1cuQgl?~J549-HHAC^HEAOpYSX}|>p1)*j7u)Cdd z%p&p~kbj^5IfUQAxI?6qq%|eS1MVFT^cbN~a^<9NlGAhcsbv)5F&zBp0~-I(@Fg7pb|1jYj>{^|&4CQ6sA7&>+m_J2Su8sH!lr+|Dv$@xV| zx0};mN;Ad@nAKiNfW*1nT}+;}sFyfo&7vYqHtYv3f%%S%y#iVV>ovZBkk(Ocj50Dv zk5$wK+yj`cWRe^ifNA?=#z9IX9y>wW;7u4uPBYktiDD(o3@9TGewBfaun*~yAF4Hd zPfn|4WKd44Df!H@6E^k{hp%09U}w@GHCT4K)>z;gvO`E%Hnh_yjJS+7xY|fR5-4z` z?BFV_%l31MiTc7;wJm$3jCN~?36eES+u2OA`%c@Dkw5Rvc2K9GE6cPtr1?RpLI*>a zM(_t|2N4fiSy|JuWT3AFF}IY(aILfD!tJEW@OcDqt6FQL8!^*ken34SG97Q65APZVt%=&#*8F>2wq<>>Y4dk>E-uX(*=)C>?J_9AzB5 zEF`&U;Hh*Ww+kVm!4M=90<_AI5OpyIFrdm&ggggp7biRf++4PWvudN_0! z#*D)mQf!ArRB|^kkeCzFdGR8+FYq;!4G7{t(v;}DEct%mcS4s78Qp&@B9UxPd&tW& z6-Rb^D7cbv9Lce_xO4F0!Nc_i+o@8~Ak*T&Wfo~bgXviw1fiJZC!{C}!esKyEfYyG zk_ksih36FEipr8TLhWS7oJraK+{^5tV;TSw{>>O`cEM;;?TvE+IDqu-=Yb97~HXzh_5pS_-%Scp_=aJF%HAyfkgB*z;$ zE7lbTWi}`#_g$%Rs%17OA=ii1(NVA?g6cN0)ALA@FI8Yow+qhAb0>Rc-0W8Itz4`J5@( z_E^j5HCryEtH;7fnDbWYN(u0uJ!kwK7CJXR^CYAr0MZ7*F@d2N^Sj=pus z!mdS`t_thCwEeLWC_2Uof)xVK!>$PncUj&*Rw9JNl5Eesu_P;$kUA458-{vJJ0-?X z(4w%~VsONBhQNqoL)I*|60H4tgo`_k3rI}_In74W45tmsY*czxR5AI6g}bBZ8<=x( z3gg%!YmLVlsX-5@%DkwU5=rVvz$!T?ap<5vNwpcDJ4z(7w}~&^lNn*sqqO9Q*w?bj z`AA|}!cwFV0pqTXcLN(M)B;8{${J%5ui29p*b{VzyJf{H%Th zCM#E2niVng#W4MpIpTLM5*kE)UnvE8CVM}@PbHfwFpQWD)lTiHbyyRnYRY=S#YFm8 zE=?-pUxd*a4e5?WtwNy{aZ35NLYhwLShG1D2t5L4i(Ey9+Vo&g4fr`ttLaM1ZkpUx zqewkkT4|X3@cS__F~^g16sIeGWXL<=pCz?m*=AM5ViN-q(Iq7`a~T^OvI$TBvCU+_$5g%Aa+>C@h*hw|XEt4SmzCr~Q`$mZ%o_?> zZPBVc4j<4Wq)g^ZrK}Iv#Zst3RSe0rEYYHb6v@<;q;X_ZAeWO&>oq~u8) zc-;(yea9pznvSyyj(J6W%vveUHrY*nUH%^mCaHKx%F3)!YPa5Urf2)SZkgStmejBP&nHt` z=6i*uOl@_ft7PpURG_GkQyGmpmM&dJ0OKf(YqII;&xQQFFa*%Q-fmwq&-?{)+kX1Bb!+Y3DKYV}2*%!IDl?nO@c|@X(MD@u(_r;TikzqS?pSCUY$Xqnjdv20-zCDkLCJ0wpB36a`b*#^h* zB%eC$>byHfW|<5c6% zJZ198!X^WjEp&;eHr-@`%nA-AH^YUtit5NIC6SQ0m)ZSrFOc`QRkK8CjyU9i50b}P zm4cZb0B^z{9|Ww;q?8R20De}3MysJCyTvq~SNVL=fg|h&L#aB+m=H)nyozU{OJXo0 zH@OqQqmAi^_~KTKUrxw?^qaS?v7Di4cQOI!%$k$!YUgy>4v=HcYgO^Z6aUUslCkOR z(~Kl@5^r?qNWBB&Z>kcYUtmf^g+=X=#%t*p#g4`Q0+a)>eFpA|^+(OTgJVF$3XiUi zu>of^_GQ#F36mhy9AzoZILi{lTA$r+*%zJJu4AS<==R?&=wBooFV+AO#^BjQT!yFwWv76ofF-S9nlpN4_wd+YV=H%=qDyFurs`E zxG@kQnG)DOG2YT%Z|^hH}4C#H1S*b_b4v_7`%5u^>2iNR^5^ONN<{ z<=2m^1IqybqC=1uH;}0lu-N0xQ*=`rzfDq>C*F};u_W^FSafhbvmttrE2zL$rSIKB zdNW(!!N_S<=1eW-v?qmkTi|`}dz9fGaOqWtDaxUWa>#jCYoM4(w{J99)hAm()&~x; zfVAsaNKr7^i~)j3OdgM#a3F(h%#NuMm_b`zWI1EiE}5~K13y{VzXs#4!T8hjtp?+7<5sJ|_;(gUP=oQO3Uv*}KdA=e&xT2-*6GwR zS%dMn2_UG!_-inJQikP1>1#0l?BIbKjK2os->~Q)JWw?le+|Zef1$nx;~#RuDQAxx zee%)Ad~I9}#$O#hP=oR30P1TnegZCPF#Z~he`k>cH5h*l#$SW+*I@jaVFNW7f4j(p z8jQaN<8Ko;P=oQe3x3$)wyweWYcT#o4aVPUyr{wW|7iq44aQ%C@&A)_)-@P^4aVO- zc%TO3A3Nmco540^!rf~y{u+$G2IB_;p9cNp1P;_-{B5HXYB2s9jK2os-)W@D|2T|4 zdHbh}kMCMoDA~A<6XV}n{FtgUps zM&zugh~NcZPbe%*K6rca_d5&y?R^G`63f?X+ZONp?@(HGZ8~!M zVm{wYj@-WF?T+>2pT{Yy=q?G?NLNL^ByxFJM0Q(b+8_y1nc!>7pO--Jl~*|>_!{296BV8XAFOTMPdq38Iq<5b{R=mX zIWJ7>D;|8j(fXJEQ1m+PvA*K2tCoHsZd%`Xh}ig4<37TAs<9}%^_0$ds&T0J^VLiD z5T{?Wv?w}VvviR7*1E>Nh4aM3^XBa*j$hY!&cFJHo9q?)i9@bi`mnhC>Ba-aXV)#= zTg=&b~mCJf1TKu|6m0wihCT}g39oG@8J)$&C9?CxbuwA{**=PkQ9 zzna)F$smuK+$sOQFDzd6mkxz{tLE0nOPAIEI}2_%F}eFEdn6OSv+QqY7RYUJ@||_! zow;{)nKyg+=T+CelMRLCuN8R8RU?zrIxHVaQI*AAmtR>(>kSgK3rSD$N$2Gsw0^Pv z)>S={uDdP&+pfJ$=kO6%g-3L&Fa76min^HcyKh_KYdx3mFNR$I_+H7EdoQ0*5VN1U ztw-|t9?REt_;3Er*j;;?PI_DZB?FY$W61Ks9bn|}1}RA?wheq6ehj@ALa=rb{+SEGa`J_kF z>qC|&uMhdwg{oNj*0+Ww7o7gBku$_KpRO3z^zvKZN?v~J+rKizZqrr_OJ3=H?`<80 zxRGCO=zni=L;sea51+)wttsQK7=NuYE*O8Qa!CDtr;ls>Xja`O+EUi^kut>rRQ!_as0KV@7mQf<@>d(SNg79 zJ!>cRO5aUtSJB={#nN}}>Nz{9SNg79J$EPdO5c_1O=_K*YH4n^dzJLdcDGbZ6Q_O6 z(wFUP?rdDtHUo2Md1a{ob!Zs=oehn;v$09PY~OtQ(GjCgXgQ`l`R1H+TOV=H2`z8| z$}SMMOlmp4TpV#?%U8?ANhh|9E*CeQ*m8Qgc<@(S&Mg;be6{7wa&g;NTaGOk)vvXj zS}rz!t>xNsaoMev3MY?l`EvQ&IXjBur&hjQL$TYAJwlASqotovPHNdzTzgW>pstl) z#FS+%{lzJ_wG64QJ;!gY{r8hv23G&_YUP(hPj0CzKf>6PTh1sKlanjYxU{kIH19OF zj4W4}eoD)c<>E`Hv>dbJ-fH@cBd76<_L!C-pk8LjsaubfetM-ScJa^#M8z&at{ z;hj5p+-ttxGPL}s%f8-nRCT2_XSY`Ba%Rh*>R(DD$)6Xizbp-6>7LDJwiL_veBjJV zKXyK=@{k|RtURPLtL2CtcQM4QQ)qPZ**w{&XI0;yR7LWcOTQd;cIBB1ceWhW+SJ*V zRq@%`mG1lUIh8S(a!%!0-Z-b_xE-y+%O&q!KZjP2JGc5(`9%&azwBRb<-v0+_xt$V z>M=FbF)iKX-1919T6b6FsV3Z2d8$=+RgZ@$)|}be{=Y|B^55Ae?2 z)qiTG9d_j0$^*Xmjq0be(x)2t4PMO}S9z+j<0?1J{nhfx0mNt`s$AJtLH!2 z=w)A>FXRZF-?Cr%sfu1@u6;JYdKzIjFV{b~T=h=T_V>~>E8XuLzh!v2(j9)~sb26a zPqjx-d8&(oO6Q!iu+lmI2r65`sIc;9gBMjc*jK{p#vM^=d|4=aCu$kKV@f!pG{;N7 zG)L9D6^ZEOZdLl_*{FJoayL1QrC)ZBE5o>Xapg&8$1Pvk(T1B&u01cL^}oa|_2pL| zbV2Ksp1-8>+z(a06myoejIO>(=|M|3x!}U;2X)g2E&a0M!s-Wg(+4g6^4^7&2VK9k za@b6~sCp8qLY*r2^GjMfi(g+aW2=C5Ada@_8f?O#>WSy%dlA6#6Sb?;o< za&q|(UR~C5c)8eXdCRCB@1qLurLE64|64mc<y7b;og=}<){4a<&`6~%lOu3flRsb zFiXc*rrxBMO2-^Bp-sn>H-2SB%Yo(lZkSMMc+HBI!>gWD6O)@-`p=)hJ6t!hvY~!+ zV&yshGO^`^>W-Up1|4_jcX+W2uc$1{*;lk2RdrKc?ic;V?C(_ei$PabZ-uHDc}MGA zZojh9Jty7Qa&nuyjN;McU3SHe&@!;iPo<~6zjFVXS5+SP_gA%?P*v4Px01=<<%!R} zy7I(RuC9#gkFT!uf6r^$bfl5au+rb&bWP`YT=qbQX1g#W50)HJwO6{@De)igLo)HJwOlZL2iaJ!0{23HZ5YZ{y)Y8qTW zQPbdR8eC0-6aO!1aHl5ct!}xjV>dU_NW%#go+4iT)$MzW4<2q&tG~Qw?HvQek&m?e zu4|>HSo0_bjtMKL^$^NOw+$DcKhiR+?XN$nx}5n~i?ic0dHJ!HlMfZo#&`yg{1#O*?eAahGoY&x#{wo%Jp}#s=tY6>QU7Rs+#lqIVmv7iy5bO3{QJ4Rk zj@Z8Miuy11r^#N$6d*;6%4?Hw+mHnGK8zqxY>-hFLIVZvi5KTK=hyPOOdF8w zf5eJ!A1JhmjM_)>ile)UK_9maOU|CS;)rhIJH786mOORiiW9qu)A~~!F>S?h6UE5W z@d_{bbj3Hi7lHARB2|&>Q6P2<*t6htf?u9#r;`M|LjHDcry@kjGQU4*R7ub+`O-*! z3l$BV^fu%PBv{@-K~8pR7^UH5RUuU%r%%e`;TR4K15OQD} zP9zXZy%+XLRxelx2RS*{A!z{I1ml%O_FcGzY#6T~GSEx|rW47CT+C@U<&Y%VZFZyk zK~q)Ykd&MX^$ujFklIMbTS%42;H#KWT#^bKModVLm1I=iB69umD3DdbgGusR9byQ0 zXUK(Z`)~+akq_E}GflT)85R(7?VZeiym>{TnA2$b zB2JvyXqxI5I^^BNk>Sey8eFSEH>#3R0}6v=35LLceCIj|k|p_+Z^8=93=GQ2wR4t; z@NSg+1G~?fUYI1);%5v|J4U;l$=Qy=B)jKqT~e5|jF{E0F8ig#DHkq0q%I=2G}AJS z;s8Jzd^+$WkjY%q5yUD9fhD0`(J?^OnehEV{t59t>^ZRh!E_=S2jEmonIO|d?~-02 zfTtW%IfzmN-%?nOk{gt7+;Yt_cS&~BMroPV=QORRncn3(D>@LfLz@VYE`;sFB*ip@ z%%s(3Gn3TgWr`h07}*M-wA9F^ZYj`0k{;fUU_~VxNT4L-?1#w%js?k8KwfJ|yhu_m z$$Ozbg2y(9q4G+Wf4TtD@thXR(CVBPQ!2R18k6iQJ`_y4rormyn5=J)6!|6@=3!be zyfSkWTm)fAmTw!zKSaCd zClo{KMXGm*n?@ZA%SFl$k<+v%q2g*?PRoOzfJC6giF`!3a{m#U(_raUD-9T?O#Ka` zjbwF$N6_%u*h#uiI(wKotG4%gmSITOD)%j)%yg}Z-^z8J zzF-i6Ac&w74)c|JHm!YVC78kedvL&53h$b#L}Qij3nQMk7lVxF!@4E zVnarY@+WcdojVqDs)OexivS;?KeQo+^?})QrP^!b)ELlxpOr4;@96v z11*x@KCrbQHave`Irikozk8t57ly)Ls(jKl8T9c3GoGl2@(wB^h^sX?X5a&L;RAxG z#X!=BPcueKmWq9Dt&(Ej^w5Jh?p}|QYQgm=DKlYKLY$Nw5H0Abj$6%8U*OdXz+a=)Xu zWVV@U5i-GDv z%M1sp6x1afsbuWIEaB?JJt;oKQoKqXJ&jHT`Bwx*t>Rg}!)rlu8bc&zK<6rHw(GJR z6m$Bm*6OmG6Yre3^#HN#gpGZ~HNRT5AB>HT>NZ$vl`;bVIh34kY z6bR>CjbJ%eour?Ktx=L%s=ilN{ewucErv88a1QO0HDsZmI&g;t7=EBo8q!=|YjvJv z?S=x$wjn6giNnKgWx*Q(#h2b|f}{o+?Y>6hGe>tlQN@9<(LvHYpdy@kE_v3^I{qw4I{xhYBi|A~`RQZBl8Yby!Ext^ zH-DN8OJ47>wu4^pY7m|(E@Cyjt&)0PkA3Kg@v-{&=DgO-5KE6*OTd0^<@%%760o0F zttw#ug=5w}&}nGiAI!aYsM}S%x_H^%$)hK(y{dcRyRvJK6BEx|yW)#lamJms_L{9JWfm>x_(&GkI#83`SCgHM)efNH`WhJl#A9a87!8c^VG29Pczna>n+ax^SWWl z(535?-r}Vh6mMFyZcd%}NiXUeudW-?Tl9F9Z*ShZ?yX+pj5QQ*{qwpp`--JkP~mUA z)<4o!e71ydf3}Y-UUVVft{%GnHb;DYE8m{`jrBddiHG*#+u0Y&V%LlK_J>!j-*30V zk9NFv((lIgJ-^5tkojVgOK)4>-R;tGm%=XmN4#^`ZC3K=lj}Dq;>~d7(fT6> zs5>}#>s19#UERhUWI1Bg)AI%vE)*YrwEpOSUTMnk&3g;)59{v|x2$_`xTxR0{>Z-S z;VGqW+uKcfX5N8f*!t{B8@kUQCXV`e{j|baV#Fuwk2q+j_bS;#=3d;d%PzZ&oqO^A z{NEjE^_@4JO!;K}eM7`MD>e*E#`N8=byyLmUi{QHCK8#GLUIcfWbV)q9KS0mYT` zH@JI>-`qs8%gPPc^y@xvZlN?XyvBja^d~p;IKCc6df-ej*Q56ukZZdt_6?wE5WxFx zU~1(RdQO*(tkAs5=?cB4>&BJ&RpkmzZtk{mH* zsCc~3>okbI;{gF&*_bZC{#bF~!}qOY2@N1JK5#+b)UH_vB{*4)K+HH;W&9Wj2S5Q3 zHi1e*p7?kMKK<0JnbW!@{?U8m=|wHG>(Bo+BmqV1mG;BAp-Od z&3nG%#6Y!F?8}(Jf~0Wt)`acbwo&7PlT)Yin3 z(`z0mi+a)W-pu}kvfe6^oGnA=H*B0$TXMA}S6gyzj)ecoB`3f&*~!Q;8*l40H17{KRq zU-64}Mbm_*pW*XEK0o5~ET13qd5+KXd|u%5BA=J|yv*knKCklG#OF0WKjHIJK0o90 zb3VV|^E#hj^7$2?H~75C=huAR;`4Sg;punGsNPgGJahnz@x9Q+rf=fEjBzR=E8w4T zg|~g~^kQx$iA!%hXfy6>AIFrf1|X&ZTH7(+FYuL~qS#=5Ir;G)ONV%Ft&+U@j=6bJ zojC21&BK!Ar*FQgqgb-{7D5%y+Wg$^V%`Z9jZl8;jHdXD3uN)6y(r#r$>uBfsrMYs z(Ltd4x-0$IDE%fLUt~hVa2+JZ(t`}jtR>!FH*cThrKZhOW{5XV;m%j@wdKGr;+E4X zKC}0h5BI8F9esT;gjvFnDJwm?iFh&+{IGtk4>04Sl7B!S1@b?}_V#iPb9f!b; zJ%Fwt!*R4FQ)X_#ycZ+$BF-|07%NeJfU_ z9Is2KJzjm^LWI>!C5~KO!;3g)@YV^P>LLYCn}p)M|(qRHx9FLod z6Hclq7BY|#NxVEIgAM?(3&!(;?HP39uv~FoemLv$M#_)@pCf8K!nQZ}X;RTj@8a_oVbIN)E=Hw~}t; zq2isl7Idz&QOcN{GTy>q0{VvZ4x6ZBIFX}P|EdF!+II0TJ1iN3e>}sA@sxumfx^j% z(@}fp=~Z_W>ewBPP-CgtOl5W_=DUf~XQD@2I!^9VoQ*`$6bbdnYFeykwGl>=*|L{{ z`b$d<&#oJ?g3x0eQu?Aq5)BkODjFm?O`|d*2!I#(CR0WmfREuUAf-dBhLLoq?8Q_x^l4i_+XntjxR#|B|uT_=(-BLgd5f-EC4nrvsT&y6L8}DvMN|eG*>QrDE5%>)!lG~yT z{33dWGmV4THBiS#5jd0r)<8nZ@U@sxw;=XVJtCrrqftVlKv;WLX;-A-8gDjr7tgKO(xd3BY~j+B!!czzSe`f?SX1Cmh}Vj2!My5|+IQ+B7pWJs z0{MkY#uFBuOQ2dvz@Vp?F=Kyb(^CYiaR?G96Y8K?49*eWo?Drw7Q;C}pvtcowg!6T zwLSRem1;4kRXH?@m~_L!?sYbJugEb&U|k$VZ2qwY@Y5tMI8ew|@STkM@j=b8uS5jU zg`gRA3%rs_7?DXh(553+v)VL;b30%hOb%s_5J@aQI-5pFc$i)R-vLt0YB61HISp2b z7P^QZe1FT{U#YSupLaDVBwIC!zET?0>gY9Hb2T11I!+6PDIhExTDUc+^T&>^XMp5k+8?M7V+m zUWq2kaVb8+sEhll+MpAUK3rjf>*%28S^r&M+A?F@Ckw)}rvc{5ngk`xaL@k8KSCp$!)u|k>s?R5LE%P<3PciPN!J|2&)(*{P&}A7DR%?4;=JaAk*sRNbHBmom zzUlO}mr8nVNRD6Q#1I-Tl9&-Ib!bJ9U`XJ9*`@t+#e5e6Px@ zKI!VM2YgXia`NFVTR%FwgSo3%zSoL=ovlMn?NCE}`MIs(K$Aq^ngQg|)FS0jL?z|W zL$T(Q^i8aNZtGzkOjS&IV{0NFdVcF34Q7K;LOn^otcxzMR~oeck`PYb*{DBI))cdT zzV-9g#{m8tT^Jet^R*+hCDc6 z#h{(c5T57f|LVMW`IlQy7O#Hk?jE(?V3p7tn+FWub(dX+4{w|1@n799p8hvy!~c(4 z#bM(7x3_lxSLX%$#WA&>VCQUl{x@6s{3dz+H^04WQsHqiHr)2J!taF|ZyO@!e|y_N zF+SQhQe1oIw*BsY?)L-4d(pOIMb|~!_7exq=+IAG-ngx^7#~w(=+)cy7tbBDTX*qx zOqGKhsj}pQZTpJw0)D;nW-d>;WLsbHro8<8GA@t2aNAiTnoOy_&+i(&w%6br(OBmuFnTWy59L z&K6J2qjdje+~AViDcy9yse20Ta;p4UKHBJE?+z4?UQR>5nZsqNU1dC%-b5}-@4HG~ z9(Eg-&zegC}xXytei79(lPcrYBcjct!X5 zSJJtCui&ep7f|E<{SVqp%(#L_DO}Ix>R-IuTXepXUz=oaUUVfL^xRBJ?_9afoEG z_vDpaZa?q+!Qzpd`1O@rxoq&>-&g22b6F>cc>DPGhluaX%T*U~`QaXC>@AF2_}hcz zL|Jmnwr_}Ir&6k$wC(HSZ8^FRo!h&Y_|7D%%)gY&r7xe|Qxqq2`FGh>&6DZ>@5z^; z*Uu1>Z=|&E@7_O9+%ttXw#t4z=fQ8#eSdzxv*>dx-+eTR@4no$dM`2kR_=1k4P1T} zj)Fb;m=Ai2&QqzfyByhB$A8dU+$1mmd^wkIPu+H&7$p}*+rGZ?Hfro~9p7Iz`u#rQ zJ$bqBg9W6` zo&Q0d*eoyKkqe zl>=6qch+<+uahru-kaPwk(b}OhRZ=;zNx=>=(7(x3ws9NX;<-G=?xx|muJZ*+t&HR zf#OTIb9t5Qv(oLC$xBTx{?eoDbq9|!^{&>xz4H!!_2zQ<(H|%6D|*i4*8}A^m;QG8 zOn#j!S6%6E|1N+1hwT1eFPzv_G(2$YU@>bJ-#sBGz>KeaI8xYm@)v>Na{TBIhl+Lb z@`2f0&OhbD6UC*oxg4;7%U5P^yHXr1Uu*pZA08?0ox=~`|0b6gUh(1K;wyJ?`3Jd7 z2Hp1Inc_d?rI15({@vRqiBIK}d2z)PUB&KmnR9h=k`4G|@#$h&w~soDx8_p$bh*B- zd-lUGi(BS#d6Qhmi(daw6TRolUl((E&HQcGi#O%2y5~V3^%8X6NHJe-KTnKX{WbBL z;I&?sPkPvbZFh^N>sUoI&Rn~vm^J#t&SJNPRKE9WejR)9+Ox#d3#t6D+{q4Ev~8OB zoHIat_>U)Dv1$n$Q=i3D?jYaInYR8I@x)>(KO%SimoL~jT3qo>F5i}`chv{n`Q+bz zq=;TiX84p_Qv!KB}J+GUrE7d+Q>my7R{r50J*E=%8&r4_QY zPL_TqOE1dOhqClbS^7klw#m|`vh+t;S}IGsJWuIWS?VE6-8G-Et}LyRr3+>0hq83FEWIX6x60B(vUH~`Es>>f%F=tXbe}B! zQI;N)rDtU6X<2$ymR^;mUs0NL@vmiZrTp?eS$ap7K9!}nWvSB(l-A2qFIoDvEY-=< zJX!jZEX|cAN0vU2rDJ62IaxYYmVPHo<78=*EL|!~AIZ}7vb1QDEKZZfAIQ?(vb0*3 zmdVn~vh<)Vy)H{@W$9U2dRCTNWa+1}^tmi;mZe<~GcW!~mR^zH{au#+Q^g}yWpRZ3a)B%zE=wC_=>%E&tt@?AmNv^$AWKim(ga!hg)H4HOZUjq z3|V?fmV_+*O_uJJr8i`0jVwJcOHawt7Fn9~f-EkV#n)x&PqOq|S^7+tK9QxTW$ANS zx=WV2zeMRDvb47>Et91KWa-bcq|4IBvNTebelAO+W$8^>I!Bha%F+e0w5HP}idV_v zBl62Bvh=7rNy%JiY)y_mfn=5|BtQf4$SfT z{(VFC9*9I15u4bfMvNGZ5w&+vEwQ3%w5o{~^=gq8In^3bBCQ%hxwhCv5vn#xjlBDP zUNx)6?{m-dyor4K`{%vq+;i?a_uO;Nz0dR9cb|kz651u9ClcB#p>pQ{osm!gp!C## zBp4wxf0R&d3Eh@Za|!(`p-vLYl2C66t&q@g30;%WcnJv!&6Lo33C)wxPZIi8Ld6nV zBcaU_+9aXhCA241g1<=cgoF-B=no0~A)z7(?Us;DLMJ5jTtbS3+|L8rB%u%q{U)Ip z32m29eL&N`F8|Tw(NmYrURE;|!EKhYIkoFF*Ohv%E8a+xSJ(n6zXrmoD(j2$bS80j zeeuV-tRA+>9P*ysP~4Ml_OQi@cDco~brKAu=*<=%s_m|8EPQ@0KL2KBs+;a@I{2vA zOSJi=_&1GGc){pHpwyv)pIZcrl+DHWHA==2l~R+6YC5WlO+=WdPpVY|b_gFM?#vN;A;l8){s7C2o*Q`>q^2)gj@BPJp zs086SQ0(TSR{pA>xTc8DFE(jqw)~=%q3mX^fmCF)28wA%it{z{?BO6=&lN|Cv}470 zv`kqC1&nbE601)X-_SB&dpUcZ{Q8p$SwHr0r{q=^FLCX+|5O%{wWEA_@?YZSE^ePL zzN}TS^@&jBl4V)Q_H)s`+IxYPo4C-}0j9K)4U8N8`Uk{YlxEVz8-1yGrcUBP zV&k91k2EmvO7Y(+Ncrci-r~?TIj@q797nC*w8RKUnKIuqR21Hjy1?c4+$#2VQJH%E zgx+~=^9UlPp(Bpk71;wt?V{p~S{)h1sN?%R9sv}+&Lf&WyH^|}hTJK>q>%%4u)s5T*2Lv=Y%@QlxJSd*1lVBk2FR=R1 z85ct(iq5L*K~dgzFX8yR_$!T^_o6sf_V*U~Pl``#l*~zJ z92JF%L(ht@{%$(<5!Vj8e*o=yR`Favu-43%^e3>0@N7}7Lw zbS1+(8h9|&Fhv6s!VEAb%zHfC0QF_?w+P9Q{J`Ff(mEL$Q}k)G8%=yWD1weh8G6#s z|7v!InnxQ>P+BD=0(xCZ*yH%!;StOkZL zDoCrE7(7LVhKBDoQhp-?CZEfKgjr#5Ml-`%4eZy#U{OIbw=sB&p{)!nHIip*1JslY zfCM=)XlrNKsewD%8*Zy0AvZ|;+R=a|&RmNU4KQmAW^^$;(?ESUL#_t4PjXgH$n_TW zdKijT65V>&5GRcEbFEQ|JYo`iN1!vG}7+=hHMQSH_!kx z!9D0X*Z{-JVAN0pRzH_@j5Jgx{}e+d(QCM2sYZV|!eG@v(6Jo2)Z@{HwHlZ^Mye_2 ze*S^MrhysbN`%sRe2Gwy8zj0+G^Dwx1%8`s=&2Q`F{NbowtQ&#M9XdYk>LvsJUgu< zci;>I{@embg@p>h_FC9U}@$~^(z>?#Qt?sE0~4LNfwGu4f7`dUn{#)^m=%K ze{&2S)Vc+{-UE$2x-CXkP!tMpLtzytlft~knvI6uS})RcXpqT{-C-d#<~;0S-_3^a z)Y9VB7Q-ryGk+Y0zwf3YQ1-7fWw_v=@4fTpAW?3+VU^a{>KwKEOs*te?SLivU*XBp zB6^o$s#Y(Ws7C>v!^4Dicgb92JViyxiQ&yjDU9_H0sG|Q<>3z=j`4s}S0o=WOw;Np zx}c7tDXrWNNi@ngjMmY>EcjjLd88XvJ_0SQN7h)Hcf^pV+b^0Ol}qGM3iOJPDQg^u zUTqoY)T=U6 zC?dX!7kOQT8yj%Suvg=efu5*;#^BSHdXLEh2<&n;^mTyV9_6-KA2FkBq1wX52S)RO=PL8w@8j zCK*_YttP47TqvbJ@BmS2&9mQTU zPL^lfmFe?;p==K?8DoY0U+MM%28y(o(yvI6k}o0tfwsUyGhT4p5F;{mM$9MUTb>wV z2zZD%P|k<}aV7^ivvkIql$RRmN+X&FLY(7Z9jvZK%oXz_+$;8>fGtqf0$o@os`p(a zLIi0YJUomT`Ty{woQ$kWrc0XxMVy!Mm_|M{4k4qnnLv@?W4xkK;!lDy{H$7eZ86!; z2)o5qVviL;c~zrY((Lac=HCO1*s(Ds#l^&wx>V3C%0s*iGD3%$vh^R7;a;8@rWrc{ zW0&NjlAWWCpaubv;p!l+Rx)B?*SQMMvazyp7_ISlZqDk287Ju^9z?uqyqhDMOV}C} zM9H(Fu@4J3ViEoiIeul7CuMvUgTGk%5TP{t1J_97VT}>StQN&rHTntvXd}!Im(KP# z)e^|e(ovQty1^&niLu6>G{VakOCr{or@JV|RyV?SbbXEOB%WuCqRDPbpn$i6>jW4? zypKQ^@jvf|=wR_xEhEAfreJ|p5h- z)5d2(?fA*ans{SZO5OqmW7p{2(3p;pbBi&E4t6rtri?W)_#3>6Ca+zMjX!FHm46#+ zQ~c|gS`=_5#+wR4vHyx~YJ|6u<#shUwsm1hB)2qnQbB@KgwuEt)0`68SHe=VjaE%r8rij7^Q3QLNB8*QL9?qsS*{wEuFQsHxK+3I#Tp493v z&T~aQN23l-H-JV5EeEQToKOzno%LD$yi}ru2EB(qd)P@ zH~&0D9diuJ#*RICYKjs77W%Cfo^xv0oYY zrdip#)`%dTTR^18RaALHkbkT*?or7CHXFwQ%!$Aky*v?zXFpPE)qgT#AE5S9_8;xw zdmy=p25pokpDBo~F_rh@YEnT8$}HJbmco6@D_`Ty6>@xw^u1gfjVDreSeX@~zA%J1}7l?iFj6<-`u-44uS-D6I#M?Ys{fn~BVw#!YIbfVqirQRzIU{6A|% zN`s<`w{VZt6E4k55L6tZjI9+wg=etR*(^uwm&-x&>^f+~{)D+vZ3k?YYWa|(5MAb@ zo^|;~?B+7 zjR*fzMZOT_k?{)A+HD z_NJL9%SZl0X!b0#HZ={d8nFik9wab8gzXZ9Ert4|)~ZRF!WbbEZpvlMJe6-t10z9# zx2!EPi;N+qWkk^(W1vQ63!l8dR(;jXOuuhDqt-z%K<~aGuA>HNL%4Q$lsZ~*wg`^2 z+VxaB+s-Q25Vs4acd1kspV_5>W(u1~?soFrTK=keKjd(>E70yB7kaxgLJ7R+5JEE_ zLrGVJJ41=sMxl zE&*%1!73QQuCI(ZFk&)m8^~dlvAND0OMj={1ZDeg1C^;XlsMvI!UU*1a%Jab)Pd=d z{_kaZ6V?V((CLO*!|T*mU0-YWvPHP%bA|>Xs#s=tnqU#Q9OAuB_)puqZ>r}8Zxak2 zlUapR#@0cE(!XwbbvDlXnxKD@EKs_NCaQCRrp_*uRUI)SULOgfaZ$QZEh8x<{Cx3(O_D8d^k_9%X{2Fj6UxsZKn=(fDB32UEuaB!gYD(~ldo~o5USllJkn6grAl4$D zZE&3&^Cdi}hs$?I)ejbJTAN^YnI95`V3oQ{zC zerik{pDcBr**U)DVMN6@@K6Oi>Rl<=_E%sY_+^ueeIeLGH#GA=ZTp$>bjyU&Ps;b5 z&s9qrOy&XLQsz6&Y5#$y&vgDyBpLM34=|$v`lF<37Pd&%lhe16TFdR7qFL! zE^0})l^6^&m3TQ3J<@dT%{uazC@tS`Yw4p+*h6!>ymcws*x+p{_*C&#-TdV-QYhTc zo)1i2wRTE5OBHxQstFq-$;nQDd+|TM>yHU?NiYisnY$a*SG7BIvYf9s%QkvLuSZTX z!4`5KT2D0{)%t*a438DdIJ{vXB@Aj1EYhZ%;4362vDHK;m>foltML$U$4t{`9ocRm zI>h^C)##^BFa`N$XLJ}c%QRmn@gTA3Q>l{-Ui-|{O9NZYG4<8J>@Q3y8W=R+G*JV; zSzvlk1qsDQQf6U8&4SGP%7mC$R!H}jfU;&{qhL|I$bgGoM&30 zvI}YKG){nRNom)_lhSH7!P)O77^JwJCU{Y9(rL6}DZfjTO61o7JlSW#^RAGKoq2Hf zTZIzhnp71jdrSkhdP{vu{`$t%gy(+KW{n*lEP<9RLW_p&2%f4QlX=Km}$a)23@qjNsxH^r0Exp9fNE@@xM2zB-)-b zU4An&dz}*c#>hWAQ)c7>=GsfEoktmPrAbvvTd%lMgJ|@#{jqE&*#q*5tWGVz7hPhw)22_vQDD7&dsn^~KeqBBz{7l6 z!y$_}vMp-mC!)Q~h`G3AL5wAo0@}Cq6A?aUOay1L0wl!YUEMaTT6v3jKQm;`6b#Z^ z_y?FV*QLc1F1D&fHMZjsPM09_4vkXMC0Tm~(K5sg_rYag{^DrOB$Q#JH>{%h*Eeg+ z@81$n5<<-oA+wiMBoe~RShmOvA;=<{;=8oO^Y$ucL`Tetmp0JZ$Py_v@PS^(N1HLx zOow8|QS^tceCXT}IQPj_&3SK>B$N!N*W+6H(2L5jloMl1#LEi=@-S-bQVIkqgo~7# z=CfL6snFtQw+y0xBe2z&Q`?M%D;v+ns9hKmn1@$trbvr7Kh}!N&Z}KW*apk8^bd%0 zMkJbXGF37J?gPKv!^P`XXodIL zWxp2<{D0(_ER+b^`U0ib3@#ZKPg+3rR`6$Wtvsm8PMF6XL(MRc+)XGcj)J+J5{(jD zQ%Y7bzkbBR4sx}Gh~uNmuAYEysMxP#D|fuZh9^g7-ZO90YAaD%%A4NGRqT1cWKox@ z-s^V~T*;o?%3T~9tIfHldieuU?@(W?i{q*0om!RrZW;H*u}8tv;t6I%4BU0qtaNJ? zEcQ>5+I}hnk2~>gKngi>oKHG8Pch>F3V0A<(3L6wNJ3>XcB&acF=rwK!UR-kUyTm@ zmf$WHO*7+6lqtRTVrmNF+DD5GGt6*9oXPW*(yV=qIP|d@Q3+={rR7HfHxm4Xm?a0z z6sI`+WV>Es%WU&(9lc-MfTPKe@o~(r>E^DK&Y^~25iMuaUsKujmFDEaJwGR^g^k(=LrXY0jrpd)-Uy63`&_HB-Wrjy)3cpE)_24Vg7MT$Sb0z|GZyHkL zK~+jw)3F9Axg9;|m+zpZN0&&$uU6@gDw!wYpN^60)3_(!n6VpRG7e6>#m%K=M2L*@ zcpL|D&{nT@Cmck|7yrg)m2?n0hSC=O>1D4@IAY7~=q}JK4$HX;c~m1-es5l+Rf$kW zD|7zGPF|GxM|*b)+wJWozF1|(USe}GREED0NnHbvn4&oK=H?nX3BZF0ihyUWcRK}& z)oW!toQY>y-eSo*^HU9HElqlEjuj_!%s-Zv#Jdzn6c1|VhV^+Y*Nig&E{>=fHf%~_ zDE;tLXFr0k#Jg8N%hfH(0&0d+ZRW&GqK;OHUc0tPxp5v=w^lm2L!vM3Z3!<>HLs+! zeVZAN*q9T2k0)F2M9mO)|J97+8>Yaf#nYrOJ8KSd(k^FlLgVq&ZeSwb8@!F_oV!~t zQdvfzHY_tP4l}QIK`hxXoi1lOjeB*m@}OKY$c!Ke1=saXNpz!i@v!s9^36E^WOmIw zSEoJcU3|s)BhsLjt%r(cCYGlIpCo#y?$Um&q%h0nbw*;4cy&S=ZZ61z$=}u`Qn;Uz zK9e(5&XbBaJ&Hh_+vrRQJIkQTGDs&rE73!AKU*SUo}Y}xiB)Lvvo2nwENtm6@C?;C z0ZfknF|oRsdqJ+Ovb`3I|0od&%PDzVVz^lSry0+{n3LPh*psNa)Js=NB)sOD*+ZK; zZX@B05=jAtT|Fr8w?uaVZ;C*R>%cmWr{q5q-9*4`^EEBgStd@jEGlVDswzQM!?Cx) zq^`M{=taq=;dj}Grrjwk(x`FWylLn`2x{TIlA%e{AYdB&)V|?8P;;o(O?W&_7mF;3 zcsb%}q71jqx3nJNM95pJKz&KUk$ok!E1jr-L%X8!;Ms!8BT^I@s_^*u%oPjSS6wEu za*_SO{6Zy*Ef1xmbxN}(EeT0-7uBE0aDg+~eR8|`v7H0e4N%H6O(}x}EDcmZ&b0XuH}J-vQ_7{UbpG#m(>TZrhEPWnThxkZq>3$ zsYUPT;Yx=2J$xxS9)Zf(MixZR+@RbF5w=3XBB6-|JCu@4;n~cBT@T|Wu9O8ti?8C1 z3B0i$C|a~A={YnqPQtP1w_@Sx|Nd*V6QQw*SQTMIldm%-Bu zZ^D$t(4ieYD$(SfU~G2*@q0#s#pwdrS`_W?QBS30cd(drG{X&BM0_L}LzNRPU8$=( zUTvJ0Xn`{Xh?jdhSnvW|U~&k(y4eelg3iL9^5eA;U8L|NPu7=+vzR6Sy5g@o{i)sL z=(q;?3XVulvTV`O<449c`5ERAeAkHAiEKS&Gui)Q^DFnB)bh(@n9W{t+?>g(tnV#* zDRD8kj|J9(NgQ478-Q1M5^gFXVpl&4wo%NKH$e8Q<|~|;@cS(H2U&V9UxSgPd0~q; za4@Ftyzm$zTMh}qGh=-BIgSFN@N|J+jhH>mf{+b39$?BdvAUZro+5gLWsQ#95>Y3< z3w@6+B;wuoT_Y{?b-EoiqKCN(of&6|rbF59M$+OStjD#ZEk`9SV6z36g8$g3GNqL3 z6GWM#vG`7mfoMPr>{e1Px=)}=yZ-^S9kd6#?ZLEl8(vRF#VNy}>)_8^khwC{_v z_kuAVV&aE#g)x~uN6HF|AI|pr2GIFMeM70ROJ8@9`H=<2n|b)yuJuWaC&hQ_TY;k2 z_KnCI^S-Kwm)(CEjUbvoyX`&4(vu2e@D}{hITpnF0Pz~ z{~W^CG2!cBZZhWft1XUyX}PI!9_R$lgL(^IWo6FAMtn-(yTKAJ0=~B3@TzPkWq9=u z6FnDOaAv?1y!hoq7?B@kU+4$dU8lb)hX+*6i%(F3vBL)|TtV7eh59IaHXkEQ7Uzc(V>~+O4oW(U|yy2;cHhf%vxlV#KN+ zq}5l8pETe=;wfe+`LF&}=vLK!9<-|SOfRu%mE}8)XL1jWlx5<$)4!TnvqsJxm&O}? z6=XrYs~IrBQ{=6+;QX2?QqEM=WB@*Xt%VNvTyH__%M>X$0l6WZVO;hVuLMKl(ZdG> zis3(5{?<5sCmDUj#Eq68RZfASBKYM((Y{#GmiHf2rz+k1d8>i$?5!oUiudn)#Q1HN zJvwUkEfyobyN;x7J7Bg}Epr}%VtqWIWOjhnX-WP-FZ%4rKo5N1WSOX=sIKS> zzWlC8FAtzE8H2RG(71!}vqwbOA!%){ZpS$we-$2l7#cizkMj(0-eC()OOYd}X^3P3%1*ZK~uEhtFCPwL17L!^z%6q!d`D>1fk2EJ5dY z>~1vhBE}joAMLW?Ja*ACODA#FUx}fYq?$6$Vb0{s792q^?vyz!*V-WgV$D?xPX0o^wJy>%Mwz$bHY5V{N(xR#O za)!H}mN3+xwj>O#NMTKlUgG6#S^E6vhDgeOY&_WBM&M!Ls5$H)T7sKZ^dSXe3HC+tl!v{`20yaX? zaC`-YkEj~qHi3y}Mg)p~{*kteIa8`w!>Eli!iOq&VWZ&s!t$#|*?bdrmCK}m`oNdg zC*l;`<&|_OvLGS@jj9w5uf^+XXDusi6xUkc2eqaZ4-ck*A`FpNT@x28c8g5r&#GYD zo@zLMRs}7Bv3ow;O~kud?`RZ!0_7tbcvzuDT(V>!BEidwjfu<@Xzd@3jxVr$XmSV! znpS%x7Sz^vam}=J+tdB52tc_GwnRLzyz~d*Z}2P$uwvKFJf-zG{6@G@UWbvs)GK0y zyFg`5(McYGwh+T`8RaO{b8Bt19hEXNKrF3jJ*rZ~2ccGr#&pIY3x!#WG!o)dC!EX@ukk23BWr$eBh6kdpyERIwJhH1=3$;2kW34c1ToNsZQTi7T zyv3e4D~`iB^V}X-`h!Wo;-Ty~Q znCtSQpnge@3Xa&yRmW>FZ)yrY8 z`8x^!rdBNPKQCZq@+}z;n^}8O_C-sq7}MMe%LEXQmd_RYh?gy_utbb+{sl6#EO>wq z@|ungBk>4E|2~=K+@hZpvQ^UJlzekp|chK zmw~NtB_%ikkEF@>-g6gq23Y%P zRp3#Zw^%hu&M$NF&UMKUtEWcieGKqe`mFvK>?P!W>hUnSMozM$YvGP$_)-)@Esr&V85fmM2d$-Km*Z5p^p(dyGNj6V{uo;yzBgsh=jcn%CwOPJ`WI5o z0OI8ge0d@E&Bb6$3SE9#4*~)<*aN5nct91Dq?MLO}Q|9citxFM1<>H`Qp5}<;#nxaK6_^sWUu;onH=+n%@8=?o|^Zm zsD(2^>rLp?3*qve?9}i6sG2+VVg;&h7vh|tvER!H0On07F+1Ces7}@UT%cDy1YRSD>e$E4PcV7it2-YY)cV;9R}Wl^ zNM`Sp>udCsD1p*gHj*L=H(BvoCo^#f60p^ZXH~%Qru0w6o+PD78TY5)mYof0?qcFL z=^&V$<4h)J|C$y|={}BNk-5WJF$X~cO~T1AJ0H(15>~u;$~fNR@)n!+l(dc`MsIOz zpY>-QRsX_}#&-mH9mF)$pXao8(+^s)-vb^bULTSTNs!p;SPxqfIWY-OXa3Phd`j@| zQEOOk^a-mA#qNM}!2AxT1ACldo9`((wZOG+@Vz%+bIh}P?X(qP3{&K3lsNRe6_Jv} z1-{;gt7h;(ffYyO44%Dc4RBFG<&qW8IJhjU0c!Q6*T!j$R4Xy@ik!GXE>JhTfR;k} z<)LYL*ANQa6dzres(3pNp&PC~dY`t|K-5-z(>fJT_iG?(V`u(VG1Mxd{CggxT)eH!jG5F(`Pk<&O1|mI*KKL!6m;Pr6{qDe$!tbV7nM zfD?H}IWDe4?smm3v$gUYI045MMO$W85J@)aHMoxYiWM6YE`;jp@Kp`!!PgHDq&Z^> zUyC7Ir?U4ooP21_*H}ian~h00Zc}OsXe^Tyh=&MZcD-1~^^=#=9Up_uE$KC^z0|E^oTnx>7)6 zCwJF!HaKA5>iZkwR(ad^Iw}{6nZ-3!qv%{HKBSu9Zp+hIM1+S8GrKfjRl*>uZ?*!O-(zA8AB4K}k zZMsf^I7Pxm2%=Yz&7tBVImGr-1BX|%<=|V2FP!FcekhvvU*xoLe}~GE0LNjR+$^4c z+N-I0(sv@v25r5x69xHtEWX8WZxxhIJFbcI{n`j%>89>dsT#vC~-7t>t1AXX|YrVoK@6ntv)kZdsm2K92j5ld`v-bJ{-t7#D zv-Q+HQ(gG{IE)#{NCn>7+-^=?_1v+4P1`=1Ef3d3{o1yF7;(T`4{kCif-+jn@fT@z zZP?A~JKN{fO>OYidftUl!WsV8U1r#t*b6uiYCtEG3-bDC`DTz|-48G`W8>4|!yV^!-U~xAa zl$sf4B-!-%-mep!1}-O?NNK$_f05M_Q@B@b>nW9K=}^ozZY%qiW)Fjd?cUo~8?W9E z!*t&qZfh#`ziaEI;-X4l+b)fiz0dZ6c&EQ@fJWLrKrRL@6}`yjCPojkk%mXzw6_*r zhS<96XyFKS1vjBJrl!N7Q^Te>{ny%IvQgkc;?{86LJj;l#Rg}~xrw7}2(lzd$yR#@ zG5kH-U=5GHX@6U|zAx*al!LX4`_;argbj#t#*ei%gedmlW0@`Eg42=rC?*n@er2&b|grwKdX7JV^BY$OdD~;FD=m01W;( z!?scblRvh>o^tMovutr1xN)|v4uc`WlwOh>^SNyiF8)Y{H^3!uYbbA&^VFzluB|C< z@EL_uqc*GU?Zo@@Z6Bz(DE!j4M*}zNrAA6FG4U%K&gvM9S!8=(16M4T)6LbS{$#5q z-pRBTX*kPp#*r zRgE-sgAGp#xmpaPuLc(UZ2MOOZ~bEHs)5Zn+ptMs-tJqaAQ(KBXM=lxe-NxQ->O$< zhiy1D9&CxF%{y#)^$`hars8tM*pyf<#6hfsOMNP*~kH`hB=9QGC1` zO9Y5dUt4?OwbzF8rE4N{pY4LoT>GjxSWG)$`$lHEAHs-{`TV@Cq4+M})?OkLFW4Fj z-y`TWkbq2EE0K8=GXwT1$8F!?gU=`|P~7b|j}F8*O?b?2a=`${hV+zNFbw{4+J++) z22cKO!zNyWa>2NY4d-lqG!kSDW5HaIZK?)7zi30m#kpTxvf;ydF3XBhcv&h&opfwn zai`!X^dcSaLbSMMYlkly(h(FdxNbwp!l2Jh+ou}1;+Ach2DUADpgbIQCs?Y1=cgT?2#s>{@3;p1&Q>>Y4XipdCwr!M4Hn*&2w}S8CwL747&UgBe7qeUzH3 zj05ca{QYa7hAZC&aUDo2nhJf(zz=!hmKDW6+BsKZ>r0hkDvg~bd?HO z*VzA5p+DBz?HaTWi=@=nCQ>j_INyG{u;usSR@T5gTMYIn_;le zU0F2oF>I3aBHEecDCZ2hE%)uU@up`v2o(`&6EVbK@1@}peQoi=YLsJ_bTQj(Kdgap zwU{8z4Y1pDwE{dV{w_NkM#% zZc+#?(Z!9eXjfjLixS_Hd-hZ1E&6nQ-lQ-X@puF}LgOPXMB2(UXi{kRKTa=SG-GW74a|=w~af>?T1-HnQ zx7(tCyxKO&i|@5fN5RVy_24UQ!6Waph1j;q#Dy=iMIvvp1x;RIi%RAFwHWpjl||lM z3!1#P7BqQhEokz>+9XfDtrn7zSJi?=-cyU_>+u1=hosr4Dm{>#dQj+{19$Y)mB`Ze7OD zm{cSF`bMebC$E+U8{7lfE>g2VrKxTwO$b!lg-ewL>HcIFkd~>);BykWN+$x97?<EWROzHUrs6bYrb>^%d7Hq+cLglJvm>p<@QV5-b(E#e-iOChUZA;$2D+& zE43zm#$ambaMjrrb%$v+s&m;_o>~q5B8gmrm5~*s+$H;DJJ>(O&lm`n9{lw~1D9XQ z(~!Y?JCG$}sh#+AXwm%;iBa^)7J z-NPWtU;_rZroTay>p*r!E{n|6E;HR2lSVBwwObi1sSGApiYzBrP9`szQcSL$QcSKL ziQQyQxrowB(K~ZhqBMA!srSlY#bvPUZ7QcUf0?O2mciDS!Oq-MIpwm+Og*Pzd`-5R zm42ZN$EE*rT={R-_5Wtw`fpazf3xoXCo4U*_&<56rvGMH|C^=!H|y{JW_jO#Qv$wa zF#r4R>iny+2rNSkE`wDlgH+U{PhT7+^gAYV*~~5UZELYLvlhmBH$i z!RnR4r1g~=CarNMo8eNd+)R(RmJ9?@wYP2(Ex|m?U_NCq|1wxm87$O^iK~?r9CtaW zX1LN4$taoR&`LGAtR?|fka?HhQIok8U$TqhpkqM$&_T_Sh=0${Yb&j zqKEi-{7ial8Bc=KOuJ<9hvZrn$ua6%Rq5s-9RX<9DF>J?mz1hX9}mrW7t%Y`lx{Bh zM_!mqkN?QfgXTnWg)Vw$)Z*{G=h4jl&X;OubJC<_xZ>6@I?~0ma zL@KlTFGALw#rQe(jm%#|!|R}hv$U{|@{{g4HLib;Ng(w3@aHl;s;9*JN!yP)LSg(z zrrxDS^_785WW*~AKbj8io--x)uUb}{Pm$Glwi<46$xCx($&GZdzEY{bjE7~(>C3?O zd^vtnR^g{zjw~RfWJ&z@XJozHD6^!?KUmSB z=S8OHC8g(8q~{Hxp7(=#-VW+{GpOg-NY7hAJ?{kdycG1j57hHEP|v$SJ*&5#HCoTA ztmhqIx}G-)de&P#dpDu-6W1*$fWWLHcG& z7bF*&!Pq&ddUNGnBr}^U-H{w`&Y6{4;ONFdX)QSOKntZeF#nc}4QWIHk!}|2TL~kxeZusWNU-&+o)+LE zb)se+Y(5xlk6Q7oX^pujtUON$l6gEhopZe<#Qz0Iw^QwMLiM~JYJ#vL}fgZ zx}B9NNLF`d%&Q9|LG(cv1rM0$To+|9l3HCAJUFATx+=qwn7b-?{7%W;xaz&#m{#Q- zWg;>cz5};NA+s-{w12wOKFE>Q!i$D)(f(~L>-f%4wq8A8kCx3@mOAwqP_;W-<&e7_)6tZ@ zN=16TUcpN%9V=JzaFS_cAB=n}?d*g8ZKZ&|sCg@m>Wit~O1t|)`;gg>ld=6E^R0BU zAC}oxs?i@yX)9&*hv>Iv4N!&w89qSiSA$#p{2F>t?}oEO2LNd;9+w@Ohlf8YE|>$y z0Hry?znTLftK&3#pfbjn877)g;72pARQX!Lg+EnslX1cz#T`6d2jNKOB7HUp3b%z; z4T7R>A?qOYehYOOtc*pHHyAWCoga+A%}lL_C^M1l9HPuXQg5j8DUyvtp_68+IZXKs zN$xOai5{+e4D5R(SyL2O_W%zU#9W@#d$iI>y)1ak_NTbiYzjW?{Cb2kksOn-m|u-h zCZp7(6y+mXs>YxIlsbxKzegreeg$$Y(s(*)jTLJY(kZN*b^s6CnHR^vMM1%5}On z0o}SzohCv_uG5x@=+T)NgbCyff zr(-{mi+|iizjLY04D_auhRy&>A^kQ3^HE3%GnMH`#7yN=pF-Kig)>q4Zy!_s5LBM_ zvGO*kn?A;b7E;YmAk#ux`w1qykV0lbe1$Y`7Guw5VX_Np!l%m27_Mj6Jk-xt}E&DE!mga5|<0%Lbg&>-d9-%`?23YSH}1-`H!DL)^DWb zuchQB;$p<;p^6V!=nJUGEpnL)rMN|7=VGdF(fPTUqFWR{4~lk+zMsdLy7^GTTlkA7 zrX8NIjEv^0EA2wni}0g%>_bt~vE|g^OMG{;T=wsz%sz2e)eGF@SWDHe<@obebYMAs zyZ|${oXiU_W6Non9!)N%V|pmZa;muyTCtp#ErbFtC*4<2_~kU^E5>er#hi&>EAJt5 zJRa?n(F>IBU<}X%e`C4U{3a_F|8P{AWVD89Hm$H;jDt4fNAWPrkuYu+oHY8N2r_G|)=Jmcr{=>HJbG3@f!= zhAy0Br$D6i;!$nlXfc< z0(NDE@+FcH-zy7|n7>!P^ud1jt<(js<=#rI=1%D=m9Z4>-^h*9M_>r2=+O@d8BWnh zE0sA&o~%TyaEd-&g)SNByH#+`1~RUKuQ1>(EW~#P%32NIWFWhmbY6q0Hc+mbcx1y< z8E9-a;s^sB&xTVsP}Evwl&?W{!Z!k)m=;lv8qR?N=B-tlfP8-~Vh96u`4OsPpbbBA z?XQ01YDcW&B!3-O8@-;ZoxL8NyFe?~D~Y}rWVNlUquML=%28%Astw3ta)TU10T(Dk zVs=`SgDAjGg*k9ucB;PtQGlI3M&c_USf!q7jv5kLl%w`-6Me*h5QxM!d0y{`?tL?xBc{uwQ#<&_)a*k7jN}{Fq1kH)1S# z^kSpZ#Ya9_OMN~K6>pzT7q%<)9?t6H$~ zEEFvNDXra%f=fEI^l&=HJbJkaQ=dmYHgg48o6(&-y1kh@P<0F9-#q$Yi_#g%mMsuT z9{sCkCT!)VzS_!7T~M(qd0g3qJP0U{_Ns};Hm;?|Hm>uVZOro5Hg2facIKS19n+OZ zCsZtC2WB*n#_Zr0@^&Eb#cON7a=8J&B7DlD&A&q9khxPCf@Jhgs7)Rn-3c#((eGl; z>AMiu<u)j#BqRc;`s$#R@w| zKKr0#=VByiW}`DY$|c7cbGlI|-i7*3VI^{YpdKRhqCL zb)KZJ_QSxPq`&v0&Xd&T09Nfu%07UVbdq!jA*Yix;2g9$@fkrMFtFVILG*-d7fDquzcqc{`Uoa2m6d zLlw`k>?WOIxt==1{fqn^QssU;D1P8McNt~@V!EOv zX|-(tYkaQM7_By3dg8QuIrQrlENiaQ2S<9TYt~8qK>UTPPW(oGICA3DQVn_cDpo`e z{dASZq`St$?RQNXrk0Pw;dbhkeRAfYUweC0D)9+9&sXpI?VeU(=QA z@MEv3*$pMq1?S8CZzyeLnsr0zAk*tN6g;tXrO=y7N0|=0sSK9so}1X!pQqxR*fhVC zYvQW8JdJ3K9c12p#glMBM;+&Se@Vk{q2V1g`xeaH4!U+r8R)x1mPy75X=>iH@)V;e z_-rn+Pfg0W4L#dI3vQ!VJIHVwa@;`;3SonG(9A+qbC0MHx^$0T7DD;%(ZC|;**)4_ z1m(L&mH)yv^B&Fk3(~nq1!_|34$3X1u6Ho+OKHU&a4x0icVK0g(y+Tw#iexmF4S!) z#opuOvwNszDc!sW#av1)?<<3Rm&*Q*s)+;e-)g#2;4RtTZv9KfxRk8-(VeB#r5HV4 zN=u5NYD?*QF-E+Uni()lOKG8jJ8;9mjWjiKflMPpzol6wba*KZGC_%#QmzThWGMxj z(eLjl-V8nejxx-c`0wbl8Aj|oinef)YEhcub%M1PaNehU3tGKT-d418pN3i?nftWg ziay+@P#adneHw35<|1*}lu>c_<(Q^V$Cy@q;;K3Q(X)W6PX8}Se*6jba=?n?Vu0#& zzzz@d7oD(UIsHWy6m;k>8n0k*f6*ZYT{u9u6|AEJRNDdU3UzaUc7;|sP~Zw#W#$_4 zet>doXygM7c@6D-fEB)m0{=#z*U+fHp_FS#{0$XdL;ep@;4IaDh+&W@-HdzV0l2 z{sfXfO9d)c@hN8xeG0F5mexE~+90|06o)QnDe@V&H|!a+tb2yZI7<(oah=WofnuJe zjDNVm!GAC(XUXF^*OK&{3w)y{SDtfaq5twwM*qwG{N-Pa?>U|R7bfUAReFILdQPb? z(D~=IM@>9lqJ`(w`z6!Xzr-Hs1{J(SxznV)L?=#D<5w^qr{!cFaK|H$Znz)w6^sWy zEe-*5H*L!Ch`j=o5?xHyI(@hRu%iDXDQ#{eWd$~lsdye@~LcPP2MBNC5) zCYN`NKytpkV;qu}u8#MSWV2iZipsFrsOY-(5Zj|hRiQ2gG zK|D3Mfs=r23iWjKK=Qt)V<3`4o)}j)g?KsMMe>0cWBa|(++(`o<>-f`p*Onn7}wxC z`ujYV)$TusW|sXyQ#>8jPTS~&Zd+WI(#fc1MHF6i)abOF}F*YO^bRlbfvNS^wl z?gv!S52JlR{rntVk*xM}bU^Z#A13Dk#rcEfG9~&W^D-^)hjcH~C4WatBz^&o)<}{9 zFlm?Ry8sB}G8qCemdlh7=xCt3Noj#7ewUU6IyxfxBM_aqOW{GNnWquCW9 z*KKr0V!x8PqT?MTO)EJX>MZm@B}b&kMk%{K^KjG7Don}#cC+zKqNl@Mw~;PaLa#RB zJ8cv_NbN!)lY_J%6!jgX%b}nhr0B|^J)w4$F;7qE+sY8eVftNW{y>(>(4!xyW0<3N z4S49cQfJ_)TIzR4+%>hBcoe9r7SANvam1Z^-9aCIX_Q=-GD6XZXXF}=!91fO;f~%& zHitW!w=qfPZg?O*w#C>M>cK#s2eQsAlBc>qOKK?aQfgXk3DrR{RU8pGl5Z!IKWUgu zY&1(IMp_{gB7vlh^91^hdibzbZN+(5`v#RyPF3o03XDKa7pZ|vwonh5m}$IB9+F-r z$0!#`o9nW-x$x|zy*=9(M+a&>e0DYhcqw&5S_!p~bdin-d6E+y>8OI^lQuwcG%-Xb zR{B^br-+cW;azlP|8j3sbTk%w1GO363^xH@+KjG23Dr&yWKDa>KMG0S|Iu~U@lpIv zAE#&w6xZVJQrz9$-HLNSahJm#3RDI!R$PiZ#qC?{hr7G&ZgG7+nd}bodOiQ#T;7=^ zlVpTZ!w`Ly40#8KSkm8A}oKk9=Yt$DM^PMg61h>V~J8zf18xFR^L? zr|UMBTK?FLq36tI^ah2j_fw)b%lz2qdA@SF}v!w2mT_I7oMt_(N_?iKS&d(~Lus1L z7>oY`uVpjJrdK1odkNR5S#hIH4w@0aV;BEU-Fy4>@7cq@TafDo70PZ5#-D*}vtyE7 zOCPfvLm~R)Fh)SU%3(}`=%3S=0}(TqF%x2LE+Y#B5~QcgxiGUEbiRO*mI~xHGT?jt z+?cftTAmwM0E52g#&l~?t2{<0h=X}>$uP(#uh9)+TwYx740@3l*GYqFd zihB4ORUtO}BKayxSqNFLqM?P1ju5vB!Om4wv@mOqFKpEF!LONUuB4aj542gVPJIUX z4-9gxqF046V_u=MMU2uA!-`-Uy+Y@Uz@C?67Qv}Z6!m(e+x*ciDp$t9p<+di z_!M5$$Q)lqxMDou4{A`{sOr_ruVa^9_zCoe@)qL^rWQk!y`g7XR4tBc(i>W$#i!!9 z(Rf46OW^+H4Q(pH3F4JRwn@;5*99P9rNADe)ZzXQrJMI2lS$N6#vt&z8{6ibhsyS>4%qxIhUi85JPvS28L>%&CN)zCdPW1DkGDhTkHlvQY{GdTD7@Wh05!N=H{Kqn%i_ zvd0QK(9+07?W*9?yn=qTG&0fYDky9P{i*`zTtQu`8si~eR5kiTw5bN~LYivm#1)jL zIt#znMrx{D1|JQo8)*`B>oK^Ce?IPbtQn)r)s1#4No&-pK@GU^3VPMrNJE=z7}fAS zeH#q4WHp&LYy*C+kx_)E)-n=OqU*_GM@J3^Ya0EaS+JJT2V!Y0^w$cCU)vY}F{U;; zaRt4tZTLYnsKX*(JLGe=4sMfHkY798*_Ek_j$c93>vBdv>cX*B&`f3a=e-WGBv>z1H(HSIcQ-MqmGU)gumW1?MUH6UQM~Y)=iDUI;LQA?ACTGMS2xeoboqA z=dK`EC%9IhX1I-CL5G?d6FgRgbVj=lYtD^Qw=){$d2@{K6;!T;F%05nXZYFe7Thu= zTXLVnGs+_dPN6yqj!=*WwlXr}JG=oqW3_@qvrA-17>~OeE|sgL_P?(Lm>!wf)a3oA`s~GtoS1rcZJ;Z+2}2t1K-j8 znJI5ut|WXQ6LoKk37Z`uH|=a|B*J%iLmGP67UPOtB0Z&Q$ELzB3Io9!Ly8wl`AYJ9Nz%FQs=XA0QX8Pw8qbnNZ zIaTWlYoC)xH?-dJuIR7lw4*C#oac1AtC3a5s3jj`yW!GAbf%kuH+!jScVjZd+3voUi~?4Zhs>PFvkF6ImDR(# zn3~lGE^wYbdi!+IKeV3+HE;hv2eunQjch6n2?v4&|(N3^fKJlxG;n%^k*Z(T8)K|8S0bJRDcO z%T#^@tE?J-!>gki zR(3Po;>N(ZOsCy|o2l3sjPA`eY7C0qOfSZu*v-^_tT7Ve_E^lZo2kJ#V+h2lahNDJ zQ>F1N_K!ElAXIh&dTKL8OhBhVbD}XF;`T(t4bf(jF(l*f1tV(J2zn!4XTO1d?Rlm^ z3vH&TNyzUwZJBB$r*V_v$;WBKWHiNbN;?JDoZ}QQ#Tfs`x)CfMp*cT|jDg{P`0Y2v zC`u!y8a`?9qXAC|UZP9X2g-@l)vwdQKK+LUxj6Hx_-XZNs!`TQtwTnqmz%?#?%Bg1 zUGDGKE68=8>P^Gcx`~!fgMV+L7t?S@u!&ks$6UXOu1+_mXHv_SUGK3-nLkiSe6C0P zu08xaE63rqKD1~Ce2j0I(o*c1xPH8(sxys65D#Y>>mg>%GS)$q3@|oBya+JDAr{Ow zwm{UG!z!QW7#k1@o6Dk6ps|V)ZN*xUAA!bF@FhVQZpb#+SWTgu!OdV}G=3=d4Z)Nj zMTbK$s-ma~8OtC(64FEw%|n_fDmNd4GK!wh=eTJLFmj?O-$ElSJ}+T8kxuErUHb&NqG;1%)H8}|FEJKF zyjX(Sm5oYC=|a()$7psa3_L~;LXAL(K}%8kF-o-zwj86q%Z$}}?Ml-ED4}TqWyCak z6JdBGHr5Jb1;pAFsKqgAu#!XXSE8U}G&c-a>|^+|l|zxMj3o&5T#eGX#FP}e+Nc~` zEgpAupoVL)HtFYTqi8$@;0rt83+l3)f|iD5O%+%nM@;Iw1{b;=G-nM4!VXHX)+mb= zD|OZy#Wl=9>(}Cvu!GL6HCF4GG&E-&Yn@+*IqW92U5}QyNnh8)vYQmL0bOvDif=?k zv2tdk5e(61lR*%1He)8diLC-TLD_KB;U?YEV%Qc|NxT(v?P{8_)u<6y{lbUQPv^v? zetpsYeRmqoV+6TYQ;Ti*A+wrZZo^nyO(V7&ArMJ+7@-hbcW_*lohWKG-PsBESWSI* z!Hm@udpC#X@5cPPnzHXPra^4k!=Z9}QJK{gxfhql*YspB?&a=Nm3_EtzfX(z;VOKe zcJD);_o(51JW#qvVf&56dUeb<_1NKCUkabA9Lb$4G0i%F+p~K(3kx28j}{$-zu%*J z5%BPP^eqB|0Ly0%85<#59Y(X>qv%JBFo-orIMm>%u?C?pM_Fa{F=M29kdf`UQB%pj z$BiH*W1lc4D;aRY2-M43JcD6~XE0Sfk2{zKXN)@3AQB@cV4e|2DegXL%=7-efCj5* zXi0yx>-|P~lGFJUMm2UorTOfXE@ISagLKy6^O1GEcy*ZZP>@!>|h`@9S`vJs4;%&*pLacj?x@%YXqr`uGRAZqvoPDCRY}?!hx()4_X~YhIJzeYo9gO8Nl) z_L`18Fm^(Wc*ro{BV#K<_a7NMAVMB_h1Gdt(no$OjHJ`e^J&DB&j~=RXFu_=JJ^jP`!QkCbN=>$A}pV(@1?EO|!n zK4S`eM!mk^nF-eCXwmsAeo{Q67h3p#!_C_>vcADnp3#8sxL`gb^E;ldJfopMSp52d zd$?yb@ux8k!t)nrHsP1bCD_YUpR-JrM-mTnOlE%c_$xCWC#nxPqj&Evp1>Js@JoFng!r zCzhG=B2)FD2Hx*fvtpQP$?PDVGTRI)n>;h3B7K`9mW+#Nc#4>w9 zbd7CxgE$x4>EsM=RD1n2tD;QE8($dE-y15L~}2*3B(33 zvk}BE9cmKStbx$txTX)D?Cp+=Jj1C$CNm`!j%Vh;_tx=DwVY&kJhQc4E7G+cD%G_e znF-DMfu`j(%KZrUB%+3&7 z5}2K|ZnBZkO*WF|q=9aqgl1yIj!no)PZP3PwGyG3!)aY2vjarD#He^U`6ot=!|7UL zh9#1qZNe!u376rKltbNTn~g9*8_F+3Z&rb zl}usw@a6?c#(c!$gDO-v)6&wEW{G&6y7%bSxqlZ-0pXM&CFeafCHgCzo}}dB>!dRK zBNUN}t;mzw?1#|e)aD?Fq(0^li0MA&2#7do%+U~&(wIGUarO40IDF8>YACKl60;D- zIrgKxRl$r0MZdJ>P>7ppO_w&Fm--Zs&}|r+j@xicI&L7J^lZ%7^kzFGxu2dJwrU1$ zr_~v_Ej%-FTXfF|-wUU68O_e=xzc^FzzX#NH}>k(qpM&4_MQB>zHpRoc+$Q*lUV`c zOC}U?jyhyE`+4)~p6z##5Fgt7@SGLnQLfBpHoAAVLNeS#oTG|a%sxo5B?}5ZM~SnV z9W!vkcTbQ|eW1j+o%#&y;)hmNk66#q+^lfe>vSutSr#I7HZ!db`A~yws1w!$J< z<~HX+^vPq+_Ba`m7hQFdhUUev;8KQSaYgEUrdk@(A)o0FaXz0p9HLf!cmbAbS zp?j(jE0rp2_C{z;VJf?u5 zD~uW!V;fEtbBZfd6>4Rwl5YVNSFpGllQtEHgN4!F;^q*Y(}EgMSx|#6R5KgX>=I@o z#4anrrX(zB4nt^aNw)7>Nv`gIQgF>MdR>Y`olA4W+$+rmHz{L|LEM!xoUVRZ^a|Rp zEM{cbR?eIVaig3$6{1sla|VP_o;6(+&|_hERB29v@UO@vJgdlYewDZdu2Q=@8 zg!z@ZdGl4_#$H^7TPJ>1wt9F~a{yG{R%I2xYM5cd=y)|&DN@~ZArxAj8#hr6xOEr} zsbTigt9csYg*g{KC{sc+ZAcxnR9yc)?K|=6tT4Pkg5idRQZ+gIe6=v>v8=2XXBe|K zh6@&5)n<6THaA$UI$Y)+EppbywIPhUYq6#-W{EILUyrLw^*HWYJXO#M=E>l)bT; zJE8iQ3%C2bgZK`o1ZAARGaFTy+#tEqSlHOCr{t@~W(_5aHZkifIjM=+Rmrza%vAU@ zB~DXZ75VpV3ToBV%z*F1o1*4F=t46y6UAtT%h3<=Z3e@C(As9Ogk$>QZ?pQXFweDX zZsx@I$<0yuZS+Hn0WHu`+vsTvb3_7O67;`s=mqsb4P3Zf9h%W72_7Drc`>nUqmwN$ zhi#*Lt;|di$aN_Gj62oJYygqEHC*g5g|E}ksRC(e2V@mU-#Wl)18IsM95axj zcVscLBTBdbVeHmQr#}_l|b6v1!V_Pk*?_MKw8rk zt{6zEyK%a}Zk!-NcT84+w4^)dlClTeGO-6nP9P<8nOPxVX9jBQGMjih|2~rM(iX|+ zs0)2Ck4zUlaUQkpiBU6;{?`+(JC92DVzIK9ndP5Uk9wJEOOn35u_<=`-eyi8^`|Ag zrFyV_y5ZlYN4M@B`{2<8EBRyj(7oPf<+x9@TK(X-ly0(#$5;Ml85-k{Ddq{y@W&{4 zLO=b{gipxb2krBOzV|_`pHSbvW*9_@e(0Mgw4fj6dd@K=o$CkRen6S}<0|lg+V{s0 zdO+v;W7I#O(gTqA0c{?DjG;2nOszzJGa1z%h*Sq?`9P#PK=%h?Tpyt7g8;A5fL?TS6ritgK;gnMym#+X+!Z|g_)D84?*QaY2*;}ODKIFVvdCvJ`|HzD19A@ehH<4 z!^~0fL)Fc9g_ZdCw8Ba%&=$Ly-*4VFJ?$TA=BIlT+a`>TSE2_DNAq*Oo$&{4>=9NkQkycZ^vwB%fb0==5^qx__3=y3H04 z*~hSOk1?AfbY%<%3*cC@F~sn(TzuqMR>?LF6UO`|GR8~x6vwu#+z3N#?F$tls)Nc~zj<2+75=!_=i6_H9ztX75 zsP0#KqD9>)sOMMOJcZ-ZOf}OXgmhV`%~bUDEgC%4^i8Gy(nfnmY1fR+xAeG|zeN#K z&3agY~#sIUU4==)6 zn-(Ri58UO%>e;2gAM86rivrNp59w_HZmAzq?%BxcAp*Yx=V6^+^7&%bAoYatb0U{>_x1p@)IVpD>q zm>2%L&?{X+h0t+rb$%`A@P%I4CDeYd2@x7FCF2Q8E6mNWRD0Zk>5epA$ zgqr0bCWm6K{Xm~X(H0+Qz*5YOAL#i~++Td4_RBEPKhTY3$m|2vU5?KAK--pM-v21-9MukbYSDN)9@`j?*S|#PLekH~91Ez*p>+swXUVV_ZZ9}Ucq_^8()IqAZ9W%^9+PNK?2gzp#T;?N<-+@v;(y<-zhmTZr zC+4@q^kpZ0UaZ+^=J7tPZAd}N@yVR=G-Y%b#}CF5lD@mpD+_7kE;Qys%Cs9^{17 z05);nzTW)?_2|@dz(7Cs9D@r;MXrNzTrMReg&o9j=b}nbf(WznKjkfsFbg9Z<@JIn zcgU;_fugc|gwU2lXv3W};;31HnjXd_dnffj44>XfcaNI+=*3~=ypw#6FzS2+qhu$I zJ_a{Ac?3?rlM)<7rkuVb1kzN7fO@8c@%+0C$%u)3P_HhSDR)w%W0(&(vC19kieuw_ z4B@?0?l>&kOVf_SdpMMht{q1Y|3~FdU^xFr+fQH!{YTy>%|V|3sirLXJ`NVEEy_si zPU1%7Kgx5;+ze455in(Zk3 zJbd5_#kpWk_hhBnNj$Z&r!L?Yn{#aGv5RV7#E$|_6M)h>{13O4%gOj3hRbqlcnNtd zr=?fTqIBmHepN50M3>>Q%W2AGa|6VMYxqSt{emrODTrBlU2!L+$J4Oi#5ri7R7iPckRXDnvVM; z%3XSQ+YEu2bjR%VPnmh|n)wlfOdCQVzwC7BEI-{|H8{N(sX9Uhw9Jn@SKZ;Y1pq>InsTON-vTE0=~ z=Vm2Z{RkJOZxr#|tfNd#i!tkY9-9QQ`UP&C=RCpS`9>Zuaa;QMGj41< zq>V~N1)iFzbnXr6dD7Bil$yFfh39gCnd{p<|LLh&2WeqZ3tIFH?iE4h`dB3?#>x z4H*vs8%98&&{8z$h3Sj$D7hsB3ax-q-q*nWR+pE!p9`TqFX1U6Ijrh4*G&YT{ufqq`l0 z;?eB4W&+UAx44H1p=WO~eDSjJJ5I6Z9iEwnP+|iY8MZ~m)%%FG+MXt=6hbpU;=a4S ziFr1J&U{3%hfLfPgwPigS4N(rQc?#CUBuRxr^^;5G&ZyxRg1#NWotV@z|d(Bu(2s^ zdXH-<8(InimZeS<|Bt08n3Q%=kq_`|Hnb$|{D3ELyC~AbN=uJFU@AljAJP15YdHv* zSq1_Ysyf2BtW@k1T6;d7{)8WHlktJ?t3RPwMgbTqZGM=oXxnGlc!REeHXC~0P)*)p zxFo!B4E40?L7eoo{2+39S-m0r(^xt2N`O@d-w&m+icp2PR^#|wK*{x<(Prc;LUU4B zDJesxEl@=AQr^4rpIB;@f#>ZLaPGxif>gx#FqHhSRJu;6B!Au;?&n0 z?XZ&edRu`I^%7VEbbQmTRKUx^O?m<=mx@SdRipL^tupF+1}iHaO33_WLTdt0pF~y* zo$l!lo$f;-Hr&Vn!v`nkG7crS20?d8Mk_CMOk$P9_eDvpP97^ml3HDK!;CuMNn_)_ zO-+-MTICSGER%(0UP-MQ`0ks`8U|4@v*k;-lX32gG9&l4$+-;!lUqFy{Uf0|~gClGqA6$CoWmquo^ zGT}RVtrEn8j8+Biu-sH9la=b9UYnT7N&_YIn%!Xq$tN=xj$TtaqQj~|JkkPvRfO7Q zv3!BhVSTuVS|EU4$ws5Ia%Sk7fgWopVK%EZM9*wi6Np3EtdS69vs*zB|AX*cqekA- z`}SYfX=eoAw4S#=@q<+8>? z_~o`1dpr#(Vl|?Md8`H?vx=aHpXae!;CscqRsh7syw*yH!TGE=5Z{YgW3U#QbJM+lfU z5CY~@hkzy7=wMYV^*`o(sS0;zb4EhIk_z;(8XQTP)0K8t$MnoL)u3`U;Gk?+7y4Pl zs_~C?R1;SXwyr3Bt!cIS$HaZLtnMnO%4p)C+EyZbhn;D#eUs&a>z!?9HO3*KR#tpp zQO9bgy|EDGtqWg?q&n>}qQmRLu1Gr6-l{?k>S0!jq*@)UIwAGZt;nGR{9r+St0%s< z@w1xX5Fl$XzF%x$orc)b5e2+$hzT;1x;L^;LHIVdE<(g;Vr}(|RDF~ypBFtC_BKDg zYJz@=q}olb^AIDuT0Ln}Gi!xMlVDg6KtnCn9ovj@Z5uL5Tf9f%%i&YUZs7F;w*wyNV@2Jdc z2-IT&z3GOlDc9u;1gg=SCik#9!+z8yuCgEXSVfC_TD9>Vb!kTBdvT7a5CNesji_gD zOn>WXW^c?V>uG;)Oqm=?NpE_iRuSaqkBfT*J@iNS@qO=Ph%$q4`}?bpH3{Eu4}w#C z?2E}cg4*@7$TLFq#=RO|G^WP>9F(=c)gLj`-`c2S9@X-q^ON@%p)mt6D@4$~0ai~P z6Tc3AkgeaJn_3Kn4{_1$$a@eh;}Sz5P}m^|l+%#j4z{xYQ&{C8xZUBxT9GltD*R80 z?T6yB#wBK^MMJGz{}lUaDEjyk`3*zGFVU4@@WxA2akw=f;?{6$CPa@B7?zjl;utG) z$VklSm#FVZD*~e6D0tWt`I1sI=RPUG;v7{DBU0Der z2$Yna_KicM?WXX_R(WbT9%FGg4IU4F*iE;_V}jw5CdApT3NgFtLb6S?%6c++(bbE# zoOqj-CQY;&X*8ja7acx@@swZ^#uJiH!gXUePLajf-Aym2;^t|@WUC0iqsW2U`n(6| z!FbHGU8dmLc8bPMu^RuAS=_0ZlQ^@gG;=DtgLBMAXQx_eb+%(YPEdzgR&Clf4d?`2 zo@UkaJfX~xvQ zgxj8=HZ#$CC+Ou&TsJuP{{Q3{Jpk>=Id+0TW)uF-hdR!-sv-s%)g*H^2IISsAk0Zy z=HT-Gj;_za#p4}~3c>}y>s<8yJK8wc8WHE6GCSjp5IpQi;L1D03;)n7@^h*eMRq$< zlePz9aH^a}>eS73!u%8?81w!HsvV5mfeo}Y7!wCS80qoPGm+6DmOo-b=Uc6)I-w=_ zSwtDSNSIbPQ0{rCH$Mz1N^j=j4vn)cM)l`g_3<6aLbO(O8nwV`;(1D0w{o`^9WdV( zr$h^_Ea=4|3#|@uIHo^tkW}|xS%~Z7De_r_IrUTPb`g z2H#f7xXkMQx96Q+hNj}oe6fp=Rrl{vb#giCy_FV+p`U86fLm{+^(!!IZ>2t~tlZRo zrPUPQPp`u4_I4#4l?$p)1H#xsB6#qfT64?k(D*(JAkcN=!9HPa&h!x0ZB_S2o=zv9(G5C<;#ThX}~Hg z1Z-`@AWVQ)GX|>##A?f6t$@kx7_1i%tv!Q{0(x~|uo=Lut1{7#$re#b(}}@00riG4 z*dgH2a0a^s*v+w50e&BE>loHJ-~eu)2q)yKF$K}?AlG3bqXRg>Q32h985|dIVF81a z0(@37I4z*dS_c0Kcp1*%tbo*e8JuU}Rvj7Z7?X>lGVct7O9IrZh3Kd&0_?h7SKx}u zqX%{!hsG@jaAof}Ay;YaM~>)w3cOVMkFeb$fcv2$Zxp$9k5!&HfOB}}g!m0aBzPep z*BjPEhxY?wXgu&)M5f`Q%ef4O)aCq z5@kr^6mHJ7yhFIfDmo#=b95C2E~o^pQ!%LS0M@VRgk1S>Fb#~XBcP`S^#wH688mW$ z+sU%2L!3-n2(UA0tpLAO_^}li(@sF_)(rd{z-D!FLav=nIJ&EV9Yq-Q2#D*pv&>(N zNiR{kTarN^0jEkZ=r7>227?6T)JcX4xT?Vj0socZJVpyh?$%_ikfRz*5HPU*S1>o&gtcXz)fr5^bv65Mqz2C;|4U`Y6C2 zM_&Zkx^U=O3{cpFiVU3(bCvH`}QJ&rs@#rB$b0_<^=K!81t5(`MKM@=#T z_Ap8*z#c|E0`xF)yV41<$5BQB_BhHSz#d201=!;#mjHVh|xVb zfIVuO39v^^O9A$%X(PZMHSHPj{g*v#I*N)tY`O@rhfQ|@_NeJ8z#cXJ0_;)KPk=pY z1`1H4#*OhiM2J0Xh6}KV%_sr(uo>147nRHt z7_1a9dLn~W0=7?Lutq?PDGb&LC^MD81_7C;GuR}+-ESt7a3RHJG1w}gc>sg$0zBt1 z*eRgPTn4)Z#0p}tSHLw5_6rCl);Q=Ef-|$#WPV7%ss#*=2spTi!7%~Z@hUcwoDiVi z%?3Cn!0wm-6i_c_yS}bujdKogb5C7xf=>J;1um-u*!y2)dDQ`|f87bWhOXi0n*!7e z+|a)*fHb%(;F<>a8SwZkzmC&96cxM_rZRafK%E}|@KnGw4W0|IG=SEKQTEI9B z-U>JZpvIpeB=;sxV+qKxlfiodPc--_VBRj)_$(mDJ_cU}#NE%}yMO~4{8YfrjlAg~ zt3<;qv%1yiL@rc*0Ofrf}nkx)4 z3-G(jAgh1_*BE3Mu>3lMoC4H~@u+NW0e0>4DZuZw<3t7i{a?@_?AnE#kSq2*&Y_5a z^iLTS6QGVQJ{`z;7SN0dtl%ue zU_&+rGX;#!&LBWQ%p45n2yolA4^jks!bNl4*DB-yc82**$h9IT=de(KIuQd_EEcd* zgHQo#-vMYW6A-1r3IMk*w6HI$go(<~LJU?5$W@rZS^<}fFjz0Zrxb&Y0w!p%S-@Kj zwg|YN!_8^7E21|tz!re8b(aIU_IsR=E4z-~FQA165emp2g2>>AXxK@P3$T-%;v{_i zQ~M4g&3{fNT{vsl(urfC6}v3Yk7pK<%;MQoASuJhy>c54V>RuTCgI z{A&Rv+A(-5V6O&-fS&DH!xG@tfx&wL12p(3pj2np_{`&v?NVo&pxm#bG63&-0elyb zwHt$<0?v155Dl-%>$)ZPXAoV0TZ5PaUT6?o0e-{Xz408U@lxczY9O-@gZK_$XGq|L zoan>~+*A4`up=RL#vr2r zYXpPL0-lXzkX69k(G0Q+xIUgiP66vC0O0puZXpFHvPxb7>I@ecl3xMd<6-e+2EGp9 z$`*D)uB1~rx~PCYGZ+*Xpth|*jwK!7c1_cyv_tsB7pRmKa3h4%lov359)pSk)Q%NM zQdxlC0tQtDs8e8|QC&czWenUkh3wa)wg9!41=7?NVAsBZ0=&1xGHfdWjm8e(=4k4K zT)%K$45FJ0P$$Fyv=lJky^cw1A+y#qXe;3M1_tc~G~C3%Pk`DU1G#n*kYOu>E&`Hm zW6(`N#C8Tf=Bn!-S7pu)COt((ZJB{wdke5D+gE{L<@qsj+zd1ZIDl(E$O-XjG>9G| zVC+HGA0{A1M09@sK0?U$2v!*-pwS@)ZUG-O7%QOtVb&Ng;Isx41q?XK8j}S)KgM7x zfLm8({&6PLMWxC~1~Uari)0WW;JpTO1hhNL8i5L!s(n)7=ub32hyrR$4_8+`*UWbS z8Vuuap$gts@kMY3C=x7DfM+Lra$4p9Zs8S9$Tji;ml!6XEl%4(2CD_A<97ho3YdL^ z!FmU{UHNY^+2|10+gl7a3rKT^!4?6L8f+6V3|@{aXS2)Ou(!6^ZaKQlNZAg22ZlSm=UH8>}r z@mJQkARy*92LBUq;yZ)O0%reYa8Rr z9EZVu0mI@mcqrhJ29E{Q^=6Hy0jth19I_Q^0Bsq6IpZm-bdu&cWk+nk2QP()&Eb5aI2@4cy%BXa!4=WQf>wr1*q*_ppjXCon2E_!_FU_EY zfL9unvH|Kpy$q|A5fzWJ49W=@qCo`#>R>1sQb~Ya*{TZcQMJd}P)ehQ1Gx6JoRF(P zC62BWsIGsUfT}a8Co26LF=!y5Ofv?J6j-LpxLk+l_ofQ)4koGFutp09aG|Z7kn2)g zj&36Wd))B#ubq&3eyq|#fI6BA_2?+z$Y2JY1*pw$pwU&peGR$`s5*=_TmsawRY=kc zz^yG;yW)WOi^`Qz4EhQfGMYhu0kibb87Sb~7}gjpVAog%Lj{x>&tSNK;uCZxBZb_U zz$&8!teeDOjDS9q8H^L)n!;d$fXUMsOcJnnI)fw`<$ea6euIMy!Ud>( zfskaYfDVTlY!{$T0E5O(0q%ZBnd}x4>m-A{0wN=*Fx9D{=bJT5XgB%ta47#tCB z^Adw&0@OZ3Fyw?=h&^~uE5N%01zcm5NC)s}KIep7&2Dn^1p#UsB4qhL0mtt$xGaF~ zF}NzA$9?Vp*M+Fvh>*!m0k#!)6u7Qtoo>%qiGHHMNu_^E>pyn@>%VkD zZr9$IoZoAQxXQg^@K(Uc*9;5+(cUny1PuPg;Jttkp8Oc~qkxdO3_c5}8lQpttB{%A zOuh>UOvvD;fEI}vM8mEzx^APAF^Dd}u52s?!c^^#;S>h6r>6tB_Hmt%E0sGn$Hy1a z*oQ#^0khIDNF-obK?X?#4Dn@Y?12=J`Spqzl2^%ztT zppJ<{9+d=yHDORiK(gixsyV>z3TVNkhC^JPTQR65;71z35GCe_Ztjj&_Td!4LS-)K9V&$ z+W_}pB}XyoDk|AWGw3d0?^p&d0b?gK=p}%rGVm9$b2@{*0^-hO&|g5nECt-IfkLv) zW|hGL7Hcq6Kyv(5js6`jV5J5l1r(mg8lwdq(O`@KwXGKN7^i`H{&ruJ38JEo+Jnj@ z0Y5dEB0!zA2aRb0DlTF$LqMbkvjh}a%o?)=%vAut|K|!(XYC=^AOV}PGz%a^Kts$G z0P_U&!Ae_z1p+GIX)3@X0V~%qSR&v6mQX`uDFc4~?Ow-Zxu~c^`=GK?z_Wb}Rtd1@ z)wK%z;>!~Lyg-r-4&aG)lM`|^#@h&p4i~Twzj^`OTZR0>4hbOJ1*kJ}0Cp)L$Kn9& zbpV&K-wC-&Twwi!0@C8A74#1Yh=%_F0geb5?S8=In2_g>8JtkyqpH}~wfs=#v;u$r zMHuM-w&I)${ZRTicTkma(E%KN$qBjEVNVf6U$FtkpPk^kBHL6B-Uh4OascOW#|gRC zMRD{!0qWF3WcfhAq0bB+2~hi&LF0*l;pj7fXMFwPLchd{rN|3Wv8{Nm0NMl_Y{q4c zcMf2)Oef^3=FQPj0z48j_#hxjA_kuX6i5<_fB$?DQY|T~d=pSL8G|1JHYI29OMq=w zbnL+73RHG}NWmJh9Kd$QaYC-yJ{;|(fg1lo8JWZrl@*y7cnjE?nL$DUhqEw9EFe}k z21x~E&%q$MfFl~D6rc`oRO3Ijkn4(Iv9W+QxfrAqFeD#?3<7!=WROXKRhU5*0ac1J z$R;3JaRxaU@c1`MGsz_?6UsBlBOpg52KfZ!uF9Z*fQvO4_zIX?i$P%lcE1!;0F$yl zq)M&-DCrRHsnSl!Rj~o*P*%Xsh78IJNZ*)2MFEYQGN>#-9fXAxJIm0V2P zipup~4B87&r((ivKLNH`ofW_wrVhqrjcyKLJ9{`GSNTyK-BUpR(F(X-y@eETvq~QU z^~W*jC%`UapaKU}p(AiC7|a^t04{Wx6LOWC#L*)ZxTf@*&SWs!0o~o|&7Cn$&^2Zj z$Bz?mY7T=53WTa0ZUi%!>;NutsuOZ`pU2VD1vFm3V5Wc;OBe)bpvHgyWlZLXirQ8W zW(5j3vW7vh0NaXr3Y=ADEnUwV3mw2_Ep|e#jJr8HRKW1P43@ctblcBlg@8Q~48jB? zKFVOVfPiBR)(S{=g28$LDNZriD8Tm&gUt?byONw`vc)01gCA>21f-%++lEBfO(I>NdYAvGB_|zqgj^FWj(#Fw=6eRu1Q;I}yby5pBZF50!ag&2BfvfQ zE0cFZY%44UZYi^h;D|n$^}zvb)+Zpez!9J`=}h z6OhoCK@I_hN;AkM;B*BBc?4vr#vq@7w>23Q5MW#3E~LmZWtaUpyQl-$tm00{wWS{C zP*T9t#tcdec-NdkSphLxF(|LVdsT!_I|h{;(7lAZ{#S8=uBz=hzM6o_JsH$c;HS!= z`A`P69l%D`bwW;b0|kyM{oPu>u>)AYsS|QNau4JDnhQ~zNW!L;3gAZ`-Y(zD_y27a zn5zKxkyIM(9l%-oIU!fVNgUltf$vJcAr1kCepd&ues?>B`Nxj$smM~50LKEW1pW@- z1bv+lRvU11e*x|0GZ-k~-a-b01!P>sV5k7Ua0bIe)bl4UwB0@?BbCYrWyLI$f!hIW z#aJigx^RS}#|x-;n!!W?>KI~FW3qsiml#YHkmzb`zW<*tB;E~HnJK{k7J~o*^PVu6 zBjD#N27wCTcMsmp#lgBLH$(wb0sDXW`3~TkEYu-2|M*044vU?5*Z;mV2o*3WI%l~| zz=AjoRw!^)l~Eu*gH;aTGS)aD*N{XUy-t8{N(LLac{ztg8JKJml_8lKgbT=?gTYn- zOL8&TE}%|c20InFt<17l6zp*To3&4c9;xWy0&Y%l&>=eg&r}eAz-QowFOM(a$WT_ zQ1hQ{XhTuarn#C3(3TC#>1r;>wyl)_+qSj>Y}+~rux;xk0Jib`-&K(i)n@aCc&ZBa zP++G5+23+~dO3g_(ccNV2EX9wegdAoVlYs^r`HUI2q^W2!Eg;!{nP6*Mv01D=okTZ zG2;c;#Y_@l7c*6WUCaytb}0c0>{V48t%_C8e;vYg3vxm_UY+iU1b8%xy9>!``mp#rektZK~V0$P4x5GG*kCq4hK5rREoRhsnzu<@({n*`WaY*Ao?GHX_V zm(tiS0K3mBuuFj(O5?&P?$y1L$JkGt=>byfU$|~|xrZc^sAZhVQ3bxJOy(uwOinm} zYk$fKx#IrfEdLX5(~I@b322g#!9@W(6MORfe_04Nu2q&_6JQs5Q-NK|io~;7=19GzIe@cayt32@ghz$B%Rp#>TE2zXeCK{^4yiZIA1AiOw(ECNz#v$6}Y z?aU>>wk)q(h;3Q{0k&m@1lX1p6<}LdLV#^qX#uun`TF~vvU7wAw;t+1a zYEDRP;j3D>rhq#Y7}OE4wIYN10&Z7k&`3awdJLKh_)?oei+O7NDKlKy%U2cCT2!!; zuLA7^*mn9U5TTN^Ez6GHSu||Rx(To?a|y65>n)&#TbJ8ch;7;c1^ANCp+6fkSil;6 zjTq(tZmFV@C$l1T58s&J=L08*9uK z@V+O5Kmo5c2oW&epEc$S7~${9*S|$V?8=4;XrXJrTtM7GoJp8~=o+jMkWQPrUVwd3 z-6X(n!7Tu8-N<$uZ5I{0jdlsJn`o~9yNM16u$$1J|T zRGQ6a5Gg8XBh{Vf1=ua}KLK`&ToGWm$aMjBi`-HG|Mf<5WgW_y-evz+gnj7#zwwV0 z;EAu_R8H{J0j&Sr38`(7m65LmU{hoT-U_fUfu?{yqgliKUWh%peiD#uJga;aFlqvW z9|B0v$I<3Hc4F&fRc;Icw&Ae_*yVaDfKI{q&$F1*#J5Q_SN%{12_3+dP3(kRYo~K` zG6D8AF{OZq`XcEgVAK*$l1_l_2^r_B`)~FQyW_KnifvYQ0fTfVxdc?#cIFje8(u(w zZAc*jwjo6Yxb0j^D6&%dM)~t>YiS2??aMkL*N{~lT|oeLc2-SZSwP<945|rOu0c%! z*y~v(sRQ8Fg?7`VzNl1O&nk@s{94VRses9A7_<;jd>w<<0@Rk!D5jl&wVN6E3Gm*a z$6se5<2SHMHvwNXa0ytui8Xo)Sf+FBD`&kz~Ei1F-5>Lon*Rzwuf0`mVi<2eN5&E`LLfskO1sIty+-; z`_j#Lclkzv5Nhl8{q5_R#_`5!*rSr0*Y(L+$>=IaZa*T zK;knDb_gh-lk65?JNG^TZrh&^3bFn9umIbij|s5-`J@2bpU()e-TABl+np~6usiS4 z@BX#@`KqYc{(M7#?a#Lb*#3M^fVXzmhXQOzdm_Mg=jRS^yKF~$G&44oM_mYRutg2bFHGt zQ<7fH7L*r~vGqt~5>v*p}Lhe}9}7 zGFPia3b1Q`UVvTS{|T_`dqsd<-|GVG`rZ;?SNAS}TQ{;@-v^>%*Y~jiyS~o^*!6uW zz^?Bb0d{>20jRHPfG7cTecVhwD#9~H>5>e-IDmWmn-g*-l%E3Tr)PbSg-!->GckxM zpm`PsaRk)YAT9&`|6@~jR`C{grgXgTSP!o4T>w^=2l;$ zNhwk3QG(Ny6;N3BO9cUTM^zSJ_e3=Tc2Cq4VE05F0q6<#|N27go@gY%?un)X?4D>L z!0w6G0_>h>C&2CrKLK`6bOvy1w^Ln#HtHrSc2Bqj*ger(fZY>)1=u|?K!Dv7g9X?< zF-$kTN)#0kmbO7rII3c(5 z7-_CUTq`Sbf?xr&DlwQRpmQw-3k6{3eARqQ1hml_%LHHteWkHdz>(Sv+^dCPD}AN1 zPJnIsMgg{E;R0;Swh6E;+bO`dY>xojvi&x|_0KjfLR4(ijtHJRcqrp7^uQYfl z;DEOCi2&R1=K|cet*;c>t?CxDBOCHofvpN8KFz?CG`6Yl1=yy35@4J9Re){k4*|BR z(cFuia$gPNVq!Re+ccIFa!qf|(VhZqJL3s3yRt?C0XQH)IaFc+I37TOWC9ZP(&I0s zkda!&N5CU(RyqN;of#G2_Q`vIbIqc_Db-*>yBTDcBzC#E1SIOk8hIC~`!8;FyW9ez zVwYP;0j%A}AGuRFO;HDM`xJLV>YxEtw^9POjABq$z*-F|2*7~@eEq2`B$+Ownt-<& z)D&P_QAdDXZhZlExs3$a~dQx@=jGYC(a#!N;?N|Wji<_*P3Y@ z-AO>?bOv1oWYih_b$)@$v8QP0BHS*2A$G3)1lYL_RDl0m`lkOB8{z;qYnT&q{)rnY z!2TzWot!fWoX2*K6Aichchy88xpWy*1la$?O&4HWF-w4*>l^`g9zhDQOX7S4ReN#( z8$KT)w~p_>lH(US@h)1+AXLC2ox^egS;JW)OaP8hP-UzU5V4)XdI9#p*d$=(Zax0C zD8hru_b8{?E-H30y9C(9>=j@ab3pQFrzfmK4&cfjaY8P8ydG!3_kVVwr$oh`^8OQ$ z?IfFZPC%?v3@!@r(uH0Y&`g7C0&KHx3Q(EmL5tlHV&{5afSv0j22{o~suUmC&=y;Y zV3mo1H(FR)e9~fx7I;TTCBTbKO5i0aC7x?RTI|>2vKE`QIIhM2RD&VK zCN1y^l?vgN8zrK&IHm>OVN&p>7I-a6h3;!{Pm4oZ+|eRfiy$ptYk}94RJvzcTvQ8I z5qztIcr8qY_Gl5V#W^jmYjIKwytkz^e`%rKW{3$+{_P^~D2xTS?!n2V5Fe+r?NdqSusnGkASC4^co2{Bg- zwQ><5wKNbyEeM2AtLfa3S9MUWFhfYKB!f`Pqaf5GCkVB42|}$%f>6tfAUau6?9*bW7HWY6RMgS{h%Z{Gf79Dy!G!wf8C3mm38DVLgHZqCK&XFfAk<$95bDV) zgnGaPp&m3qs9)R=>W2q}x~GMR&_dk`A*3#e5bD|hp{@uJY65K=y$Ch9PjVyn`yj`MeEnUY9RJO1LdX^K|g z^EZz0j`H%_^(V{iwfE0phGfOt7j(=&@Mlc=z_a_klm*9Zic|j*L}jL*jh8b%{3p)u zb^Om@X8M`{RXXz*OJ4MkmOA=ZRAAX7oF~?ta%JtJLK7krEKL2A8GTo=>dC*ccOs~s z`ilh~I?9?S{-*m6Gd*w9NSd7}Dz8_h1bq`nWePldl9kT>P3xXwiiNOj5l&!B991-q zaOR-A7+KXFvNSd7UpV^o18XvDjA8s&jizCtm8j+r=fiF98 zWF|q`ea~ca}Q5jV?VF5MQ^Asgc8I>-%Z^_>S=hI(NbgTITHuJ-uI3u0w z^ni!-$G`Ef6lemhS^k~5FHU(v8R-Ml-DCsb{TZ2_ypufiq=hM?;@N5xp8O%}ef?Xn zO=^wb2ws*dDkJ4dh4PHQSzE=hznN_V>jfzG+PSxjMO1hRP6uj>CEGzx~@3x zV^{@6#X;F-5!{d}I4%`5N>uoO)+BBvVh~LvmWCzJ|lDrp4 z-zE_xi%Is83?f-YvYX@*$+a-U=h$qAA#NUBL5 z>_~@Z5hjxShh!$n>m(^8X(atgCX#$fQchAqa*bpG$px7l?s-}BrD4+5OP|@aB3v)^ zdN;_QhfG(ro^Yu)8t-W2bVcb2)wNb%8n9e_^x8kK>4O#;uUxV8F5IXCjt~u~=$u?r z8)d?Ps&9+*^zx+v)fK51bb2>LzZK~T^rfNIW!DRQ9!2}o`0BFgIbBv*ussd4t`NO2 z?okNd(P)dqjT((AxIE~(Z0nQfh3l53hkdbsw|*eX)6<)7e)@}b%j5iv!PjNet&PWR zXg{mRSv*!$TpMrk*zx^cXz@h*(xbX7Mz7t0s>2l)k0q3@z%o8-ywK_51vSzw9;!k< z-0B&P!q@p$&ya9>KtLOv4_@djmf^eDXJ`9%dO@(qqCc>DI)~B;p_R@AFLVlcxu~0M zo?-sJBQfS-HNwX(Z$4wzXZ?Sp&@X>wj9tS@9FuP|k6+yFm zqGIT$z<>Fs%gz7cNIT)bhMXhXE=%p6^(lSnNn3<4(j&PzG8|&ig$auOi{fG&8#_r+T!tbC>WFdcVH6M(4_B5ZJ(i_Ob@#f=-c5Tf zLUAsx>$MzJxtM-JIWDF_I~C=j02jx+%M>|Ka*JcuN=4D#S}9&Hb9MWI`pe&@a!Aw95gpPlgX4K~o>`98CDxjlLG%`b^m{$Y=UHeD9e& zu(M~%g-N}*BKOXDGA{LHnm36bVF%CV^8+p``UrOK9GeB7ccwiscxQ@$kvmgW8*PD? zyAgAW>a&_|%W8I&7h%F~kW-YvvHgt=xHaV*>ul54 zowxbPH@|ygpKPY-o7nvr9Sur@$+{?!#RKXmN7`sICyx1ewH8Y2>(@$7vAo&1P_=7g zVmH~N+sn!0>pS-}d#L|L7<#jj_pL}GnVVCe)6rZ>ZK?5y7*q(GYf*VPzy999zBDfs zvFQjHv<_CvOxMRN+V}#I3_4hUCC*9{NRcoT5rgvKV9ey2O(dV)sy}JA-J)J&auG48 z>-(+b6h%2+#!q>7zXaL&N&Vl(+GzGEcDc~QAYE5<9{#q7$>yYn7Q2;Zr(*U2gbdPQ z|H@=8C6e8<8m11k(d1REJcWos8Z1r?a*72lzArI%sMuYT*0HE~A5P zDvE{$C&yypy=jnB9J!3`^|GYMDE+U?6Qbqu!wqG_95gQ$H(-Uy)|^2timt)@l4DlQ z)Ji)|MDi~W8|p&xXeuqv!X%QdAJgCt$#kZT{9*RUu|;i^1HVU(9g0#EVNn!iRg?|) zMeg^tB3y&cCi)`UUY$Q%_stz_kq)26=k4ap^ijh4VFQEa!c0m|au;bv^{^5!q!heHf6)@gl8Ux1-rW#mnFdazu zCY1Tf&0hpti|C_N!6Sq1HGT_o45kHBh~$Kqn#-~iaZT}y3r`BRw zCzuLhJ7JJh?8(wcoocr0oz7(X$(iNNmx2Rn8A{B9Nrbo~hn;F()=@hyWmWT_uI7JI z%UyUk7>iR*5wz9^_mJd8ubTDJj8`IL{Qc(B(T>lk*FqRHu(iWtI5RMI2I3M1g3FtL|}@8 z69QAoH;Sgg>VRWm@Hk-l_DY_@O;;6dguwwfFM+cG(;whyz;pvv2CW1S1E#>+iYCFe zfMc<6Ct%WkP}J_eq9hm-7_9@uzhFtg9h=}qz!dtEA_vR{IQEr6vUh%qJI2vOS8Wem z1K4k%J=_799=4Gi1_2yf2V(%yB*S*K{K=6=>xaUQEPPJ!FKMHd0V*~)NYOdu;&Z=& z!HP=S=oh5jb89ZL>zO8XQ>29}+7O}W*GO-|l^w6Q$d1>44Vde=OCP5%(&9OzeteRm zdq{-m*sv5u{^>+AJ^9)9BdxUbDcVg&#Grr-MN!j;WJlqRZ$@_kvTRB zDacGmkbBHD2T8}`n!A3Um-b-uOSBp++#?Y(C_7Ej`{NYtc~MacQh~YO$Z3kYy`t#3 z8AR?Oz9#LEMJsdv9AnbH>a87e?;VdwY#=QzizsBTiv8}_-!f^RCYn!B=xyYqawcmA z5>dqocWAjui|E=BLq@yBqIo^{rwrN)PDrp-P^PN=ac?G#j_XiM{uV zb?xHXuwz?YUG+_JGUM*^`+s;hJRjiXJ}0?y-7U$flW{jo{^CxSJZoi4O6e{G6T8QE zDjD0gbGPU&NnPT~l#MHw)H%9qe3!&-rQ)JvI(LgnEEk(pDk&zebLUdAGp=`)dmC-7 z$*r-{;pDfsVS)nkY_GXcA zP;jG&zX;XQXph>0XJx9o^iE3b+dHvy$u9i{^-Z0X838Vqij3fMi|IjD@K7mgT^jUn z?G6XgSDi}!U_>y;s8(P>yG^_^F*G}Bqy*Kq@wqNH7X>!&Djvlz?Be7symN?!o5gc^ z;k>6uFlPgsn^|nAl?U7(*S08ssYDdtq=*OYPy9UA2sXAFE8|y2n0Z&2iO=a`;0tca z_(KuJLHeo!DWBM-NRZOUb3kI>UbFa^+tvBRh$8%!L^sG9-^anHcC~VDKGALC&wDg{ zvO5IiRUhrgZ;dL#xp>V~7wX#$4yjwXi4Iw>reGb+KoeFF%4glhdK$n*ZIFRicMRh@ zM_52g%Qk+<)H;x%a)xlT!00PO%>3eFb}p2^=+g1_a4DbQF2Y~#>Ee&OO?-BOj(_Kh zY5-Q^=>_TrzQ!N|n}&2L4^ za796rQq7Cbb_12 zEx#$T2*0G5jiWmD?_R0c|A|)ZkL2h#D7AObqyYnGq2Trfm52r$X9v!*g=)T8oMT3&6jBovOyJ^} zwHtn4J~tr(nGuaP4qn@_C{B7B zEPRcg#et^H*igQ6mndL-s#Nin+*Uqilmz)6r>-}zKu+F#6Uy`$&ImR?YH#FA_Xq|l z(BnYe>p`b`^Y#3Sh)|Hxah3$QZWJlUzwQzCBhqQEtnOx+J-jy_T~LM}*y~1xA32H= zi2E^Cf*I|{>O{z*{k3Baz?L^ZjEo}HZ-t36P1z%MnD~SU8%cc-bF)ZsI$Dy^4512R zBQh#FrbFq({YYVSR2crYj!P~hPTw#2&wr-)jpD-2%iOZ$Uy1Z;UO16v6X|KYQbbxw zBvoj6BIOY&^e;g=NugzQPmbLn(z$Yibe~A$+qNXfP7|rhm)1l&NThqc+Y#xl zBpqwNCQA?>kb}Vw+mnO86N!5(NV|wM{b@&X>$-)0fF|~#veTejx9EB5jQvMWiJ}Dt0J?NSl$AKE-e1{Lw_bNe<52HjYT2h_vIF zAiXEji)s_dv9&~6?c#}ak4S@*^N6&JNa2x!^nyr#)LTG~-6B##m32hggQavowP`&O z7m$g(U~tuABhw^a1S~5m`J??_7Z71k2~|8MA}QFaYF^^D3NLmy-tp;CsOUBw}^C+NDC*V-y!05A|Bgvk4XE7H0FjNog&iJ zArHy13q+b|dPJn#L^|=CAbls&p-+NziAXaaKPEMEh}0kC6X`jTYU-b+6LBUH$3{OT z5+u^*QE!NJhe)lfy(7|Mq2?|@$|aJKdryw7Cek&vAnhblSeMKTcwjq2q`fnT=VECS zkxDhGeJLG_3yApT%H~U0`h!T9ZkR4(=_!%sRUda3OZi0d>nuozh-4oed=DRcNTh8A z`9wNKr1|=;_wlh0L~3^XIgwrwsYZuIzsu6m!QDjs&+XO^aMeGF)Xq=+5KCu?1Ra8O zgh*4z3?#=2h*U1P%p+X$1d*z8kBGF2NIT8HFC^l; zF6Q^R>INd6Jl6b!j6XfFF1MfmK1|PNq$>HcsVYA_KTMw$#xGjv=2KGRM6)G)&_V~l ze2toqoTmipP1W@LOrYg+W+<|^Hr4QXyG;D|)MS1~W<5S(aBKd`ugQGVWsUeo+Z6o4 zK{~$sHYfjmiie*C)V?-bhxFoSFDmw5^<4b+Ar1Mzwwd{)A(7&&$XQjn+JpQl1vky`()4?z9qR$?#_eZdA(2+Q_0K|=gI(6#Vw`XdnPEjetP}40x zdR#Fgj@SVAdJL^6v&QN9g7E?T@R8X(*r4WfK?v@fw~Q=-Fz^3p_;A!xqHCAoF0|%Q z@H-~d;5ze8TP^-vx(!u5&1j{FKS1K|(`!Am4d- z1Q=annE`A%6jPFKJSG}A9z+J9wM^FJ%6#o{<@q;LOM;AMC3XDP8PR;k7$sMQA3d%l zK4(&+l|eibqCmo`2tWSy)Y`l*vmEW>Eq`EKY3{)f=0|h1RL!~k_6arlY2z#W&9s5Y zK6u8&=KRqCKYZd6Qh7(F8hJ(ntWT9nK4xZhq{EjVI>hlE__fn(^R3p^;H%EY3O+#x zCn)e#iUEIkkbgL;I^TNASV>m$BOwxTpkX??%%zBVHNM*~y-@pEh`k;M7&y42wT?fy zune+8O>AvGep)OaJF^->_>YwDH7$`C35V6+7zAV`6R8H=m)73P%S-t#d#a778|c(=5S_vD!Qp7UxToVBRe zX8itNL;0q2Be;7=>PfN_r}w6?YD%0m%TO5TQNo)=Fa96-kNII23Jdom;V#X0D_E=M9l8x0OG(DnfzQG38HR+{D*~R{&-F-Uv_E@?0rNYFKckyI8-Sd zRE_QFPm&KOUeb^#k~?^b<_y2cW|Rpq>=h#dH^3ctu*(V-?+9sZ(ENU z$pAv zSP#5P!o9^HTN(L|8*5Y;UpanQw zqvU*t!S(nWJ2lv2sK4j`NZWs4@~TBx>zRCmUFA85GSI=Bmnf0j)F{oZMDaT)Bg^vc zm1U8;u>bLL40kskEFP2H8}zfklY?mcJ1si&2R$^=cgTVw8n4k%1HRh9R8gr?{Dr+~ zeA$B|`N{`If?VZ01^@H@RIo4*6|U_~<+r0F_4bYA&+Q!vMh(tZ@VWa2@rU=PgB;~M z8De^juY70}i2k@j!Ph%Fl;3}NG=KEiXnyaZaeVD#qd<-d;nX?uCs68_Dfrrlhw_yU zj}9yw+o?=cO5g6tfPeB$j*sNepP0mdI^T`ocYGegEZExSq>iVrXA@m4_8iyp+$Cz+O~9z2D`}ULM0OA^ z!SB454i+~4UB<^>AIulKF^u2z+Xzt6fEvYLPX$5R-{t(Ns{{Fn>mxv4ytl~B!F-wD zhJ%hqbOcr5tvA!a-B95OSBf{>a&ag5MYm$OZ1SHc|F82GZx!R-kn&f)#O;pUS$@jx z%G~~~r*04A{{3ZUpjo8}}jur+DU+wc1cA!#QuYWl%5`Z>-W4Mg~kRdv5&G|k_0Dl?dyr|(hBt(iB2$k zw?zEw;_yrhQD?7m;6rl}4V!)mqUFqL65w-PMU7~%MD`Hvro~UaM0(2Iks>Ndl}i{T zx*kJ`=aPz6?EG@$4 zB9&OEaP5_75{tXNTFqlHF>2cooC_Wr3i37@gQ51lXtTfuT=^&(UkE@O{Ns~o7sJvS zU`jBG6HhgPOSdCKV6Is_PcSz~vxzfUIUz(j9Ag(BVGNvxfFPW8icv<9n29JNp2p&@ z#MQDsz+7qQPayYbU@Z8x(oi>eA1-ED)_b91z|In46@zKK0MT$(g!l|qEG1g}jK*)T zD&n_lLEXNM#qDT?o6W?dB*NaFt;MBjaZ`J-ndZgqj^g%oZ{059bu6L%F11ZHd$&tQc)vPrz2vKhUV zoz3KJ;t%ox@SOs)u8I3l4yqgCS*)tmxG4@~wnM>f-&{8T9*Y|UbN-emUd7D8@ld>o+9CI` zcp7aqq=jnSF8nqj(7A zknow^4zs_A&obLV^i8};;4u4$gkfY5Lh?H&slm9~YYL`+A1a6E6q2zvvg8BV#UwhA z(_3tUGs7f%=(yD}Lc%zGkq%U#VT>dYc8-()Iwty;lpLkSd(jef9R#iFw6J2FL`~N# z9m-2i(F$cMO4ib4!@FvdHcDZU(xH*$zFa6aYV9Qo&Fv*8=-zi-C9xE&bZ^Ne3TEsp zsV7(*rlv?DX>rQ{2|8ycE6a>j$#9mdAl}s+y7^vKZh-gFB;N(&!Pn`M%XCS1d731P zs**ZOvWqUKD$kXSr%bQpNZ!Z>EU;;{q$t(u$!3X_k`CD>c|nU>C~?qYja`z8 zw3xL=@(0xl?30|Kdj}qrBvEEhk4Q9hZ@_VhjdooAtfV|;Ha=IPpv6U3BnsMW*Kd-G zREzm{Bu2XTSe|4H1)KCha)s{g^F;EQav1tlQb{4`cKog64~lT^lf*kwPW~(zLYry6 zN!I%dbuY-J_bI|5e`x_N9uAalq;7FuZv<$Enu>ZTR^O;Yb!-xmkMVf|q2Bxx_I$%bEinoOGN(qWEnJh?we$<=-#}dvSc&E`hcr1>kbOam~>!M zWlIxKyN2w9Fu$P^o!=VOl#OO^9YoM&sl)0ul%cysWZo#kjgndN35iL!q8 zhO1$NwlXKJz}pVw?JBmF+= zg-rQS%P)np99-XT1GQ`X&wg`g@oq!E&9s=)*l#CYmh^1ps~gtFSNCFDUtPYv-xXSS zXcu4IGD*I=dEI<Skp4 zt)q1-P4w0MYm%?-@+rQ$-820*(Yj&0ukMpszPef2zPe54`0QX_c(#JJ4ow_p|9@YoxZxgcKhl^?)AGvCm-`sKR<@B1t{EW zg1eW~GNJpl-wu`}#|W_DdB2-9FQ!vc@!+D zBM%Y*b-OSPRM(fQB|c|d?bd{(5dxaqa5C;%u z;88z$`oBj2nnm&s@NJ6Rz%m_?#&ZVBa|APsk8ZX`Y^{d-PA;x$_Qk~vgx@;K9g0y$$RB#%)vbKfE?ZBC8|_9%$`t9j>zXwn9s-Ly;)Uxe_Z~FTIB0# z`9xM2?OAy&!`U4`BYhRnuw}9tUbrN`9SDlo4GVz>@5#eh#w!9=IbW)R-5$ta3tGW$ zPvq!%LXsp=PvsW{lR@}UkBL8TR+g9NK z(?$L|SmtNNPCsHTFrk4$2cp#q6O3u0sLYBD5%w8>f$q8_MZQ`nE+4G$TCdMgh1YtG z(iC=?P$7M^VuvtQ!`f37_2t6eC9@Up3=FX}W3Qr?prCD|!t5`EL&IGPEkig!1lF1I zksR}>arQV>Lkl;aQUuc=xN=T$l7`0mT=Zs?pvJ1JidEEai8mDADOmsC6+?d#AX-@LU2!jy)J8C>pBuFUyid2%;epY%lJQ?2?inriIfZ{nycE4dVRY zs|2;1RQFG#hPzYKKaoaIqelLpX;idr=YNwLu2xrnbQ^+XkExsgL0Wv?-G4nTuI}l- zm=@dh@?XGci`uZ?0RI-W&#S}zSI4qNVp;S#1i1`UJ?t+B0UH%MDBs}ULWoJQb%Q@m zx7^Vok6jk1E5J`)~$?SG6KYyKO5bV5VC z)b5?Xk@{}@NB<5C7bO77o3h!l1;yc@@BTArXqtlp+Ok|^^I^$@11`{1%GU>62^7NM zoF_m{2a;FC1H2h{A~IkH%L&emih+ZD_k-Gq;Oy9d71TlvN(J1f5nH)NK#?G!`_!fZ zG47*Fne2oni_G(-hgvV@$z3ToPX5k!luUqT2L46yb`d5_L+9m>q6*vCm@xkN#?_V zvb1>hc>uccL~>(9LBL{~8&My8P8NUkIr+w?fOv{){}!;DHF-c*k-!qLk1Vi(QSj+` zdte(@pF#vSm5!xRF@ZIxT4Txv-esB;D+X?-u5DE%@Emnle4W4}{|k4i8wYwb?nIM7 zZ^rFP2vpFFyWB6(L^ROC?}G!&P-T7^6^ML9lI!J|z@pRzo=Jh;MErAdpf?dO{1WI* z#A#D~#h<79ih(l%>rsU6*?~QnojAStE)y8|-QNuREDRi>5j^>AV_*#?L*d*ah_c`S z6}+@9@Gm+V-rMQ(Y1~03p8_=QeTUVK1@53JDL>(J?Bff8Dr#iuZ`AY}7=1U;n;yd+ z`Alzr7TAD#VZ^Jz`P7DC?*lKe&TlZ`Ti{VTu9XW>rm;-uWL!fyp)k&(+(=2ib1JXW zdE-Wyav(z@2u2$bsq`AHMoFbN8=Pg8|C4O^tBTT_4O^=zz1gt5v2qXP-oKU7d*X1X zz0&KFqFt5VY&hFPxrDkTy^qqH4bA&0z1dK_zcQ5Cq{(2ViYeGD2L1$8zt;!=)^;O8 z!0@~pbz#*}%4RYl;#ZAR`m>ztqM#(Oau_>NS&Aw=WUA7;-mEfR>0NI+XDYqx&7ZQB z-c^8OuF`uU^3Mgz@pRCdo}*k!2dx^bm0GHMw++fkEV}!yRKngnlmm-`SXX!*@QHVp z2Z?u-gBezJ=u)i-e}*9dMBY{2WB>?ys;ml!-cvSaiD}#zlzX6LdF&9i?JhhTpZ-v} zSf3E>VKm%ANJJhZT6u(vuW?A}?#$O7Q>y(H%d6#VOH|2O0mj|mWRqLc^ z`&z5|jYHcL!K#}6Xd7r(J;m}L4%H+%+NOuAE@Qb_300N|Z68Le8sU1S%Bnu#_Qfl! z+T!i_8md0Je{U9m7!u#u!I)O6Y1~Qtj_gwg928+TaIiGG84&V|C=v9oJVFcZEpy00 zLApu~e`}+fz+%6|+p6j?x1H)0OBUhA05!IQ#$8nm*ZTniNDyK#B}tVjM#lNtL$!jz zI0&c*teC8Nz%tXI#`Qqn7?m8{C~wq(HEl-t!vp*Fg;z>Q-p-4qg5l= z$kl@gKZYecyyR41?*)|xG`yiUWCvC#0aAkHvCuJDbyC8egioicZgXgRe7b5I-lok^ zt;5?eUf5opr797MWNxkMc>vnJ+@TsML)*lCs!L3_->Ah1sZ2JLBmngMe=vNRtD1vU z028jNSS}J1$M3$Ty2lv&Jz(q&RY&aH8Mjo6{=Fs;5g5rbO%mcm?y0)qzHi-EvD{7| z7{L!z2bh)b{YX_06M#?Ht^Yh94WR$taya>Wz*f&yeK3JzFI7tzF?PIGxtTq%_l>G5 z?*HpM70b;NqC?3Ks+KIN?QEg05Aw?sVi{^p-#X-p|2Kj-I zCJP#Zt@2JD#FCUhfTH|^Ub4i-4}?8|K?5+AsGuN*^aMYE`ly3q3ul!ox}e#Z%GHn{ zmO1JN6|+8QO5v!~G6nU(R5nvi8Xm;3fc;yn94a29!mE+o$RL)tF@NY(5EYb*feyt6u?#OifIi0s-7Z|n z3@#JY92@6MIc6#l4OZt_u7Y4avag{F5-XYn#lfysgXXh3nxNFOSxvyvp-gdjyn0X@ zhMOPn1hau<2~9klo~J_J0O%Mb1JgTLqhOErK{?E9 zFcw5u-&n3m6E+1sf&#%^i*qYH*(<0eS}qFbRdGXtnC^Z5z*H42qJ%Yu2h9yaOV2x# zgKps^>aJgcw0g8XyCkS(656`u>U_MU4OFUsGotOoqUy3m(AL^OeVO5TEQq=rkm^6#atg4fOU`m4`k^7?`5zEZTEFjTz)?~hDZzs6hZ1ock5y_Tsy zgxk%Tp&rU|UQcY->5k41JgGW54j!MaW=VRRRW`WV)F+nEsCf?j-TBR>}z9;fm@fT_l7Vm2eG~2^PTF8%#=`LfAC~b zKnQ3o4=)Nw?orPRWh4R|pVbNwbp!oFPsq=Kl`g9#crGAUDOz4rH)9OP`UDsv;6n%w z7Ti`J#Id;ft{Q#r>?F*1s^08}wy|&20~iv942=*`-|OWHj9=9{P_;sk3EIA?GYiAo z2vSnY%3v){W5(%`=%*oR6s6F_W4UXPW)LzLzecEW*Z&2hY)5UZ(!6aPYK z%{b;$g92xe5q#TaHVZFTl&+}Jfl5DXR3PGju?Z-dt||fERMJGzkOJg5aZ_St&0AIt zfwxyVEU2m(j#dn?R}BsNh}cQEyq2J3zn?S{apZlhr>RgF<)q+3ON|*6RMd0@A&rr8 zVTu1x?m`nyq*pm|oG4ehsb)I%<@pvG=Cv8pQ3_G#2eDdNZ3MO2wiDF)y`v_db&&%$ zR(maj4Z3RPA@2*B{-uXz7W3Fo;52laOn=sU=iAyc4?0KqwT#T8jTrk-`v%d!uyNf*Tmp$>j#=Hxc=TJn%hdWo%=yE zTa30P|I~;PH5m6zBS-bNSJc+R)yIgm(==$S3D$1M)lTTOZ#cBwWz@F9)irkQD>FBr ze18%AR6%=?Va`I<@xb;Kwdn3lO4|$#tXxGqRVoy3*VLj{JIUS^wY8oAFg~b79Z>c1 zVkyu!)vjgi3|Pl%1Hj7~ZH%x&GwodeKpuUDGr67LfBw^9Rzvg?A;NEtz@U!W@)C}R z^*U=mIk@YBj#I{Jce8ezQ@6kbw~o_p2<5KB&hxd^#b~>Hp>`SG)?1>jEJEAK%e3g7 zsq66A3T-324Oy-2kGB)oYSDCi9oAW|J%MHQCM~*YavdJtszq1Qu0!Px?I^s>+$q%i zdAD{ouK#h5_AcIkZNK(5uD|uLHWkZZ$F#licJ>MF9K4M>tsRE9tIi7Td!85iiMgoV zgxmdoMd&B;H=&<(H-vh9ZVCP9?`ZGg_8;$Q^Kkv^5430yzYd!{)}p|>F1!OaH(#5E zcZNLIN+f7&daYfE#U_wRTRO=O5MJLYK}gM@M!S z*AdTtjnom(E{V|*&)$#I5zksn3&i@A)e+0fD(ISGB=VMApNhJ=7#>#A9l+ZmRdhS> zHmaJgp|=u1m6UK{bsb94>+oSs9SMZ&Iyw>v_v`7B@%{!s>qx*T8|g>@ylA2$0dT5? zpnv7oIucl$+6wjhx7U%tD%VLz9R94cfES-6;N^4|>LvEn?Z^0z-nxa@o(K8~_Wsmg zP^(U=4qfBF4)+e%^~8*S*wLPj>{x$wP2Gj>jaxr z-6Ys#`xXJO*mj{_{tf|e(=Nd#iTeb+bq54I{b8Zr^&^5!z;VGQrB4es>33G(dEmV6 zEw;&)tGdBB?!#~D4rAqC+}3r#+j{qO6Y&0v4|Gy2|Mo~|ug({a_kE_D@&3ut!DsNcMQrd@ye&~G_z>Q{j1R`i3+-it?}wl**D(00KiWQT9ZZtoV%y-@ zAS6qYgHv%V4C@nIPJy;t2M3er9XK@j7{)0#JU9vKzbHfCmoO%H6s~`7eDDig|LpYO zIJDg{H#iErzvSZJ!T9isWx-q3XxnzX(98^jWjIw`dxI}w*bn;zn(#pI2`r}{3QoZ7 z^G*d%4@BFPYr(%@dVf6<>g|6L`~lb7_B412Hg~I6!6R`$SKkD$#QlsEg%Gu?NJ0kS z{agG(-Up!VN?ph+Y=-R6ki)or8$(DYmLHo!-eUQNC1e4{8{!Pn;Qf=`A@A`1kwrox z^k}=LV#o_@Ht|@geDZ7q#$=`L$Bk0&WD5= z@b-y5l#J@x#!x>jFEode5&5$<^dXk-IYQB517(FPG#77|ctX)mI=nkH6i+CySXyWn=D%}NXjP2==giO{c-wzLC<&X_IiV1@Te&8b zxP8~U&=1&u8@Go3iS4xdaA-Q_8-5}*NQ<`RZiKeN?Poj)b>lGJo*()Z<8S>Vv@ycj zF45P)N&K@;e+<*o>Gcv^-Qd*cW4c^%ec!s^(CPLzbY3>W1Ul>Tb`{L%Me)uU|~TN&7z4mw~vl-pRd`?{XRP5 z4(cyZ+^$FTxfG$+34Jo%d+?OrF5-R@=JEGu^yo=nV%CiF`uDW<_)Gd}bg$>C{xL0@ zuj|nhhNSMz8~SauxagLz>$-RJ3+diZcl8TsaYdfKCg<(%`+a>K3fKCfej4rX;}d-t z-5dSP=dg?ydi1pz(qH@6`jfP7{5zjv*L={UZ}yN1vQPSGsZjjstA0Q2I`q4~5$<|% zlEDUSQiI?2Uks&ai(HWbeS?KG-XSprd#k~xQp2Bq!v1$kgE!DhYYg5%yA^Ek2AWB4 z@CI5-qrn?!E6fJ3ldISa)xEld-9-#aFubD92wr*&R*+IzR|+T{hD$L7Ps79u(tJq9K76YjibqKvf{}CVH%gc=sO!-)Wr^? zjv6vV+#9&@xSU9f@e`Ywt5=N+tQZl4ow?7Ol*Q`+UOpp>(UIFty8sZss%mf9l(h7L%vtd29^yIIG zO>CCV{X2^^!7)fM;sskG<;5vozGG>wy zXk__K2;vz>u}%v3Nog!8K|oP%0D;;KnTmd`OCnrX}y9zrYI&3G3NF8V&k-xwkf z0WF04`Wl-fm4J4j@hU6Ej#OhfOSlmN8-^G+Vdr-mZalsrTl1;HN zQe=u`8F~pZ0&}jnNddC_?AdUw#8h2@sJ&8}LRjubBh0~|qurzi_FYMTf25?T(UipE zW()`spa&altlNaHbZ&u-i|FLZ3}7=xa2=s0@e+N8D=pr zh!6%aM;dAzOHNm&>@~5Ru6QkjzQl46eN|xEVH5pvr?LA?i}1|pK4jX(8fb`|fC~{W z%a@CVeDuM9=ppg+3BJhFrfXg|8qS!Q)*=6;I|4_)1=vk3gK6hY=v}fc@LsOzXO^eJ z&=A)TWQfc`VDC$l9Nan`sRLE!)%4$S!!%P(S%Vulykpjtr>40$4Ew$?vAmb#>kXpb znBM&LAz;2L@cOlh=HJ0)xS)K3eV^2S5r}zQO?Up4=VI6;t#$~iJu7@O3gRW$SDj=75&WRScU{2FpTgw zFTo?`1*LgCUWts?G z)yzsLs$%ZJVg(;L48!sglNSJe9&gW+Ku}QM97?V=fdWUM1b&q|H*>IAO*6T2TI?tD zu|lZ&qYE26S;x%u7UVzaLPXbr2Lnwb^X2~$vxAMz_!HaBlzSg7MO#eM)emT0R4 zj^l0<%t4PB1$qPLR5vrz5BB_DU_X%XKrVxIlFcr>r0F%lyd9rSIY*e$%R5`(*O6v@ z0NRF3HV?-Mk(FgePqu7!-;fZi@7;-!smVqlEF>TJewuTNv>lAfDmFqZklrh%-C&Sg|!GjXf9y!6Pc-^J0d8MfXFC0d!|k0S=}X$idW0MWW!gJacggcLUygXr3LKI_>&J?gEkxD-Pzr&Qc&T!xjh5t`(MosVdx8US(fPsHwNckndh-}+*SB~ zg}T5TBmo7B+G#-ad-EDLtln_Q2lF2+;fs{$4gEivM~Vavb3U6{eyWea9Q|T`!N38c z#(xOi&(60n_Z}>=v}Z}S#hon8fa9GxAlqaS=9laR3nVa7Zuu-0Y*IGRGM2$Fe+Ra~ z9!kq+2IGK#q6DceDb!P0TFbaXvpS@+JS{w{R-u**$|~PrnOqoFJ4_bzbzzbY3R~fz zkUwp(irsRsP*DM=6|sB~gTG|$L*T-2%LuAxi3m$dVe|wD7JcX{(lW46<`-ZpmbBOl z2h;e}6MxXLsU;2+i?O^clwuePu8*~xEtJ{8s0Oe7U{E=Wp5<8+U($f{4PI-2dZI-F zPgk@sT^#>kF+T^;tu!6FAw> zqJkG2Sh_QYLnnw3{$iO8kwOVT-Q1!Ae@0uPK-Mse9L#NMdCV3|2@pZuVvL{!IHIk^ z$rv}O00m0a%9M^41`|SL1vSg1Zq%+N==9yJ0N?u|!`l3#`?7|O&@)6mCo$-ZXQ&_CRba(X6oTpjmWskK z4m%98bYt{IOVDVX#UBKv{Vl3qu?3U>1rZc$R8fSq(afQRtkmeWiHt@h$N&m5IT$kD z@`+U^j0e%^ww~p>9r9Zvg&cAOmZ_i&y53pq$wnTz*x_)IKTrFQs𝔰JYJC%&> z+PPbFm!vLnWy;2tOX?imHNHz?w^DJ@F`c``B$kU!DwPxy*ST}4SYe53lUV~{l*l@e zuHLpttvp*;Hv-<0nCf#=l z!fKlVnnL z>oi&{_LH>_W0L$=k1Tfxewa3|6LQn0=GIV{JKkE1gQFW-yHl(F+1SeRbL;;X+XgE( zwLU1EeKAjncuu3_8nv>{E{w~8S8*HUTU(O~=L&>{eo~@?mFd^KefEL~<}%Brb{_($ z+R~~7re)Sra9A(vqQY4mW5G3jtPU1RG2^U>5M^vNnUJhoMp@TW&!vpEPGRsILhKG_ zjXP%`^(8G?Rpms|iCHrdK@+W_(S0ceBAr?92Pf5CK^Y8_6En?K#k za?S6*087uX_G8Hi3xSCcaJrVQ9l6zAOs~Cz0b-rg%I^J zj#!J)i6ifTZ#wRLm^C??LxiYhS&w?@l!Ud9`?BHUN!Dy=4hv3M7c%BRbC7Y-swdyQ z1O2j50Cc=yUBK!oh_GHTCPH(NaL;N04m=@Lx?^3)%mKl?WZ~ew4`GMTpINgc+-c!9 zOP@;drNGD6R_5C*Sz`)~yepcqK`Zmj^@zO|}E zAooNU4IK8~I#@!AJN~p9SQg`6*b9ZA6u9jpfRLmuDT&Fv2N=c@EoBmSp-bm~gl4sbD|oE@neNzC?nreT3~j zClnK-Y~K_@F{P{xU4kPOey!#!o~&m(%5nwkpF{v6qQSp!+Yy|S*n`V&Vn5;b0@E-d zpF_`RYIz0Bgnf+&!i(S$$J-iAKzMXdWvM;rPX6;f=P zV0$YJ1E58cjpdOrUuQUL(De}|dS`5HH(LzL6!%|P2UWG@knd^RMkBj*Z`%si5yG3- zdJtWws2pzZW1IXxU`U>4eV=06MH%iNXj@r$h6L3-$acRl2}r_I4^n1$_seEH%cq`G3T4{#e_)Lffze2%ceT2p3JTRiR1q zdZG>e)D6xba5B@j{C`B&JMYamu>VhCXFSicePgMz0NP zZ1F5B*dGz7Zt+?vyuPw~qmAj4xc`NP-eIW^8*H&1R2C{MkiGpu#t9q!2yLT7wqU_L zaOhE6x5ABF7#kw&L)5lS&)Cp6If!j_=WP4^h2rnO*+$BQV#;0HJC=JKKTLzV+yd7> zv6;nS`h%_^@Xj;a*+MH}5I3Nda($-lM)4wN$nwp8(=8jT3A$Wzs!*gfGU6c8$ZGHYOUSEa$52a!fr79 zr7;IiwAz2CJ&dy3Hx_@39f+OwY3pZ9IWZ0~*VVoU( z;0BKi@MCE^x)VXdbY*#asltJu?qC`T3aZ*K3E00?v)3-1J%%Tm?C?rWJNh6Ep*X&d zy%y^dALKT-Hvw%I2I}C1M)vO_ve<|7n%eKO#u`M3OR1B_wYATnPAb~TzMbaFiv;@? zTHM&#zLJ?bI?rqlMagy<#~l=&Xl&BUzL;SwBu_M6m{bq|TlBWGe8YfWJS7Yi%(vG8 zRTenxaBn~RR1ruxT3iF$4zQ!|_>kh|RC|U@D0WS=qvt`$-n%31)o3v~!@fx@?0qxV zK93cN>pJ-?J&8;6G2~+L#+;qky`*?vGD3)oT^Y3l!1BD3mHbX*r0lEucAyCh< z*Od@kf#f-bA=D3^o@;0MX$MRQBA-{GcB#6^zM9=G{ov*tJIe)DuU-1VCQI$;V_`U` zK;#NL!}|a~pcbsOqa_lF&x331R)!4qVl@GlBX&8QzS;iBE&z_dXfGys4(_{Z?@Nmn zZ`vzLK-TaQS~%sdeF)vFePBOBi?5#80WD^~z@rLbSN4s4Sb$JG{Ffd5Mk(2A=NxIY zxK`>wxMc4qg<}jY{-Sa;pw04)j-Tk>sy4>}T0G+NHB%S!HCtN3v58@E;p4Q`-hMwjDoTaAdz<>2ooVifqjiJYI6`QJ_>PVv48IxjgYERm1?X2fCl`cf?=~yo z{$xiHv0#T!eH{LU81CwVzayjblByaRJd@%`6=heA(84AI9UeNwP8j4svkkR4tESH#E?X~H-Q5w4jD-M?yrNU(GH!U83@a8ppR6Ku%n0N!#NG?H_j0%bPBR3 zIvB2<{ll1~&p>#3lEXl~^)Az=->zwnk?7?NJPG}j?Pyhq9452c8SbrcxjCt5(yexS?EA$5R#y<^?KA9n$n^Ybn^lrVaO zW1kpAZyc(D-!?m9MMAOKHiwBJBthb7M@MjXw?hR#?Qo#q)5f@9|1QT2296jDBCP2W zfzzl1j#iZ3<3o-KbnnRH4wes{{9p-zadrfzP}bFIz&yJ4+x3 zL>C+^5Aq;{4B!|Zu7C}59pf3sT&!I(?0elY-w%}dI5Y&>@*G2{H&*`6l6eB+h6j!| zESHEe9WYJew1W*boN5r9?^#E>MSWGL9}T>c z^_|In!rnDaop)%3InA9-D8lD9&Qmg>!j%N)aav(*XD51=l8l)hyE@Soeo`#b&AE;C z_N<@tC5__zRHuz$ycf*4Db48s#)s{r;Ok+|q6}*#A#D+i0XyHIF-66^osKUYq0FS>NGNRkL|Dn?!E5(O(d9L*)8W$Rt}BsI765@)X#I? zrX0#Xa2{jj@bICN=?kvd4m zqloq+Q(Srh!{M<{&QKQ5{6IMW#hFKKbKslv5QA-eSlb7>nC={602nQHNxUPP%*DzB zA~Qc^8O(f2z?J^4TxyU>O4pvk7zCCMaAX`iwIGWLU${M{f*a=;M)*UjKCS233R zDx{mNo6aB*loB}|N=x~`iprxOymN4kR0RvqbEN24*{Clz?jf z#bRBw;1Li&Zd>&8zI|u=bbUC-wU1hN%L3PJYJo+IU4vP&pY)TvDc=PSTnzIARS$+$ zf|k{;)oO;Z=pR^0ID5A%g%S-t;7XJWi81Mf>xrLG%(&n}&kT@ssdv$Z&WA`*bJ^vl z6>8md^`i*K?z+~~;@Af+o3Pe`Tk>7ksR8D{a5Z6$w5Wl_SHX_&T&7ij2{f({3YSU^;NPoM2#x107=M&zEsjQU#UR)GD-ix>$ibSiwXEdt!T z1Gq23<;PZf_X-AwlFNNL$J_>RAsbzU*8d{v1fEXx#AG|&{xHz!9>9`wC`yWhlq&9e zz-@N3Tx{_IbOqm9yMMM*K=`2ucDLN zl9xe7qaq3ru|0kk`n<5)4^FS)-XKC|s$AR6@D*)*sz*FPy0O8>Ke>+Vwmhy_XOb_Xnz;?P6^^Pqz7{}A+Tw%M6&x6tIG&LU>`U7;kPf~ zO+WWpMrRS675&}cS&8gOb+^PLP^%$@;}AH^&Gwa3!8Oz1rZo3)Y>XkJ+{X)JjG`It zCal9WvS!MEpY>dv=nh7_;N!_CLD6>eFYb7jr8kYir0fE>EL&d0E}ZOCnJu6<2Lif% zt~;FJgn$fjhE6tA6VQxy%N) zinU9av(sG?g%8Z#<3_)M_ysQ6Cx9j&a6c>;OnjxU%%%YRw`nN*t z9DXmv%G5vHqgZ1EukEUllOKRiei}o&$kU$j42aC)6pNe&HSqH!VWV#G_nbpRI?M?2 zpf7}efqirywogpcl|nV}U#CFNO`c_pr$Cgn7f=b?MtD*%*)!3e{&KXfRL=9!4{hVC zdC*S}e1YGqd(c-Kzrf=)J#l#d=lUM>yP99%@W!5fcw4rGCkkZ+?B3RskCkoQ(bK9> z5u4K6gKkdQOB%t?1B*|FK}jAJ4l7%C4;e5X_YnB3Pxj;#rXLhu!Wj=lXFOTBhXVsW zlM90gQu^l0p?bJy4My8D!n1@m{Cy`NcIX`Mk#S(@l(;&e-vkfyX;Rj*0yCU65t))< zHrxEhlM0l%XqIR@#l!IQAwU4rf1D0MbUO43y(WG>4n0LN-E)hL5dpNpAu~L`vrMpr z(#uNdrYk-V0_Z#_97KN&^@rW(dWy(Fz1?vkz%bvV1L_SP6Fk4za}B*fju~%X=3%+X zg!_Zm6`r0fqpg>$3N~Nm`6`$Wc3$WC1AQw4ae_TJdy-iOF&{2KxEk!;jzA4PEMI3Q zP{JAS|Kq%A({7I+Y_!|6i5gM8&$G8sZeO$C!*td}+z@rhbA=Mo9rdtW*CsI2j(PH# zy-Cg>nxFI$DR;(ms8ANUe%8ZucI0)O4L-l%LBFv}g8a-S56fM`A6PxFc$m(O5W;5R zylCc4&srh8LBo5j(XtD^&+{;STNM%61+P5#|5$tPfGCgWfBb_Y#ex*EpaK>+4i4_% zj)OaTxuZ8*?7jEi3%1z1aqYcp6zf=`sIgb9Q85vFji52HMt`&WJO@d>zkeLwf8BE2 zzMj|2&d%;^nWz3OStExZ*Qca~7ciZk)_+Shl>HF*qW)Q#gZnEwhZBYMDG?E%0Apoe z9rtH_%9}NX3LgGdzY)<*FDZ_JIzhiL^-oj69po^ZMIZ7i$8zS@tH8H+x_;#QxQj%^s)mk@i-`^!D072`q%b2onIW$9Unkis00Lf){S9h~mTPUbw9yig)IF z;kJq>u3zAV+bW{iYoQlztBB&N6<)ZlB8qj^df~Q;DBj)Zh1)8k=&;QTw^c;(-43s0 zvgv;9R44+Co-lBqS3RQkgj=zNHo0E3cXx5e)MqHZv=z5WHWOXjK)}g)O)IMl(soMU z%U+aM;)?B*ob{eAFy)F@e;ca{@cE`!wQ~4blfV2+FQq!b$O3&bhfLv8{~ZUYEeeeto7B4lF85V5QjZk-eW0q zkzxRr(#YEhwv_R9QWNH@|AHBByh1EQjEKmkw{-6Ws)sVn{IbGv&}0t$vt-vxoaymw0q_^Xfik5mOr>gOFq^teA1 z2Nm*hGKXQ_RRkl@j_^KU#oY;f8R`8SWj=`_PG#GB6;@=u>iX1>wpKLXTksoCw%~hC z^^PhHz72TJ_1-G6{&l_ub>jl>6f&ykQgrUQ%e{Y>P!Fv_e<*Y@ca1klrRM7_Px9Je z`R@0P-lHig0EMQ+@s^9byd9)x{=Ub8x(B@XO3xg*-@656o@Z%^f1GUr&m-RTq$f2x z<{eK)_1yT>j^SeOB)EUt0$BB&cP*kpPGy;!@jMy6)~P(py<+FcE_AD`gM=&I{^i*D zPIX;j$}isWl;BUOz=AEk*@@M^>+QsF2$wdSnCBgDe|99+Uj-ZTy$i~T_Fj0;yD?ce z0UZk+Sli2fHCT3ve|IoE^{!_vNzw16_YmQG;6tG|DIAnchCiTaT-}On!v}8%D0uCC zSZdPkZ@n)`CNt|VZxgv2teoIqMT*^Ce(`=>M&ka&Ndm>Jg>bFKIhCMj?0A&#{Vn(tkC{iat`^oOH#HQmJ=Cg}Y>6%bF@~ zO2U8NQZb0wQYEo~6P)U(=xJy5NQzRH#j1VSnqCTWl#&BQ)z}ArWjlD*Td|HX%(5Nt z6g`<=KLzCw8P*-mR*q51QOffEip6DF)R|^ZENQUfEMdH~0J=lL5XA+GsR@MHBh}J| z2@Dyd=v#?B-__g|K2KL*k`ckK;%o(JbA*eF*bdNdj>14xwuHd898@^56}55P%_(bY z3BS)%m?hOgJX7Rb3!b1^s(^A*@%<_V-hkum;nErf-hdOuaqG~YrDD%qDy|T` zA0UU3vK}HoDJaoCB5Z^UpB1zXOe+d!C`YHpKUUSZjL@g``!pIoqPg`!}L@U+X7ydx=dP7ey142W-ELu z0!FVVNV{4b;mF;m;^?0n;0_rnH#Vws3~L+WTH7Rfx7$f+)W%w^pi8MR`VH$nwEk`C=O3+R$gST+D0agD)i#->z=rgUQlF zu~So@Et0iBofbZra!`ExUAtlgZHHonR~?HHj&v?Y7~i!RA*zQ@s?1?&U!Pu-@J;A^ z%s{+kcehb!d3LBerYg*C6HnXNU9>yLc zeK3ET5dYC=pMGTWZP#K;i)&f_6Mc?a3!=<00VQfXOd+T2(Me)#~lH0e4QX|NOHRTkR#y5 zu~9O;v>QISAt~tP{^o-llA;KYeQ-lk6vsaGvCR4tpZnm3r1*B^YaiT@6vfYf`rwA7 zC?5FegBy~f`0T3>Zb*vam9k0=*>t0G)z&O$fqzXlrlYnY)Gx0jomv*AdBj0!%Wzk+ z2WXs>ugR49jw}>6xVl4pb!A=Ra3aEB52#y5>1buOosHcdoybPiRc@fn5%`O$9aEJx zU|v0CYimT?7NHKF=Q-06LVT3%t*z$&BmTGdS5o8u1wR$l1q}adQO1XRy_Dlvg>Akb zY}6EGYj#7UbYOk`l{Uuv^zAOh+%BcwXcDCSr7X%BpjZA$&>#WneeoR$PMn0g=R34)Q z-v5H51l|a+U5>ezH&q@ggSu(iLP@!Egh&7`t(2r#TU5?%qx^{z7};KVk!mkN9hIc; zS`c{HS$Ufi$nB=QMkO$!hmsOe0}`0dEwgt&?Nd_*n>lYmjm|7k=naa90VZ4%ZX;H}1e4>&RB4g!6EW46VU1C?x>H< zqV@=Z<_qKJDDRWK1@iHQX7iPKymfq;GLNQNnaZn$N>a#ZQH?JwTcW(ksq|b-u<0+p&k=iN7ZOUGav}iu^duQ)LyAGy}dAD68`xWp$}0Cd7KAG*Ybq5c41+!ONFQyg@e~ z4!@Eo9nh&EI z`r<_{w8`eac#&&91h(B5YKDqSCHb!|^GzWSUeU9BYC_qSzIfqsKIEY6{Qbjy;7p3~G#LU5{7nYpim-Fqd z$pp;VOg~bjD=K$v=!e%*1P5&sKgvFwfZ6|@A0^n81UTH>4__xII81Kkw}+~SN^Sf| zL9Y;CT|2*VGKbKPew(Qrc6RbRC3n!N-TbyvVQhN%otI%A^!6j1HU$(ag6RLpLe3z+ z0@?qs8cymQ*h$ASgsICiZ>a1+J<_i%acq@f#4zG8rr1tyj`gD)n=hs%@H&t8dqMPt z|6t|+y&t}=QCRPr$^QdZ*;D;UarwVxt2V=Lr_Ad1ERrGq8?XHwKRs~>AIls+J*~vD zhkCn7chr7f=r@$;`Ny*C6@D0L%NZ}bK&RDyT`6IgkS=?RUp3Z76EqYYw)qVr!}Mw& zBt^B%b8NsO_;bIX-d38cL?87_BHKN}xaPC?vop)9&JbsfB`}rXUQHEVa1_Mad#Z3hQ53KFsBk|~6whl^mKofFAQkQ>if>!# zRJflgigUtLxSuGBPSGmdPZY&V@haR;6vZz|D%?*L#V2Vh71>OCH&NjuJ=8rRw2|r! z8OE`zpDk;h>(vu7TBtsdVKh$9?D>ItH+I)inFYPus}w}PB_c!*XxKxAuZjCx+VES| zQ?-;ZK@j=)uc};NV=q-DYj(FAx`vbeRP`x-rrjx3cV;_8<-xq3_h`b}G;QT9y?yF9wTcvO ziOM_Hsus#k)_jACGB+16c^g%va7$F~xJ9*B=HRzYMVZ11m<`)iq;N}AUcXbdL*~$R zkBSod3YZ6bRitoBR35ZnHC*Db;3w63YTxdANR=zQi0Gr#PPhnm9#b8cVJ4ka5ni7N zC{qY!l)-uG91;X2Uub$mg@FwpUs&y1 zD#~GZi+H}U>b8os>{No#=~opVy%%hp3#e@^OE&wS>H~3LlM&iHRBe^n)Ou1%-K>17 z`n5F1^N)>3z!v~u5*9Hhc=lR_uNV-;-G8VqP_8Qp?H6oNl>}U_(jMfg&nkSAfxv$G z#qyXv;D4NQqf$IYWA86QF;}LHtD6hX75yy_2yyVo*8zxURdn>n*8zy)H79?IbSqqn zzwP4czmrU1=AQQsC29Qu)A~1%27A`AzCYf%U9ED1Y%hOO5(pt9e*k3q_`k7{WN4`N zPbD|}t1>_>$oNjNC0PFmlTMyUG5TEKy9= zs%82|*-AvaeCPjKR!FxN{`=(Fb5L7<4BUhy9v%HLBakR=>f*n-G@Ztl;Z^5_u)J@v zCI8&N*k)%A^mnJMH(+yg{zp7}aE!l}63>>vhWYh18nK{us}S;+N^gYI!l+ zzY4j5UmehRN{KNWF@Jm^fB+w`sJPRHE%C>YQ+!)ynSUelb5{C2v*YJS-K5n0bJqCd z8vz9DiFN*@#ZY1VIh*`N5I6}Uw-zJz-{!x--fA}wF0` zJa<4lwzsE;h84WScNKr$@86Ho8D94b=*t{_@}~q}Sa$+Te~a#2bO-03{VP&>lRzj*HS_AM|3fF{b8w_9Jh|g3c3|H+@?AS@cMDji(A^4LG9oYaJ_ zFa0T(@+^Svu;h0fh)ZPzK}G&+%SddVyz!?*Kb#Gqg_16Pm55^h&i|N9;b2dkNMg$AELP$LLTcfD+J@U_EpV$dj>7)Ci#5OD@4ms2M;yq>2a= z*~;*MI#OTH^9(qJFR$nBdZSkWv9Ak2B^VU}q&tlkN=m5h8-TCH6>RMN11Oi1OR$y( zl$Onyz<_-+n?zkH+2jWYkT&Q__RMk9a9f@yLx9q7C$U~xHV zzUI0jV2tE3VcqHgQZQPK&GYEMu(bj5unA2z1Yj)6I~3?Q1^i9cJX*6258LWX8`Yh5 z1Y9DTVYh!oY34%#QIseR#g$=1l-B5207hv-$g?K`FiI1}g=YgWcooHo7Xz9QMekO! z>nNpF&A%Qno3iyNI2WJrW!^Q_4s5_xOcb1V)wVSpd=l_XCcEcF0PaKz-io&YxDzRg z9-jknCsGuj{T+Z|p(y5j4Zv+dQT*LTjXRN|*sFpXcOpg6&ryv#k)qh#RgF85q8L|0 z9Y~2LxtTFc@#P_1YFz4}{z!&7QXkXLw9WPE4u`$fR>ans$UHP^dl>DnUO;rzA|&*H zYkIYt6>D%zUz=srRjOF+ydi>c}Qi}OVPlQK|Op)+xG^w z!rE#v;8n*B)>i9_Z#XXhc5lqY5na!RMRj>{qq(f_A&nC>jaLt&Ompj`smC#W_7DfQ zxPf}S@f>|y3y~H|Ve+R0^~bWzGsDh_*`%tUf4hW$Smh|DA*AWMPhIODiI+SYsmEGN zY<4tNQx4Pp3)LMOHdj}m3^)IQNol42vy8+mtF4+6`~3&7sGT~NvZ7D|V@VAa&f4x% zJF$?v0+j7^=6}}8nmzUeM#t1cKfuZJH_E zDnUJ2jj5ox?SaiyH6FMX#r@ON=sHC)dxpB2Ty5iQ^ht!^+uW3@gr)=+cAm(A@i8Jg6wE}o>b?SUdHOD)o)~fQ}eu<@-mZRa=cHu!|4lZ z(z=2LrZ&^OS4fKstgXq$8v0yS?~p|fyjiO5DWH|`>X!N{c^JomPlh>gM~!O@!fmX- zt3FYRrjP+U-cyrS82_a?C72$n@g3NLm*&a;g4e63YSPlfe|W8ap}vVnHUF@8X47A( z50_qFaYgD*r5R6Zv!ZQEZz<6A@H0LOxn+2tINj4~HNg~NX zbF_4T#R;AEx@jKUN^^`)bu?Y&;d!8!rcCMjg5?2ney27H%{YfrwQX@?BDQiBE*6M{ zN}}X3ysi;qLo~0gBry^VnuW3$ufsK@*uF&Z;;AA<(cMT5M)+bL^HYolBYaU@8m9>@ z&BRZ|FYsbO%3qh_nW}N1oGV7Q|Gc%E)Kt@!65*G?@Cctx>Y>3G#kA6VC+qTETMdT! zLg*8nG#KWK;?8cGY+`5o#~ofN%n$3Q*+B{O1@YoA9}bVx;0a^FE^3kn<9botF-wDS zy(n&C8jR~jalj&tWpZjlO{h^`e-cqrtdd6p!!L zkWy<~Ow1jM4rsO!RXC1C_j^`f%2QNuOcPIr5yMwWvZ5|$H9N^x-DdDJcX)n9qn6!D zz-`TYa?HlT*t7P!N?`q)29s3^o&S1IGf-}n_K!7>H~@IO*Bq4H#fy)c<*rn)yEOyrLTjhMk2aEb>s384m#R%bup(K91s;K; zWX1KaABcBEgl6~n1=1CV=x%JLI*_b54glLVffr@P&C~{tuS!*%Wnsk$9wZ0GRbUTW zAE-TV%rigMyGbBrmLXPt+P*DPur1#O?kBKX)iQ88yU`-h1EQM+-lB}I76ig^3c$Lb ztgUikS-U`c%Cj%84?G8}I|MeM_yv6UkNjwYoc#Lt9)T^Lt#Vnv9j#oU&FDafviL{u zv4NDmZws^oIFAqPLXoT(frZWrbYctJ2RXv`69Z3D=6(Odnocv48*pxR;KOpLlAHyB zzY#D*Q%zkMNLp+Vm6MkQV#=vp=CwSKa_L`Sd|44lT2&C0kFO4V&j}n_7x>`YHLw9O zDDGc8`oiYGs@xtQ{uub|+aJvY#+mJbUn!<2$-XntzzKZW6Ietg@EihvClk1OAn*^a z{8NVl?^6k^%MB#0kq8ZRI~Is3*K(QniNIf|1U{S$v_gN!KAjD`Ma9TFA85lde#r~G zO~v@}O5iHWY&9I5{?!Gl9Kk8ApH6u(23h; z;p4!krJ{28X`nM@D#NJ&swSMXs8m==7JnB7#!{R`i9!A591-ML z*D>f$soKZJIcPK`#IjJq!zs0W>=sZjL6o^3R*(;kQ^Nl-G%dyV-j!I zMbhV!#S;H@OM~W6R(^!S3m~XI>zNec0;$`A?D-%mwBCJp5Tj}x5HpJt4mucw3H5Tp z=1|a1{<;5f&~g4b`bf~v{4@4g(2xAH=;xppWC7X~;$0v9x-YnQJt*1MB0%)7K}H_2 zjkp(t3Fvac^dJZm(B(qKM?s{_XF^%xS>nV}=93^yf0hfip9f+3vt0Q5MNkh)Y$ssH z91e73j(zoIX5jG;aRG6 zU|*+cKN7rV);Vokm^wpShGLt%GOImTOIX8USoa4QHBbA@8dcC`p_a0jjTqxZ!lCDj zw9hH?s$zgyOSP-ZAd#RI+ATCKj)U1NwcROBx|qbCHQKG5MEmtx%0>QSoUdF!oI!Qxn)s9Vo+>z zKs%hP>-JCDQM5J_4{?AphqW=phU*DPGpd}`YVbU%JwusTSY-8J?&oxPMd_Nh3iC|R z5vJ3CP%F50USHHw?sM?whs-?fdWtJpRiy33X57}gNk{iQhU%=W;O86K5xj+>ZfSD} zEyOVaF&|U9wuMk#(Ecjf$+o*%%5{^rPN{v?A zo*i+}xv>@7bYtO-jc#?ZhuBnJhfBY!*@Q~EO$7Vn(16g5iOxar@`Te)I?@eBi)5Y* z5u7CVkXcVx#dwH*xK?21tk62M$u)HHZPcPFIwMz^Uv(X2)1nv>5b_cSYFb-Ixt3H6 z#JRb{C3hX^kQIm7TVHpXGr6K5i?pD=E>H|&O_Vy)<^$)!L71ky$sV5g>85Z~>=&S; z-2AhE2}W<#x{H*)D}&U8zWP!DxG>K>&+Os%aGf{B0mw8Y6!z+|t_Smv(p^X82+ve4 zS{G9a>0H>Q1YITe^}RNL8O%D$u7iajAX0*rg_mEFj!~xm7Bn`dkV*9l*y1 zx|>9_cI@tYZB;g=lg_qeegw;#>&o+~w9w(9){^5}*pBM>N*+=P{%WtgM{H;b29T?w zR0v#JcgY2w?V_tqbLN7KE6nVw>&l(OyB@j&r2%3HR8@4y!M${QIHIbbj*{pXEkvSL zg1DhS+4;3&eO48^v#nz+{vGFHvpKt*5p;PiknRvVitG+0W`N8@nA2>Wj&k%sXsd)z z!Fb&@qPq}$JfZVc-3>BA##mHyUfB=j;nnO?*mw!dskyqCVxLoczAmXW)M~R2@3lIX zv{*;k%n_PKX0_q@5?!o?6l=0n=fd)~Y2BdrGTlJ36XPPo)U`TXTV4&dHt22;k=nC@ zpLEU(KxYRy1FTo<1Ao~m`M{lfs6J3a-U~WvbXZKyfmJ$(Q@F23w2rLkU~MIfJfO>? z`JR7cjxX(G5Kwu8xp(;P2zjc+8^A|6!YfC0J2=>c6Z9EiG1#Y*WFOd`W#{Q!AnLsC zFsJk75?wY9#1P1x9ho%L9>!nR{Y)ImBvJxPD9ibpZYrM=F1taV5(+U(5It|w=XJ$U zb_}P7?cvLBx}HSW)1Kw5DXh(2h3i7ukh9O+SagIgf$e&vqZ}gQ(0r&r_&qoh9p^x7oU}5y)t#f!1;sDh5JtLPr7W%W(R6ktloM+ld{3+-gihV(FNAQ zE5F?}0j@+B;Pv2fcvF0}eK01762%)8f@$x5sT|yt^>zrpLSWLba_~w@#E$jEGixx1 zj=`PuJ(k$~{_29CyhX1S?{Dflj zoEIzX5Nrp1>jvYs+RN=R-_s0jaDS<}n4>sJu1Cto2e!dbX_2(WYqw}C_NsHREzIZ~+(FixRkz^zG`(emxqC1v z8z^$hhDSYv@l73EacpRx;Dz7L0T8Ao#k*m>_MjOO+^URZk*kLVJ5hQt$6<(r$5J{w z+CZa`!9U2&|8Y$4GP(ITP6&QQS*Yj2vEIFd%d_-qHFd1rl;Drwt|D$ZB`7xVek$)T zq8oOd5qwxS%aD1&D`d7$STN5*!L9k)lHmIl*u4i{i7dB=rvuB}zRz@4%z#IcToC*VAJ*yy68hkf}6XrfxlqB9o4nq!BQi! zT{nXJ(6rl-A*=)J{v|k$*vbOI0~X&3j-{N4u!vTwMv@XbM|TLfhOLi-U96?%%6lHX zfT|U2dUvpU8N7n56@ifSD!8NEMu&@n=a!1io7cgtbZq?I7PDFTS1H+CdskXEl|BYf zl6AB0bMT^4>89ZC;G?C|&7L^@0GZ6GGWs8gWB?oem(uXS_s-bpr^rMe+mVTI*X0hi z%j@e?W`x)$he}!QzEsxZRYN`yurXEivuSF{h9j=}GL*3!iDbio>iS`_M*eowQ>H-z zCbpJ7lE4A5@*DZ_yX)xZQC0B1u6~dVGulhvl?ubW^^atj8A^JCT=dl+A=+s+MEUER z%N#bV^|Pp=JlE*I5Umq8QG@k1Qrfu%A^OFXNEh3+5xZFMtO6W~(l4bWnDiCkWr9AP zIIcHhjvETgvnA!51i`0t{bpjTNHCzw(pN3ZTE4{TL7PVU1?5E71)JvjlTt5+Ut8;c zmu54N-(KIFa>t%44lef6lX5mlTJNQ=#-0qS;0j&(>g&iP7Y)+?Ni+uy6pqk;Bx+8> zu(Bs~AFZE3Ick6cY1sBDdJncTq)7m*o21`FB?1UzAR3~O!V#Ly(chsyp6kJpAwor# z7T+We{+y?OOF54sG{Ui<`6B)8(y>8=3XrozA5XN5Ml91xUkScnr6*-R5!NaYUaZl3 z%7$8Nlb*EBBEb6CBAp+$mx7K!__(8(jvwe171(@_v9($DW2G~TZs~A?B^}q-VvbQ| zoS37xGKlT{S^p=&O*?ILa-XLe>BfFMT)h%odm7E7=_$PvJJi~t4Xiq$uV7>Kmah+{ zGS3fP70mOzo)UKAQ}C5v_OET3@?+O_?9+kjRq>_4`UV8TnZN0S*rgkK8*sa*KZ_yq z#qLh<=ViSfmoRuS{F+`vm;*2iE+t68x}Epl)MsNDj}+LPU-dJ;T{1uvFG>uI^?E~< z`}#$a2H4?;`YGT3c&>on@c5D5g{ZyWu<)sVjm#nSg?<{9!{C?t1Tu$~g?tVsn&JLy z{TL#Ly?^MNQ3iX|VJ4e7EZ&YypV-X>{`{cNsKV+G?CuK9972u~8&@DYz$WLAepMtJ z_fv%YBwPM_-;jEhq;FNBA*<`s1lF<@gF`%-XUn!O3pQohK#vw7yQ)gWwf#fvC_@CQ zLJNo*94N7Sv>^mvH6yy{kDEf)%ADIn$f}Ccw^fdZv@b6eCtVC-Hd0Z2J!Gp~?d6S- z&*j;X@jaa3U<}Jq%rz-UA8~UcE#Bad#&@-}b)8>Wt$KRi{ex2XkI+n*a3FLoMcu2gGTU@Clrmt5y2?|b@=45B$3n}M zwc5hoo(QEJpyX(PXvZmbiIuY!oCvjLewQtDR-O;-&FNIm3#G)u7DPbAIHG2=py-#- zzc~qUuC2_S&@)8gT8guB(Bod{%8C{^&sU*q%HSWzi$W=D4JcrHc=0-PBB6HyRtA&{ zn!OACfqxc$3|-4V=YI*E!#};t7@(Ym=o?$ZDaxpU4;jE(RWytsbc%L;4c(=cp-Ght zWnGZUG*1I%O&o#x!-YDAe1e*osGL&Y(4V){VQ&LrYg)pv@-gfu6KLgY7|aP=QyEs% zRM{VD1QYtj;iCHj*u@O#{O? zsvS*eXu!+1=#iTkmQXQfeP{TMtO&>EhQ^#g=avS_d!9vWu4`o=T`Llm1KLUiCbTzD z@~#Vv6CDf}$(=#p#gM`^Fs>V214p|XNbACaef?gBZk)i(J`{%th!IT|K7N29lRx{1 zL3CeueX!vifdE_{X1K?ny=x>zZfPWXKg#fs@NB8_n6ZX&oIsfg2Fi&d(Ris74S$h4 z-=j%}(VRfXsRqh4T@cth&G4M80mCebz`i+T0(<9`;`uWcNPX|tLVDk;zSwY@pa5`M zW;n)`e{BWb^*XOAh38wc#vo?QQS0gEH)Vs=hk9%_P}XIH1YOsH;sky^W7tU5fWtY%P%;7KMZ-W&V0j*8Fo1rS(UDMzx4CB6#Gh^d zi-D3iLUf|$Hw_obx^glq|L^r0b(to=Pt z45#?BM?E*}ptj)U7p0g2H+W^p;{@uxHc;M2D4KuR8v`kGtEl|vTb#rof!ps4l>4j# zL-)Zzx>qPF@B3tU#0f0_VmLr;f=^!!Plz@!0KPA4oXH8e+8A@F1iIQ9M{pM!BTmB-M^M$)Yo(GtHo8AWGUU&TmSwG0aRODmjpRvrYlZPX$B6MYPM|hpS3l!NZVDaL#h;cQQ$Kp_To4M>BEe-iCauU)poFFw9%g{nD1C^c$aOYcrgnfJ2qv7*#YK`H|AO6rcj5; zMr*Pi%-T}u2%l#d$59Zn?i(}V>pWv9#Ub#@3@cZMydR9`C`a!E!uEsa*|2qmu^Od) zEC|kQOx;8qXt~xn9qk34Z7@bt?lK6t=sm^?YrG28b|(Xfdr= z_oRrK@bID0-WFwe^}=}D3O~>MZfxR2@S~zfWmJ9c#KLMPbl6ip%#C7){VIgDWxc%< zI)cJJtOh}+fCyUN%Ih4$UUGTnI)*t@4uT?17TXt+P?1&ECj_vz)x!!2TCt!^gO+Y# z-rVZOdxqI_o0(BR>?(ROXr~AZr1Up|tWHko%?9~}xxf#quw`6qttO0QiWa!?@Hi;U zpRBM?QDGHer7`RlRU|-!LtK#kabYtlF9G9So@s_9;EQsrrr_JiA0{+_u!dpyGT$xm z;5(XfESObbMf0#YigHBn@aS)>1OwZLwV?EjI+M^I{d=Q&TlNUcAoL8z5daasa9ofQpVJ>Xu$}kVOH9rhfnlx5Gw3&Tjo`j{{i-ro*WQgy^oM*_wP;BJ^J*VctcVg0KTg@hxY z!w!aG`T} zKkg-?x`uD#Uh`G=@LK${La*>aoaNZQ;XL?-rUSwq@zfi18xvlc|2=GMxKC~TT)jN} zH(r12%J7F|y;L}r7{=aO6q2o;#Cwze|iS6-tc9y$;6|`CFjG_}^QzP+Vjm?U zdR-@~8Dw;h*iAVU$SX6f>r_?w=i zq@bl*le7$+$42B=KpL;6NBD48SZ#KM7<^XEk9f>o;p9~jOZoR+>mp`y+PgPJOrcB# z#1qE^CEKy+3xzi9$n~V%FlARnJIbQM|BGbGJc4?$^V0|y*UE-J9lc&MM>5Er<-T$Itv{ zku|C6hwL|zjZk2A{aqww`X;C$hJ!TIe)B%^8MmN2pCUu4tq9AlqSlhtgq5A(SDUCs zyfufEk2=KtV{o-7e3`%&xb7D9j)>eJ`glfV5!^VSibheEKCxZ{HnVw36_(Q|Wi4#d zL@gt77cg${AUNti?*k`8qTcXPVt;rPzIhhCZcNlj^6d1>cTv^YuMaU=&mEl78Dbkm z?L|!Xp-I#P;`9O#lAncwmCd96q}a8X)oq@UQU!cFMqxJiEv!?QD9Zk?1;`C%b&Yc5 z2DGwAlr6=s1yb{cDUR%nO?m)y?iW>yU`kS`jxf7m?|`UEzDVf!vZycA(2bqGlA>qB zevG09J8*kjl#L&l~n<+yi^ zFN(U$XMOcQM0t?|^26b&j&R!{I+7YeGeoEg+p9+NTLjRvMzo#}j%z%l7gi*iF8kUr zbp|8_LK-2!{7Dp>xt?sh1+oK74EPyvtN$>x-d?&gAMDC3Cq_F3e8CJGo zMQ@|+SmA+aO7dMqk+VaE{uG^oNimKn@ulKNqqh+{6k=t=yJOK8C|+Dt9&{plKPDsM z9HLG~6B1N10WK3 zrz(xjpQ8 z7JbZ)RaoHb3e(?2pCdMBdlt4h#sS{HjqZubn$Wgk(EI53a&h!$OL5PnnD&_0iU0qj zbxf{YJZm3w#Y+12#|klL<>KT@G3(`GROOg$a`B5}%m>Pt5mP;;FD$GYGn618VCA#+ zl8!v<#)MFwkGgKi?p}zlvOpPQ!#-$g1wcJTjETU8cYg_LuQFzmjYMmyIz~^XRd_wR zD)iLFj3Be&2yAQh7&lm=j~Og;@{Wi(M>NZZEc;Hh4UCA3F;|iVypb70%9kjVCBW)5 zjM*R?(?_uPVm>ZOE7b|yR zg?_KgL1AG`3rYwn5Vn1Zac1ue(ZM@d#ZIGi*rO|AvxNmiX8BnATI|^nwJY@Yj?I*{ zd&xJJVl^eOcB)t#7pa$dM8&?TEEVf!#I~}RinE)>mZ=~W*K~-jUS2Am?-jetM*hLT z*p_nf@8Pkv%Sqq*Wyf}qEE`sgkJU?b;qj!{c=_9~X|YS?-#(okTggtUX0s@^1;OAL z3N(&YSrI$(+q)28-3W@8;?IN~t6~l0%FEWolFroPlgF}2>tg4AyR=+WW7)a&v8SnZ zO&en=_bT{vVa}#lYg?(M*XP7mlkMu_!PwfeReU}in{Ork;OUXrLvnHNu~>Y=gBS|- z9*^}d%L>9Z&hY9~>=*glzt6-zl#6f9#p1+5{Pz5XSd7<1aY|n7F1eU?HFk?!w7(I1 zST5eY8S5__Y1Ln2+sSk$7sP&%f50BZ{w?FKdlajdzkT#1c7XibZZBeS>Mowvzc6;Y z{DYu3u~X&Zg}1S;@(=!a7yDHPEBmpS`uk6@=j3m%{T=&Uscv^f2-eb8FwY6SCVHH#qKUdFFXj z?F?PU#toIfE&4ugq3jM9&4}wFYtU&Xxk0hPb^1&wyB4;mO9hZIWbc{nj|jnv;O3ye-Zk!!+9wN0}%K znEN}6*{s?dH-RctnSF6_GMgp`<8r7l4}OZvm0@-riKDEJ3ZZHqE7nb?6LFNKH~~|1 zG7jT6?j07KqxOxh(BXU>hI1T+*<6mJyw6dPS$ZWdp0F4MSb2^`r8na!cd^CeV{XSe zQ09nOc_ujA=glf+W%nP&RhG3;<~h~+XEH>zgT^o7oFvg;$g8-jvTIrXN1QK_>2OxC zBd!9B{uH-cqRX0nv3T8qU*j&ydTdxWp4d^O2QYkqEo=M0&St}6Ys*Y;i*5XBS(w%p z;$O&au(@OWM9NGL)jpg>ABb~ccf&I)uvZ02N0t*=D~tVAE#8GK^oe(1Jy*ofWV@=x z2S^Qr|EMK5Oubt1#I9HJOgom{Vw4RGsvTcJmTHb?d=}>I+>ZmnTd(-d6sax;A?2bF zHy7xuidR`l#CQ0|HK?w;t zX?E#IA*Nd$7LVsTd&XC_l{7tRK>Q9_(@8_=^f@*lc92Z}>Zte@GUurA@gL=HlPAXq z$dlkHv*LZ^9~_$>KZ+uh2sE=*uPZQoDL@VQv?SiaS`x~AWjy7U5-Uc8+pFVo(?v`o z*RG2{F1N(64e^JFOr_Nrz}1*jm8|lm#jriYA6I6te~Mr6?L#XZJrrM$5`C?TH8q7^ zx$&fY^Sm-Mo`|PJLE^!!PR8RQs(hAoHvZ(d3(dsy5u+-b{Zwxca4!A?RR@g2h-H>~ z7vpowAg2d;@swFbF(=Sm!G21Usu_+P@e1kvc(FI*f8;tU%8#eqVMjg!O%3K><4+RR zBO{!+8$XPzChC4XBMJs6*d$7yvB=@XG)0(Ee9MKdF0kx*ycJiCc)jS8m+>wOZkpVn z;P?1FrIFJK@Ck{C3&Z}159XiS|B4@4nwGR?Z7;`HgY%!`{VjC&IG9cnrh44uMPta2 z!^)ZxIK!hhCc*Tby(yQ-v^@)VGues!z-&eR@5-~UPvcv{Sx3`5D~pO3RW?Z~F6&|% z&JAI-tBJC-ib9B|Il!)}CepgN1;K;msS_PpWM6B zRe|wKxQUb<_-KrIEOdx6VQ$=fb|%KO;oF-4kbs%81R;noOfU`L&#stE*M6526DcvU zAn-EXBwTanEV}(2X<#BH1{MV38%rA4_Z?jWF3n7&#K3~U`WB|Kya{|-(={-zjR}wR z;Q-g(MA=LbJoyA(olWb$-3+;d&xTE1Or*rXqH<_=6K18&XRCXf)>8?5=w%}1 zdJzO>_mv2I>~GpgCD3r7iIf;v5V$hLglW3-S%6%yD>>1NcL|F?H7@rrINZEl!6epB0Lr|A|r;*5!u6j%s9_q=Hi zCs6;AiLy%}Fec`iNJ)W3<>gmRJ2-*N8*~li{9+;{1r`LJ-8Svv1XlcN+DUalW$v0t zNr43czx&7@3G9AI83SO>BU3Z7{VjQF!j!oAtl10GKC1A?UXli1IQq(@FQx$qm_n=J|{5ZtLY$7`+J_4>9gO#Wz7%y z^IO=M4-uaaXKc-+oWMeR4a=LwqEzEb=A%>sryR_roWO!WGbi&0u7QSC%*Uw&PP>|) zkUL=88fHw7pU_^9L_8DJQU~Z0}`7pI58mY{roWO!WbFI`8UxLl#k+4mOnUoV)5b!gam+)ph6;3w=WuzH%0^{0B zjG3?{3%E9fsSR*2Hk*m-`Pr~N!Hk!|^O;AonQ|ResG?1Z`8~&InrPv}LE!hM=INZk@D^tB66N)lW>Pv}L11qib5Bm7UVAfP zg;0tvMszTf(g6zsuR2Lh@Lg9kd1+=_H}hI9M8lqD!bME5>SgZEXDB`Un#s#k$NHJ+ zt3%BOnx}C74+fjJP|YxEsF{`tm~T8}&M-@vfB`cBci_6%Io3>!(*>G0xQ#cT=Ri-s zr@A>AC~LAA^D5@E%G0UNsy(25m`ymJdCsABGnvfzx#oK0&7@3!+)TnGz;4iDp}9AA zz~bGNzb!R85+@eO3NZmw0sn?@&~>fZoy;ye+w8zT&M8cOdLTEWVWut1rB33v0MyD9+_;N`=20SVrelYRm* zuSQrUz{J3$lQ9;A=@1v15YA06J2C;20_VfjSczk6Q$h;4*Ewb-RABC^u#T`SIpHL? z@Q&#TWTOOxYe3f1$EdK14D$g`h7rvYx{yUe7-tyPI$M{++w zgz9kObiy+($o7i~b19i@EL^O4F5e-nJ|& z!1>VYb;2aF6>&Jg^uQNNqb)fz@WZbOm>D=9K9)=Tfsz?ms0@?)&V}{06Qjt4czR&K z*C#BmiCo+x6FZZeuQ)R>3~Q9QlXnnRvqbV3z!1(Ep0`L0r`vXH_&kX0nAnl59FZP4 zs9PeY)yaoPJrnIKQwFYiI^ie9|o^X#2mr-@crsU%n^*;G&d1*1mo|H zCcdXC2QbBN9rCQ0=Lcq(AGkBrKa*(1U5EF@L@`Hl$xAel)hyEPmLv&wE=cqyQ?y|1 zhmrRaZ*Vm>EKJ0Nk@?{FIuWzT=EHAq5;0S7K5TuP*p!kfSn9$&7cr*y4JjQ_o*s80pfzT2$_+@mzrV2tRze%oDWy$C1E1rd^oTu$%^XN0a>;tw=hwz}J)g9O zJag!KKdH8q#P-$Bh$S%aM$!=Kngk%04Oi-rhe?<|I3EIECSm&EeE9vZBupQS$}N|S z>4WoOseQ7TNUy7yjOl~(p`1tZ%+d(p#8zIA^3&E}m_v9nT=Pk;O-Upyj0B~S0AWs0 z@*{4#Lv+cQO*kKV#wBAm;e7a_GA0+sXm3oi=%4+^CS!78 z^vUCsF}W}f%@dO`xiB`?v}8;!oDatrC1Y~oeAu`w8Iud=!`_w2m|PggnDxnvODDAZ zyvW)-0rW%|l9Noj9cF2_@(i)+!K=N=37ne#r)1H|*B?&C66KrTsR+W zuP5_+G4RLDWK1rc4^DTIF}ZL)RJdOXk+2`zGfOhy|7I9I^CEc+bzZ}YkpXiTuf${U zFvmJ21m|sVrcz2UxeWv?rU;%6)vKlOHDC6wW(s*4XhD+`4p*<8f(eIlX!1;HKv_n% zxNUoeiGycCU+)xE>GUV^4~O}s%;pV#J|G42593l(SPJGJ&IjM9lnSL|`6)5QmG$t9 z3WD;c6w>PfN+cFoZR$pifUJa+;}xl+aiKcu05ocxvWu)ZM5qd@o2NYGooHIi6wFSX z52M?qV0Pks7~VN$0byDUC}$3KHuh#j$^@EdJy^Z$6lbPr8Wjd@Mx{h>J6kp;r83d; zH)dOA;7hVFCvivEG$rLF!m$O@Q)bb~H3mcz%S6{_r_>}E71UXfvYRrSz*C*O%4o^15$5uM_*%TDq(Y*8%JY?7!^Pv#970xug&R^oa&wxuITaHO=fkxf zsZ}YHI=(^6_7%T%#-dZsXSIqwBs|499we-{3gx|n}X z{g^tRe|mgLoy0#kSf%aZpRLNJ&E#$H(l+f6C;Tj^k(R^TVosg3Yy7jbGHogUEE|}1 zjDMaCO*_Cp$3~~E-$b}KK5YzYowfU(PT?(^k;Tcp_AonU+e~krC>vG)E$kJuhtymB6j}X}=IO07#%7+CD3{ zIPFL3v%4-y+ei=|VwR`zr3==5RT||)q|ja7>NL_3Fi|;iUD|Bk^0zjmQ7#G#jL=PK zpSTdZAJZsDs079j+tT(C6v#y7vOCknDf8>Q)AmqXFnVv=DKddW`_p!DE%y5@vB{ObPGX|D)ff@`%4An-Iqf^!$s zW}$G<;&NJVKIc4pBTYe=r*qI&tZmSX3e4kvnyp0n?42}M=6j%~6JSZGP>iqF^3$a8 zHTQ1XDWU+*R@eOb|F$zpy#-n?+KD`Pzv-r?-e`@D|uMwzri&4~$Pxql9367qsvhmFEPg^^- z_~i%}D4(3ZHTc^d6F*`c%s3Xb%}yW2ox$z#=^wb<*CwVHR>RNZ%hH>2i>bXP{V@Oj z&f4^i{Bz>^^rQSUVPpCc6bbCMOaDKWBmKYcPU-(ayVDQz|Gz#U{Xgd?>Hp0ROaHHV zB>fow|8FOy|1Uc&{oj06`oG=z^e24cS#&l1+W$&hQ*Ng}q-_uwj< z^I1~av@hw2)$#9ZRWq7!mHE}m5Gs3KJ7XhP)jW?3p|ZwxGJfJBJNrogzpRx0Kip6H zf4F}}As1N{oU!+RBr-#hH$zrFjcn=bwT(Tt1-T<$YJWc)xGEwOn-dMFRDS)P$p z)#{*>=8EmkI6&zdnDc!>?aV4{|DKF5l=_(Q9qXsDyS?<~*|~e|9icO1{6>j_c=C7w zb?HqR2ljqnh7)`LQwAmTsEm}(G80}nu_MbyDk1K0#(8VzQGL7<^ZYsE7+q2sbaqmA zci3<|qbg;x^>2OxYTT)eoH7!>xU(6Q_s{(QfUZ23F{P{oYP^*3!~aI;`FYqcBv8j| z8LR&{phK=_tdT&$?^Z^QQkaiuA7_?(zFaw0aAIr#c>I988eKX`@qB*0pKoWkTJ&qnR35 z+$?h|^0hueyNJKsi%A8Cj!o>@ODnl|& zrBP5Iyd0kSx6J0_s7%V*Rf%M(F_}9l^9qgt(UUTdQqF^j$IqIB{d>f`%w?3V0Ype< zc{9c|gvTp0d%Lq2HCnqux7m^7W*xSbw7M@Ri)ybj*pS^>$7JD?_hnhs=X@Y*tStQ6+^kJf zaYO49S#neKIsgB$_Sf-I9pC>r{@J(-AxMBExQF1b#Wf@=*=$yl-Hpq}-O%EmL5c*o zK+q{}ZE=@Eixx_ul!g>3R`{K{cXreE{eAo%kDk2#$YW+F_df6QJkJ@q_s*ReAIJay z>*ew1kwH3$+?Q7Q6AwvReX9Rqe4&$Q)SE}+Z7{@N)bk?$D<1$1igd54dO(oX6u!WgmQ)AJE1vcm{XfcXfizRu60#ydLv52RI7t z^L5hn?y}*;*dll>qQNSg2@U+%mHy3WLCatbLaPPi%e<&xyI`3_vV?eqPpnI4whwl* zmk_b*`;Mpg5Ui2(-%}#w5CDbgHsR<20NF)xjo6r z>B03$Vy|GtZHF18Gszti4BGS#zHLo(_v?9*oc_T_a0W3_91k|#)F1{d|6m2fuVKNb zZOLy%KHl`unBWhHf$={92>{wtLs0NUSHV{LRvir6sxY(ZciP~awqka>F}T1=%=V25 zUJdQ-ss^=aRcx?^pQg(SZo-dUkRSXlM~E#9K8xs%%oGK8?vPv~I~Ft^PL9n8#yr)A zYJ-{;(YP!)nA{7L+L4nFBE6_$cJNfp2;U6jM~W9?qLKi>RPhDj$tA%mJEB|P(35mt z6@2KwMc;Vt`#fVq-97urI(I+7xVssNzhAIkaeN@KMeoXnQbb z<1hhcQ$?^BnnV7s;9oe0^u58W|GOJRQ*O{;)WW8M0dWU{CyN|P4+mrBI|wMFjs$PR z+z|>V@B21*8kZsDRPao!3@c9uH^JQV47?qjLxO%7??nr*1rN0$`%4>o(_1%#yCdS8 z0$2j99SxyP58n^|4YsIY*#l34pYZJN7r}53DwIz9LulUE0oqr=u!E{l>iZ^mDN@RC za%p3*7rEpg;zLtE1_z3^?4cp31!llE)qwWmc&NxaghE%wC=as_`HtJ<>mG8DXP?yx zF`IX^UI=Tw*|DWQA` z2&aH;$LY$gq$8a~<~v-ZKXaWncuP%gVri>p(k__0aWFy{yxb!mp>D&8T_-7OqgHmu zv1+c=n+z`tH4rWJuSG7k3w9uZ%cOP4fzDC`qSZgPN~)pTx=7n$>I^%Xz{z$bxw{mz z$UXpQ$;yG!9^#PdUJq%oed!iAPcmqL6thk~!=r$QdXHcxGk{JWEdAC>;Gr5OU4c_o zuxkK4JzVOCl$IlWJxY4fiX83aUW+UlE5!_Ostt1vl6oL+RY^6%GpY2lD9w3=6m#EP z4K+|HHP%d;%y8*(k<19Kw5&#c+OLxi!IGhK^inHpAzoHTNijq5YEj%`q~Bn9u$nZ0 zu8x;}Es7GKR6{b~CrkGtZjvqG(uDTUlFoJ|u|;m)#A}iiwyr%S^`Y*QrPndz4zo&4 z2*5hf6;q|Lf}ynDbg4ButS`=z)+1~CYC6+5v!zjJUINVC{u&?ZJ5M^pQLvdFUMjtU z=)oyiT4629M%S2S+qG7jgS2uYy}wENP!#LhR_Qef3s$;soAf&|H@8B%)=nrJLZxvw zLhk4R=^@NRMdl^y`i<1yPSl|F8R-IaL<3H9Vp2ug(X8`Qy=cI*i_(b*QN^hA$a~g+ z+_@vQr~WsjGi*ePM}Lr}qA4;&3W&KPyO{gZZ;qvGYBK`7}nVr|4(kLW5EzHoXtV{j>l71n|wrG=#SLk&T*-Ruu5tRU{9ldKKn=Xo& zV=r5XYWD+I<8@@OY)Eky zY=wTRfowO@Oag$uv}q$5>bZ`Yk}tjEC(E~DY&=O^6B*_OXEoN!rZUue@zn?$TgWzZ zHu}~w%y^e!QP8F_Cb;3`SQ02Td&<~R_MNS$O`q;E%qZ0i=u7AKklA7?a5ciX-m)C7 z&8@yN%rmfRtcLw%sHLr|5y}V3PH{F<2FoxbvTCd!hR9~sOdG!u7Hu|-lFh@_DZ%x= z^pDZ9rZr=8ew+-pLKW)N(_k6q;|8V;g+N3tdMBLxLM5B#MVy-q^ro|uW$?yNpyQJ& zOGb)Qnk93i9n)lKnDHB<1OR?iogs6q0RZNMBXgYb=2w#~+aff-p2Vj>wh^fYYbAVX zZlTP*W}z~$M0Sk(#%q#nbB)+!OqSKH8Jodh%3g9d!)IW-CSHwp&yoS=EDk9Pq4k%`rgBAgti*HhDU-E8aG)RtJnj{$?(X%nbyx`+ zZTvQizwIvQt*VFJEsD-UB6jWvGq31PL7X zSq4d^2V|?TZuoRiCP5BMa_FWLvTII4DA3s#Wcx9vW#PPRhNN1DHe`=_8X znGYTRLFQy9(o2xYr(>Rz3+e^6k~`2PPVy&MdPG2I<|=PvEpq(XRn9ri^^mU@+dDd} zj$FdC$_8?0vA?1je)8Lh*#)c~btRQ~-NsVecJi;0%moC05?9!56rCL)pM{jLE7?A+ zn;%`(UH%YF1Q25A_+Ijcn1?53F+7NJUbkMf(IELYG!f8LfPuAG2g@fQdpki`4-&Sd zTTfazQeIv&HUQ&HDwcH{NS}|E_eZuiN-}2&JjAJ#$!*Eo#!UjKjZ{7q$xFndfR*MX zjz_a}@-9x23v_Fod=<=ohFednPLuEE@!ljyeo!KoR_4m*@B`*dlsDxEocL0HUF;L- zrkV0y$T(Ee#6|L0&gj`n`Aw{Obj)h`TF%F6y?iyVt9v%f7xS!UyM_Dw-SSh&poRUO z8_S&F^{Cu|{1D&Ki|x)FMUxN7hl!0HJ$qc9!8^+F-^vqkomBNz+^F_D`FX4=0$B=V z7jdseU6AkNZVkL7KgzR`EAoEG7Ok48pZJ(2=&l^|6&17xN>cGq?l0`K4j)2xv893c z^r^g~u${f`cX<}NF?jKlydf=k zEw2>Aix#|0k1#JiGq0d>*!nl8UltwaMex(0^AtC?1I& zCVk?fKt1gi9Sw-IzK3D~ud#=`6kB;U&8??!;MoNY6$(r>?HHzrrIVT|?zo68U1mpx zxk`TQtiV)BhDermSGZA+u8Jy5Z4@{G!aQzU-#!Z1l0$Ibg29U4xSQqWXOQDAV*pjqsYOs5zP59Uh#pG`6Wq#+3e4vjMk*Mg=ww; zAeH`@p)erBGnM@O8tx$e%2Hf|`Pl4<)#D;XJ`cgPsfs&11S4iDcJk6@3l)cXxV~JZ z&^e0XYFMeT=f~!+QM|x(EMQ96JJN2t({dyGK4! z1n}FZX}?+=l2xgw%e(iLuPhEpdZU2v2L!X8eozb-ZP_y@)C)7OM`*M?Vyk?J$6bXZ zxpXQ~Mis81TOCDS_3DO}Vf^tASTIagU`JzpLeKGracI-f7;eImR-yZOq)l%VO3hB{ z6#6H3U0Ao!HT*QU{-JA-Hvvjg98={)wm+@3r6Y%gj>9-jFvWnmJG96zLdcaEL&}dgEW@bFt zGnppr4n^%mCY;=fhNf^1yZ438!s_tn{?J>92WAvFyyus9IgY>lxD%oNn9(j=KAL39vVXn^-OICw{|MD! zIt0d-G~6HROt-%cb+Zx0vHxfm=kv$VE;WKB0BcXrTPgQii<}qQDR*(sp$^J5JnQ15 ztizS>?52d}CN!sX} zxn&0DRX4WZl0g$|4JSVjQm#d`4no(lsz~Wbw24*D^!XRcClV5Pxt1q=JVY4{{VmIC zMk@2H#q6(Rl;^BV$JVYzPmNcu!bJEnt+L^Qk!zgkQK>RqB3VNU-xk#+zfKBkNQ30c zPqvcH;#{9fU4uaKUAPjnY=GG+FS=5#MBUQ@%U1|F;j}P1AVPW5n!FfT;7M8=m6+B4 z7z_cJI>HUugeYYoCQJmBqk5$iabKY1@1|1{mGDJ@z%4gjiJAWL4^&@zEJKN#bIVLL zlk=tfvX!^3L|*Ijl$hsX|KjyAU#UZe=ihm`7b~xDUVltbVwNHJ7q7vSl&$^;y!@sr z*Kl4}rzuO3q53xmG@PLv_CMfNXSQ+%kNutVl&5OKi$c_r{{bassq!~2m3)bEeN8B_ zisVbTFIC<`*T(@To;g$B)k@U9r$Q`!PO7R!-M1;{W5z?(2>mLQPo1E#71qeK^W6F~fp(fI8CzSJS#ca@NB`g!e+6`L&tnwl+eRoL--w_F=N3JO0rHYW9d5v{} zLbmq}CA?n|vOnHc*0m9{)(@4#FvCHS{BO$U^!=~OT&(%@^KZ&0teX)m|M-W}2bQd4 zhP8aJgcS;e>&7G|cSATBF0vZoD}r%ZX*QX7GH)fwu1$J3Tn|yv%^kt!kZR^rEoLaE(wF@ zX+l`cSRVEj=5d+eb+{S`XR7K+KGQHe`l&qZHx`AGiw(T@?hXq;+@6tt;!?nc)lmed ze;y5U;gL}LWY{2{jXoa+V^86LJ{QBR^kQ~gCzTgsg#6E|dsFxBs^#c_HjCp;0LG4L zd#V~Zn4@~mK-Cm(K#Sq3W84>SN2`wT?5Xjp3hu0F3KhH}5-bc?s>*rRN2O}c5rQIA z@LX3oz)!D&?{bCgYlCVYKY+xm;K`Lxy4wj zo$%(D9$KdA%%zg7Qehq_Sx_mUMqwI03j*WSh4x#kTFVuGxn4zTAXP`|zEQOcGcNiE zr8>k^4oh?FGRW4-83l%K==@^~^%X6#Vh`yqiZ>b)Lkwj12 zRYB*?>K~nUUj?1Fkd^o|B@e&5D>l@GbXJLUT9p;l0iE>8!ae$+LmnV z9*&vCX?}76?;F$MZH@N~s|a#nqN*7w9v^N;`}7QtmLTdVj1wA?7rnx-AkQrw9zK-B z4Gga%wqr$o!k55J-d#_4z%d{k^TvV^VxSa2M_^))0qh+fKG;t3r#OyRjt$4`)4&EX zjLBH~)%fsTqES>Q3$Fv?39ye&4P${NWXFWFz!I`Pns8X0P{_852>;4a%wCTR--#SY ziDLMe!oR{)5sR85(!yK8*o6_FkQIKrjhIbd9saQ?((^286x97>IP5O;m^A03)Co z0rT+(oEu5{-G#alxYD{d@mT2RKsA@cC)t2r53h$G#u+3)`qJR z3)4S=AwuQwBTr=N#gcWjM6Py6IE5Zls8NeXcT82s5&x|kJGx4#?t~G~jNnIRhO05J zl>uOuc(Za#txmNiN9~4ql5oBH1foI!)*|||L5=zjN;vs=w7MMD0%07Ai2 z;?=0v%EHNCC#jcn4pAv;M6*W$2D7Xr5iTE-p@#1T1qohdsWERM1emco>KBMm7CG4D zt7mc!Wrb?Y^6~fVUw?t z0Q&Pxbtj~pSrlOET9drQG2ZmqJoS08lF`#d4Z|nqXgX(!8fLBw*+DDRFovJo7&bNP zyjpz{GyVhW@np`kXC7p639R~YYm<5elDY^(FKkh-wiWqL-l4AK{3lZj|55wYEqUq0 z!_YR1ri33=!=&@T-$vA;Esv{V={mu{$0yWx`LWketKrqKP`d7{8Xl+$S?PH-%x@M> zYjjb)l9!&pq;}$`J-MzvYAqhS@0QvgF|mzS{j7e4(FD#bBr_r>c+*W!)L)94kW;^? zy@=_x+J~n7t~PO!uU@KsVQH`_;Kyd~)bLnMuk;u}Iit zqml4PIN+dBV!CJMep=zBNx>K>#15S7O3!#}8ruj?-|M5f%B_0jr!mLu=Ej*E;niqM_V`#0EFCA5P6*QMfR*8xONRt&cppJuN;NmR#kW-!i&v{PXW?@Z z=F-9#3)tv5OV%gBg8M@v!xfJGDp|uDJvu5?Gns4lAXDRljRbOUvf72sc*)^5BU<-))+!|9x0P2X5Eg#*EG2yusw!jYydCw^M@|n^iT{ zCXm3x9p!w4eAlOaB|5hO|BIX_;^Nxc@QdK^f;$M zeF`p|JnW(-g%Q|yS%Z0KEnvL3qCtK3D4d*qT~opcy#HRa0PBKIw=_2~kt7hu z5tw*Svk^<+(tS-d=4F~d;PxZUN=6|67Y$-mEC%6$Cz=yz0!^Q5rZEELm8gb5Fyi?O z4eHiFIC=F;&2C0O@kX;6>w*=3YFeUWAnv^;gAus@QG?m6hZ(WK=aU9|{XIvt+3fN? zthMM?Al*jmguOm(YOh_w2pn?MqC@bblNPlWfe--^uG%t2z}iELc}FE+4D{4q!*s^N z$@{&udl&&z9c>vl1ozg}-bNGX?xS7I2wZB2SHR6z`x~0TN`LKCMxbR=3;_x%&PT^z ztCrfy?AqH}Yccc61P?xKqeZP6CY)T>UaMmStOB)|b(RE-9v!tW(du97tc_;``gGGG zo=s3mcP;9ntw8*3Pi;86wycj9JqDQBS1UogztaFMYzn-U93O-UJ)sBOJ6PKref|4G zwVT=XXN=IU!B+mOBekf>jDiP_jL{}B0s%qz4j^v4_7`OMC6>lUwQQ+uBZO z1Nv5Ky~x2U4qo)oPujPRW&_UrrbSJ&VWLpLoCZ?RlYeL%F^~NGMjOb|Iv=zFEFJP$ z`8v~E45@#AU1m*8X+lP+y8F;ULv&m1&GzIB zw%9X97mTbV{h2*YNZfg;D{VYpSHyHLlj+`|;V!+Z^rj8NbUDZYN`Jx-$Y@pf`a~A4 z^Ctaz=v^t~#(|DvE!r2Yv%~D{1wlA~2Bzo&SrA5~>joi>96$i10qu|t;R#5S3v}0+ z&s9Y_1(R>rMBPVrg^H=VA6fa#Y3MK>Ky##t;{p^%t)zYl$0SMqJmuH9LYP-O^znO7lf?baC!x;d?tr9KqC7hDiZ)9@E^)Gs24z zuHzligrz;}NBFQ*+9+ZmOG_I^3}Wf6<`Jlk>fvGv+P`hYcmz`lC%*vogg7Jiog#Q2 z@Um0H9!&ig#B}c&;X{B>0FCM%(HX&t2>JocLkqRx{mj(5^p7}%rZ!+mL@k>01xhQC z6(DRev)Vff({HFK9Pu6#~Owf3q>9NSQK%N*>!}7cA1ulcC}b0 z+V$cq(XO*ABJ?$}%av|f8{y47aBO45pEa?;mE7DUb)#QwiLhpd4BHlQQgBR#Xvpmy zBJuURBJe|0!4bONS{veWG{S)v?u&SB?o!CT=;v5TtJ8z(Ufb`khh{r4#uDp-1XEyxsDIy#r zsZd`88;)B=3eFj98~KnSx3Z56LymC!llv2@YLRv4W9rjJo{{(1T|B$@Bu0-&-u3mX z9odT66J0m51-iDuRcuJ_iB(?WrP?=&d}_WFZR;Ny#x5|WX`}=C0!Q9ex{!cC*w@FS zb>uH*4^x-+k?YZZWtbE&iz7PchoZ$2f@IQ2K-Igv~6D{GU0VpufGKV0WU-7_Q8&5kCw za?MhHn-}>rT1s{a3YTDWGU%!ak+rY_5|7DRSK8srNP9~q88ss^w?=RT;2a3-2xdov z=0%=o6nz&)USn<3%SDmGnC2jfe8t?hV|ipEYX$vQN4jF>S_<)he?w$Fx_e{fKt#vF zp0`4)#}EoOM^-;<`>H%L3opV#ZEZ?+MOs-j-L^M!724Sz4DQPQNcegHMy$sog$D-1 zPej@=<;IPH(i{WU;cCC zFRsdlcJ1(w zk*ojvwTXbS<*&%?n7YYM-ea%d#IC=?N{_w%UK{;HwEck&dYJO_k*s&pV_t9y_E$RV zQFD%jlTWzmD;R-vPd&o*dSpdah&8@d4t;Y~#QR(cgOm^HL+ zdp-18AL+CX`aDbv3!Fkp(MuSkEa|Er&RQ#Bg!)Sty%+I!(fiP*{q*gS?iPW?ySG0E z>h+ALe6YR`l4me+8?JY!%|_}St<5|$MwodfkI}C})(Q?>HcoG16l(?RQ8$txWiSc) zvdV+LSLkop1JY%!{wK`z@M@%?vHE1jX>Yu~4MH~RnW7Ihi$x35_5CsJK7d5hpe+3| zK%&3p>fx#LNBV8Peya_nsU`YfS*o6*AB$i}09-}_=j-c|#JMqER5eroCm1Z&jOaP~ zFOY;~6foh==Odl5NPmnG9J5ppFC9P9MqlZ-vGm+>eM9Jn=+o8uNyyqP^oD-KezX3l zWS6)Ai2D{j>TB>Gadj6oXzNU(zZ&RA8*kOSA>xMWcIzW0F1u|0-~WqK+5f-)r(QUA zK@|+e9`DhY+DeX#1iv|;$Gpcb1A-)BdX+ofcTf-e$=n*F^dx7G=`mx0@!Ji3i0?bS z8@+#Azg!|<(b?bVVV?vBOCOxnZ}k(iD}K>?c_KPW^Cgh<#*!aw416KIi1+$L8`0i- zpY&64WCTFpJ=O;J)<=-;nzaE7MS)uohw1HLm{}7>)Zl1iU+{7d zHK0B){}->xVFm@~^*r1#2Ny@xUht*OG=|NXQOCb{W$FyeIIkvp17TM}i=BVL6eJoFIIs7~{|jDSQw*yy!|!Tde%S^) z(kRpzuwhXqYU2tE6a}aS*5+n3JImaRzREM8-V_MH?zdv^~tXVfdB%m!86Y2 zP41qy_ax^}+skSB1Vf}1r%RhnHlUtivrEu3(+%D2%t&?S;aedZx4`fdA4sT);RGLE zbX;QC$+NGP8hm&M;8$ii%}d{{Hp~&@iY{4an2LDhPr;D0_GahF_nMujp@vhKJ1BrN z&;thysoa`=M=aLVI%cuv`f~3;48}rDDMqw1jNt@pgYjCKMkw-FsQ``)G(+TtM5m{k8a|C zr?s7I@b-9^-?fU+NoC!ywDmNO@x;<|79HkdjrsjsD`J!?#V0r}%K^ za!Vs-Hmn)26|LXexE0fLS0kjhHBRO|kwZWY*#vbkqQ()`Y&LW>MsPOuyBHB)vyf`6 zu&zebM`hIrC%YThaW)0LjF=lvhDE{kqcBa9aI&eNaW&`Abf6Kl?uP&~V2}~@g_A{$ zy+e$doX6B*XdbK@w4&#R8>eChDTE-)}M$34lz*veHkBAl8e~QtSly5gIpwcgm4=i?# zoMZfq8I%ZinK2#c!v#k3@Fa>@jO(?;V%%R#jpLDBs+0uY{L7KvUTu7A3AgXp8&TgA z3A7m;sUK_WLKkc@n#U*}09!_STHf~1@ zqu`-n1ERBvav}@P7=J(jjJJv!K>BA%?a0Xok+tdDv&MC>7S8mEo^;zqE%L64+ zHS!U&ujfX&yNX%w6;Uv>7Y^96Dry+41jN9sH$+Y3rDRhS?3FE)R&KMDPTUc-otJ(= zIYKS^!@j6&2k}__am%rPeiJpE;|86LQgV{}FGPLKsZYEb#a5?c7anmv3I+>8*5&)C zuiV7!zMrFJj={x!1PK})?MZU)HS(e#I!F6>o4wU$MD#+ew@27W-LpbU82Glwm3Dn;l)sOCJ?j5ORy7 zOL(cv6w9%zzKlM}OS5N3!#0qDmS-15n-!i%qRk3RmqhpBkyf@k`dth0*e$1{4`Rl- zmw$}bm-bBZqHY(XcREV;(qlKH?;#YX0CdD#bkpbPJv?-hZDI(Q zysmT1eO~I|5i^vRYU{+n_7H*yUFyY{J>}#RQ^Bjy*=8|&xd`(EVzM!#Pav!!t71Mm z(v>}8?t$rId$3?o%yfkP!~#bE&VlY964Q!Ts+VId7MvazQ_5+ZlrhoVJu$ zFp&by=MYj+H>x%%UlwCe;ca+#2eWAV7RR6#o3db1z&ywa=B~1s7_>TOJy*tla}4Z> zBZS17oiXcpsd9gexk~Rk788lt1{pXEB>in(_)?e%oo;P5Dd2LC4^Dw-v^FfZ)1*1Ub0u{qly2!7|ePEFj@{I13JYz zkU0xrUF^2+V?H2S?=pwjs6}&Q?TF8XZnep(Xm1<(n5 ziA{jz8sQMSdqAue&z>9{dxd8=kBEh}=7j_52FE_-rJjmdXbFYVyQ)|pSkj*%I2vP* z`62W>Mq=mDnUiDl+(pY9E{aV@SWW?Fva?75j&(Af)(f(O+36~{pgtM zvCEu9YJc5}t&4dmW&tAr=0GkC_Oqk6evbVhR(<;Ovsl>RTo6%H6v zHePho+t_=aBHiUqahT_>6u|CsB`?*B+b0GQUFsJ%vkp#4*4+A$+uh=}O0J4?s4jJn zLrn6b&;fMCJRG{bsH!aq91z!&#`lWrg=y(62&7m4I81xb04Pv+Vgnk~9};&3A*Go_ zgs|6X#n8B(h>Ak~iLz}(T%{FIS}{6qe@!TnOAqCa)OT#$G|W>qGpA8>tUT^UBj6+4 z`u0qZ`wKI~vA~gjPi)=jv+TGmTgg?bFN|yM4C%CKap^9Qep(cF*&fnM%i}s&ORnxM zi)#zsq3zbi?S-H8;l{WJj*x!4GtR`yyU{pU$mS|Ny)UjI`~TyEaha@KeJt)HD-Zi7 zZVvnX>#4Yz?044Lxccn4c^Bdu*+IJfTHG8bNc-Q5`|QBdCvojrn)o7a7CZjKA8~is z@weW@sTof94{;&v|1+%PpVzK|Z~Bvqb}t&y!20ojSd{JFJ$@;sp)o5fViLf#qjUPj z?_zwv_#%E0)9cpgcwc7cTX}pXD}SPjZ^u$^T|8y>o!7@-X2-2ej2CqENR1bCU6UR! z=z26O{!4cJi2Qgrc6?}2d<^@2Y+}5i>)0vre=xlUPK%$y%1_OT|CZs+Ch?zzv@9Mz z?g4kNiND0sF6-jIX6f_w@rPKtWn=thmKJP|-_6oaTjOEToU8O{x%mGL+v6)4f3iQm zVT&5*-jlTzekA%q{9?&LDkk~ z#N*GRCS;W)wm!M|hm{+d`Z(T`HvcugkwkKxe(@|m*^V@h5|>#%iLjs1TW}wCjQ3vx zqx6c_1zMte8IPD-2VC2diVF$7>8w`}LjRqL89+*6-^C-ATxRFf0q^5m+e#`)+~@e~ zND2U95ZY{eNdjg9xnPF{#LXt*wG|Muu}?rW+5g0v;*d~?7}5yB=-d)8lgkAHaPBxd z)-&OSD;Oe7d7a{s;6;MkC-~4}zl3B*NhN*JDnVln>9}?Y`8JTA3rwiP(uZ9VUbsPe z?Tdu&;27#MB4H8eL06ATaA(Kw9GkESU}<=G!ea+Wzc418mq6+nEvENk5@0!)O1d&G z;TTIJ5)!tt^F5LiT0Afd1mLx23;wsTG zGZQ8{1INoG0ouw+`uoxZ71Lzo%7km|x8ItC(aiRQ3Q-fQU7{wn_9hT^+~)lW_RPke zM-x^fc9|9EL5=)Pve+_^YeU>FC1n2hnn5nlOl(Q~A0@zZho#U^jLj-?B%cdStw_FW zSJ?1nVxk8LV*gc={2pZcl7SeXq-<+Oif$x~A<(?Z$sM<@waD^flP9U@nr27VTuJEl z-+3?Km|e()?4sH<_i91~Vi;ydIfW+>K;~K})gnii=eMDDHxrIpm$rmY3cmj_p`yl^ zb4SGa(q4BGFk=L>WdX#)Hpz>OlgEY8ZyqJ+ok*Wcq29DlWkMrNi^EkHQEBC?gp0M9 zz4W4e;t`3ETj`j%Ld=bIPTVQxJY5q@nh5`0(;@L2u`Ih|V!oYFX4@mNLn9$KM3%T; z{I7>1ah0u5b}T$`qLq-7X%iQTWgiTQsSZL}VPfJ@F*hzVG1-Z9zSm$lu`5a3{olnG zYl3Q7l_UAfx1a&-HX(5trtL`C)_^velxUQYyN?<;(^-=fJJ?CS5i9EZs`wV9&CEnZ zMWs+tYa{rOd0$oe6aO`d0YuS1--i~?PCSUHDSv|*`7Yj#oSc`4`C`LNMlHU9_Fj+} zVoyBu%{@uZ(!^Z|r#H`^p#u@krL%az!)6wQtd>a&IfsVrlX5tR^&OLrBJS5LG1Gx)9beR@?|UR2 zw-YobBYHshTD(5Vhkojxgqnk4R$Ig-xvByeYWqdfEzZ7jc#@oJB_Efxn3sN*CJo~@ zDU?a5iRjhW_1K)~OWySW8v=AmrS4+z+)7T$t4ZbPKm^zvRe>x0oSM|vU3Afy2^M$y zPfFUzeRylK#j{^dO)|&PsA)-`xxK16N$|c>h=Qzz7KD+dN#z{DVR6!1E|cfVq&Hlf zdTWzl4_x82pVuX2@B`wvT1tCtOPcN|9&nXfaM$lkg00bn(~Jj_5<7|6htHGD0sM7k z(sX`_kXK3T5MR0eEu{QcR5c*(p2>E?8}_Y#B~9lp80DJWx+e9v17XkO}Kov-(q7CY#kSZJ%sbe`hBP!sIT=X7#`5mTXpkbl+sN`XPgo z&FcFPPEO?NuNY}5jUSynnX506TX3%_lFjNbP$rwz_net*R{!y=X^ky-xC)3zyHP}|CH}6@{c%W zkzaYiB7eqJ3&Qa07WwVIx5)2)*CKzNhZdn9K1wzRf9x|$X}9OeuX&~F^D^1o_Fj6O zJb*i;@n`eZ_PjEgeX`5Sl*wyEudXJuPu#nkV!2PI^)`*JnNR!&nS5ySVACG4k)=IH znVO2xLlVcEFmJ7^8#{(g0Nb8!2{zs0wS1A(l*+rFdJ5BB9;x;ki+Q)TCY?A^rDLK^ z=BW0GH67*xK20*!;r4r^nR+5_lPxVm05K1-J;^WmrlX{zw4xFD>|dxL4GK+&dk68> zW@TBGGilqT(3#!eW2QQmEig%m>tvH3=~`?;JoF+0z>KDaK6G}8={=@%F;iHIxK)62 zCy2KlNmESY9VO-D=o}N`&YzhvoqjRT)E;30g_HHxaBW*+LOdZ5uWedv!n|h}Pu{r9 zbifJ-7*?22FZ!ABr&pS0A&eIZc&;&>W(0m*XWEK$<8(S`gDDwJAYrrV2S#96xd}C0 zoGGwln+dgJmJmCcJ52i-fsVUPo3IKj+EW7;-1*uxjS+}GV8VQUCKz$(pb0g{QaIV= zsOc3WAUR?B8mmD1H>NR|j#?m)f68=$5g2vWgqm{0Vqn)f(;>vo1TDL2dgBDw7T&II zy=xkZisY-NbV?qW+P0IF(-W>Kz5F5l)IQ~v9i%V2q`)FEP~I)&0!!!jNO550%lf6j zOSf{GI3PuZ>7hY7867(!WwE=N{)f<%3lqii6_dsCkf|xKC~`S!j%39$Fb&1M*WwiG{o0`{9)o z`1Y%u`rk_Vp8bCNL&{Z_p1UoktL~;mVg(?9$4oY){aRBUy87o7xs6#0*I!d6qot_E z?DRY(no(c+Qk3Gx+mxm3cgkncm%}8fcbO?ItWyQwezZv)j0k0#;+hKYaCjwjte@Hp z!yQ0!$)`JkcJyw8)Gry$=q9N`D6eUmdeR%wvH_xwRJy!yBS|At>eD9uQVkAfmdl2w zu0pcpnEk?2TNG!9sM~iIq{4bw<#gtv z)Z;82M^eA81F6HdRG|vo-<~?U9{g^9DphDZ-k(m5VJhe^rCJM)yCLHAx|u4pF_pJc zVNud@`t^^gt?eQG=_m2`mPe@#+3!=&#N(Ge7q2_AGW8%k-o7ezJ!`wRy-(eZc*7#J zF|3NUpl*^hDI2AV-=8?86(Ku2SeFdo2*AOtx7#{9TH%&<)eR_4YLq7Q5>mf3K|1Tk zX@Yd$HA(BoB(ZIs_KvyWbNe)y%27_Qbx7;ZOn2#?=Elk!^iEsHn%QFm(gYVQ9F!(V zH)3#_AYHAYX=&{EDPzRz$j6D-u^pc#NOe3o4VFMJr_-fkc~5!TCieTGBJD22J+4m6 z#5@ocBB{d}Qv=dB6>Ju^IeBGFYibd*U2>WU9ij}F0`Lqb(dlf_v?aNs3B&V637rc? z2}e#6B^*3Wl(7B`QNr=_MG1AKX*XE3bYCUn*sm6G_O1~nN?RvN)O&+i{_93jqJ^8& z9t{={J9qUa zx2~mm5s#JWK6K8lw5~9_nlZfoV;W}lFosP48x~Mz!-a2drHHIh)Vq_`n0nt! zI}7uOW4hL&e>_aPfUukb>?tIjp)~M$+E*~o6~v);-llcJ%*6V?W_c3llW7_v-$;IjLS(4zxS}<0;|f_Z!j^Ifv}c>6o{_0!-PK^md2=8-bI@($Txqe|Hw@7aeyrJqy!_GaLdq zd*7o8T8@yz_xW2=?+QAc)}ghFC*hGo*iA7F~(jzVDsdR8SaQG0_}*<75P!RC}X;{ zWY6CwZERnXf$3H~H+t3~2}?5&Ulozd*;(!A!etrOHj+Jb@yZMvD@d1@Wt?Sc?Ana) z?I9hwHKP;ze|kkm1S`L>Gh;BOR+PZ^j`hW>5a znx8Y^8S5Uh_(=xlIs3nqy7@F?1g2*YU*iFl~7cz386V!4=Za4Ku@ALmECb^A0QT6_h#N7SfrrOeJeK zA1gEKf=Kj}Dzgqty)~J!nA;d;CcUrEeBy1k?oLi-AUd=Fskvy_()`TYZa{4N^vp5P zs?xvaW@fS987niN)rB;IX3k^8U+&A?%M9CmMEu?7cqZ&0yN5PAmHCJn>UchL20L!i z#mvjBJo>sQ#J(Rg_dzA6X?HWnA}05o2}%tpkcNtP2`)hcT=JL`G-ESN06haTvVB{(&;Yt};6+uQfevSLy0G$ac) zZr(#AG{glnVM5hG>xplp+un01$DkG2=H z3F_>&c4Bs;KD$28UWv{Az_WKv*@rnod204TOzZRymk6d}CIFdH28z$b+;g*K4gztK zF(DhXZkQQx1l=?-I~VhCpc>)S)a)D{R>9M=7t|oEM$qXqvY*r>qDIiVv$J7BnNSax z&dtVrOHggurFq%MYo<;2((JvQ&8NlLnE7+nYz8dN?t*0lA9-irN-FWoh09wy{<+fvuaZ|YO8{3dt$a`pW2h{i%{-NTEgN=l5t?C} z?`x<{|C`z8YNk!a57}Edn}j@cD8gX2Et&Hf)^j}XXyZkPKF7%AO-}CIb<~e0NTi7xOmd9fkqaFb{ z!$iqxOkmCz&SL4o-Z^Lav5WiWEaJ!Z8=PYvXSoi|@!{jQ9wTx_b9)wzu^eC+mlMXv zZx`j3(iNdOUM}J#jvI2;@B=ava@x3yr91L+Oq@=q!W=cvt}4zMg7^;HGJtJGJYsF# zNy6e$UUbInoFrJ4jSaNj7Und?_SEctM}YOFXGl&w*J9|h99Y;}5Y~Tr&O|QD?y{Um zJiBRqj(1JQr*O%yb6hFBM1rL<1tUCn=Ad?jG`AwHC?NK*A(bZ}p2801oaFpF9X1Cf zZFn^2Yg^H(pTA`hDHyopWX@5Zop72(q)-}kHs=D*b~vA-uIIqQoWilE zZs$Dax*U0sgPI*-wxSI=`uH~wa^#$?4-I~jsJUu5|ieIRiz9@41~j#2l{Hx!>_9aJ0|0=7u+N%{ABXH*UGShSU2VxlX)_fAGmQ z3wp9)u36B z9?jFI<(Ad3Ha`2!hZLX4wWpWnb(DdNpiUIIJs;Sd#me+Yq-r_fJkdbxm$p z?!2!z<(ix7gK3#n-V zyG2BMHy1Ng!vaMCs;5Mh#BIcI#XP%qVcrg&jVjH%#OvCy<(6aZR$7kTzRGfJ%4*B8!JF8z!swya zmOR42_HVTuo4+mZ8;(1Ej|JC*T5$J$Z8BqdKT<{JL@*4AO#N)hqNPdE+ z6}0@fJl|S1i524a4qAnr&w1vd>T9cfbKR@7$$!fO=553L&gOt=l5Yu^rumkDX`XKh zn3nkp?tlvc`Hhe*pZMm0!kYtyt>mKkCgXFT{GAA6g*O==wpV!**M9lmBLJ$lSNYHm z{qte@%Zs$$;QTBrNaqgC-){}+sFC^kn9h!Y5&#V#1IFYp#Io`mo8K8&k&xj0GAu^_ zko+J_7z!2ufq`rR4Lfq?M@0Y~r_Aq;x!L~*RxHg^<;(5CEMalA*sy$9D&mWLSlYd{ z1l}9z^RGIA@V_MH7u!J^lb&CfIi@%}--DIUn~?7XAw$5KEV|085rc(y(*Sdx!>s`USdDr$`NfAH#2SVnBDjpR0Q zU!9NnF7p4XS&s;-h^4N{{|(iE{gb1AMnQdXEwQ2H`9HY=QP>Bh-viRXyZJh2NasDxhc8QR)39gxiT045`a}FZ?^XU_JNSL$eSWGt zq$8XPGHf9|;$HCFRq|PEbze0q*!th&h(g5Qz_bM5i@FQuQfKkqHbl1zZPd7+gGBO~ zj%iZR*iLfgZ_`U}w<^Ha#?pWSTk`Rbcmr{7Q;`4P4J3>xfOIA5{`s|O!?p!qVY&g1 zavDj11gZ*#i}Ob(bSOx)0d-zi4uR;q@ z+x{_J3b@5CKyF8LffoEhJrfFMv2>-Wpf*c)rx(Pr|NE5`?6HOP`ji5woLA_IFAGfe zkh;$mf8U%}kOk3BgBBO`tPkn*9R=_?*%hkYRRCXbUZGw06s%(Zzj2@-p5YEUS|CL| zg63ibuJqVoEvZHQPZjt(0N%h$1$}@YDZE~Qnfhzin8Ol)%SO~8cgaeY23s3RVVHt(zc(z#{J3u!-xfcB^zYrc7 z2*+NTSeOor*D+khoI_vIkohf|CO%tRd^R2<3hKtTl9=yp?Sli*4%5_CPna$QvmDPvIw=QI1_}v z2q>CjCki{GYY}Q2OA)6>(J5F&kr{WoPmzk7(REOfIcCR>EHcOJ=FvreaqeG_FEWc- zPhM2OOV@`L!J}#c?0}|-@T^{61W$g1Qn%Qmoro>8C{T5kNS=wqUg0Lxlv#x7r##tL zRTo|rd6NM{$wi+rw{ZV7oa$y2oyCl{{{>SqtH|Uc zc}5#AElRh8v~*R`Nn1!Y>x&$nAdRUgdgl#k8x-#ZWcqcZ;@a5e zz#?ZUc?fUo`UKgEk5pGRDPD_h8~zVw3?0+FSnm#0cXTPPaD+6nU-5I61`RKkc|f{8 zwD^n!(v+~`cg-Pi1t}poW#;C ztBRMSz9Rmczeb3b*V$1VQWMK5)R=6rO}wcebFkRN3^{zP_zW|6+S%gW%y<1R7Q-T6 zln%RIT;L4pxA#Q>=KfUd$$qbXUcAyC(m}6_Vc8W*o&GF_m2fEi{%vs|DBn}NWWNLa zyy#Fe6*B|k?;#(^DkhcO`P(h2gMUfQy88QrN*qY5CWQtvv~dY$_WH3wCE-L^eX^)Y z$puVR6cAtmOgHkRc?sr`F95_-xFx*<^N|j;^CX|zmQX|mW-#o|whnnw(9@Bc+Lx%2 z=7=!Dt?bmmlCP~qT0xylmehonfVZYgNv)c*O732=oU>}wt7K7)Stax?xr=GoK=Kq) zIHM?l`V1_|L{w%92)jH<{Tc1N=`?AHje{UHT@hY##$L?k7)s(rQ|ard5?GXlg$TVI zUjmD*3)!;761bZYvO&ou&-rO%vr1qWaG|t*PD!+jn4LG-QW`$Bq`8+^I)AAJEb=Q0 znCFU;tsLRP%97(8w`@ZR+$9OP>6Z5q@@K;S^52r&(6T%++OL}igUW>GQQ%)~?mBAOT#%;F?7(f51mRu|PB ztUEC?pZERz|COY|z164gIrps3@_U|Na=SnKn7z|+c=eLmkCk8M?ki@aNR}G-rK>9M zHSn6sdp&<`<-HoOpFN`dGT*y-_BG|zF=p26>&mO+2TheHEN`wn;pBOKSjqS ze|u>5jl;$0{U`TL=Dj@o?Ob{}`<2-*^iD4?cw_dCZt3M2Z_RG*nqL0yo!N`Jq?ax4 z&)(T}uhm_}>SwzhDE{-$vqvTC|2TWZUhQ8ce06^1u2ev*>fhMwu2a3)5NBc9yngNYNlLh@7@7ybGgfgh{D_sqz~Ir~=b&b1oH7jmnU2hVCeBgemg zJG=3Kz4^D##71xntCJsH-1v_CKJgmiliZRjK~G-UXo$5}Hg5hDbN=qBjaKj6mgJ{5 zHNMLOla04E{zq5-U3X{WtKIo`sc7u8AOEiXdSjox`S&|(8=sfo&wR9Ta~J+S@rlNL zWcjg4Ke8n`ePiQ0^6$rdzi}`5{c%5N{IAmei;aJj_sUNi|3lt?_7{x@ zcjMoae$@!XjqhJ;vm-MI&nr-kKEmZJN@*XqlFxI#FcBp<%e5=R1KO__m?>s;jDjm+X9a z^EYzIX~m|SbD4$3qz~_TPCPoiX>B3nxx$-!>mV_+$EF_Qh(nrsifP3rGN~TYw76?# zCCPM2gyxuR$Cu+j8blW2OnsVEM9y6G$8 zyE9fEB8FVe|8={18n;2#nA6H9V{;RUeiHh>8;NW5D$N^$@+(Wmb*!e zAM)+~LVu>|9dXM~=JXR|uA`QB);Ap{CTwULDxSD*WnsVIQt!%3qKOwzn|!6{xuI!@ z2(Mdtuo%6esh^mC&B{UImTOnu{HcGPn<)B*_Y4#pHZ-jl!=Gy!B-U0 zscBHJk;M^d14{M3zOiYr@NS^iSFc}rgt%f;(=q?>uac$DHO;Y$wjZ08>#I>M}`%+Vob||x!I+>?sEnu|Mj0u_w*=+ff4y?7+6N=@g&a;9o<)aFEnB! z4qYpKVpgeyC*IeiVAx@($%z{){8}})%&qf^8HIFFYTa)Rn6tP? z!LnS<(^+4xsTpc)JFemBz86G};?W0UbjyNy#f(B;`oMGZbDrr@)Kp#9B0aE7-8F(( zcT_|59oMr}MRRmLOkX#%RH=!7`Yi8U)NS816wfhCoee-yebcjDCo){k&-bY_b3LYsd#qiSz*MOnvQ2iuIc!i?P!{!*k;LHFly!pYs*6b3aUuZeG5Sg*-XqFo3dSpbF<2pJcK_42v9z?p>_0)=f2*s&R?rtd`tQ-FTA+|XcR#Zed}Z@k^QDpz37wS0QeG*!j0BGX|e+KQtaF<<+- zP(N<&myCP+(SOesYF8YI2ajq#t>D(Il)1Hjq4HVXRU4F>ZW>t}uG*n(>xRafj15O) z*fiC(o!C-+HQ+Ed5X2X+`ooS7mwZhrq{oYC??u9#6bFEeMYTBu#MFTA#e zmN@x@=0itm4en(p9Cg)&!_)DxJi}E@UC|@Wh$7c@b z`<=w&cP&3m?CjUNcOg(CFZ4`RvsGjj*K=8J42RDo3{~5gBi1ibj%&G~Ye8dXTI^~T z3y0kybZyOz6vZ(k7KEuLTikw}?ACwF z6*KDg#o#lVPtU40U2d$Y_Vvb|VFjUM846pPr)nZ`o*xrxA#wWE^m1-mv_8S)78-Pl)y0j(D(dUN1h8TPmvp> z_siDxxnk`~4W+c+GHV^*)^eCwbwTst91fam2CN}AX2bR@U$G3;h*V$Gy(rd_SAN+t zmACNSzz-ddy@Nqfb>A@+-PJwQ3beqs#KM!?-=bh^dThm(WBQS2XtC;AoS=?r2D-zx zV<)>du6Q3M{pJZOKAx9kXmed9BqRn$YjKdNO8ar?r;RVYXarO}my4C}g~poS*4hE828# zh_Ip}RB^7Ej;g3p7{+m6Gr1HknXWWn$vX43*k!EbA!S&xDo5Ch?7%leKZ?R+%#0PR zGb@S_GIZaFUB&P?$e5-qV-9gg)wLk$`$FsQ8PJR>#cfw}qGi-5w+rR7mSvVtG(V1F zHB>?iU505FJCW+3?}UnDE1DlA9H>mrD6p7gYHVqm#<6Sps%8Z-ZyYI+5~yNzulfCw zNl&zVGgtJ&AhhC0M`-m7#Rz;(1ltJ=J&b*a-zTek&wVCW%&6BBecKkQ`HX5e?!CRV z#(IjuH#ZL{N(oYmcGi4E0eK(335v03_ zLrhV`##`IZwLF`T6}fsGxGrLbi=Z25zM};y$Gv9=^~4oyELQV_xy-7?E0edWSvB`P zq1hIPH8&qRQmbBRv9IcZBDVw%ZO&lNuvle=#f#aV9#n7Z8NzqMR=6@ZGi!)CF13uw zsdg+~yURjLF0~U+X&z9_cq>jTyY;gLJ+!R=smnyfGLXf6UymImt2k7UY~yr#4wX_r z`=-kpV&Lf=OAj2Ivp@1JUnnb^2PC8KYQ8;J;8^5f;EeRbh?7xa$}6E4S^@I56U5aA zT1L&D`18`{GYd6qwnH&%`Lfas-A^nN&HJ;Cq_}E2p=q(YRk>A!CbD0^Jawb8{D|c0 zC{mVE+?cVpk!&p`j9ph@*=eRc~yE(3#}+lI;FXPL9O{xkr;DV`A{n;7Lt<^q8^xz z;Txe7xRx%(uE>uy&rM!g#bHoXY;;%G@*|%W$KQR+)h%oQet?dn#bz@8wB~zrd5^V$ zP{KNmP~tRH3zvP&3Vq8~^e~xpusqW;YE;Cv-)$aW$f!|FLbVxbSe9aGPV4igI1Scx>wu5{lhT1dsd8-`(kH6sdq7uC}ZFlMMGbJ!{=YR{s7X*!2B z1__&8-$$w7JlDJ!n;=?FXr!lKdJbgN%dsYPwXAwmU9IPH&4s)jDv|Gb0UMQXu)Cp5 z+gjvlo;-tC6Mt?+0@i%p#voy#2TLi#!!!}O8giHxT5KFutE^xlR4Qx`UW~}W9w(2$ zP>Z!#=ed3;`YzfwD4F)avNgyXHLLbewtw2H7HU^rUTcGYEak@rrFy9$7cj0;^>qw8 zlGDOMtoIyt20O5ljc-c9Bg0f959|JA0WxEh;*RH(ReO2{ol4j4wW$46E2y>KO0bE3 zuAsP%1(&0Xr-vv%qj7#pQ&+P!(yhq4?sBxhpUtNChpcQt|Gq&8KD7DJSMpM;K1~D#Jb# z`k{^8%E#OmV%7;f< zOoKmT9l|t*r0lyEi#Kv&sTpZr;yu4`Jtm-O6|STRZlt25bp<#LX+j9NVrebhWY ztJYoTELTS|(U?2swH7IC<$>XwSW+?D_|jyoq9_K^>}v+niZHEfK45FXyzjB%SZcQK z>aIMPF$}~U1*vYc;K^`tX z_|NWh2j^83C{y2r*Rm_MOVT`Gez0eDj$G0w*rPltCA+>51>&GS$GqT`}`nCu}~_CrI#a6SGSd5~pT{gZ`vx89SRsns5pP6A`b zeC)Iyv-+z23@c{6k(l+f73Uwcc*)7R+-Kx}rVQx*na^Cfc*>#tb8g-4U$^_$?f%t1 zsZM%Z-R}SQ?uNSEzi#*6vrDFK_pjUi|K8nDxBJ)a{s`4|yMNv8Uv@Xt?f&g-R|Gf*-*Fp*X{nFjBc*m{UvH7!_Hc_`+tJBp>Fs8|DN4n z{77%=m0a9o-pO4L%?dbJJY~iKLOFNw;N;ET^A6uD_gET|badZ&#ZMWMG*DdFG=CrQ z*w1(EC%&|EMepY42G8Sj8=u>g=LX+#jh6de;*Pze%S*Z6isN5sIi;Y11SbAPRV@7D z(&Gg*3fzO^;aUS=lsrBimlHrgTxJs)4JE!3diY)gF6J84Vo}`(lFij~#+b zV%xx{1{x1vRp^7rz`6D&@ahx|ol&bHKHk!DdI9uIt!hhFQE|^JE&CM>6)e6@;E1c* zfJ7yBl*k8w0~}s~8MUKL6;R(=V5&Z#HUsA#zCa0 z5`JrWo^Hja8iY{-uma>uM!jO=m}ad|yV8=ceLS!&Fhih=EZ-oy0}KtQ99s=++qTR= zNv8jP8^hq)Hely~K%LMr9DwfupnVhU8-H*eF=ib|ffQw`^AvoBffJd;RfM*qGIoIe zOoB$hF9pftV_Rr{%}T8(MRM1!)f6Rb)96LZ4iu}bmctti$E@Fo`KmkG|alm=UV$S%A;;Nnxz}p6?69Ep0eAf2~Yuok6Dl|T+0 zssbl>dw5UHx|K*=`-he@3wq6pRSCx_!!rl{v1R|F&Ek$hktvL!p(}y-fT$TTDxn784QLR+XvHQzh9(1072xmNBV!^*4?%kRgpLCBArg&f1LA!E z*hLX5H5s-S5eJ2gYQ^;YS~Z_hsl;`Q{r}c-c+s=*uSX#j$|`MuwssT{3jod;_=?;9 z78W6_gaW#X$J>FSXC~t0Rr44+!rvn9Vw08$>0ceNKX-bliSKUV-57lDcjG>xDp zo!0^xt42hLfTAT@PJ%jE6mp#n0dC7gC3=)NEPy_2=L$1aw;a{C#D>0W+i}pY8!(c@ zD}a6ky`TVXG60Q*o?=-YB8f$Vge<0?DoR^5d|k; zZR_&D38~C;JRrk<0REbotC)?g6x?`=w;b>o7@JUyLq8_)O2!Z9zUjC&flNdbgw;U@ z1qoYq2)uImBN!R*b%6OIVBN$7M}Cx|ztdp4jQUIGo0?TMQo18n-Pm&YC@`c|C!4E+ zog%D@5Dq|^9y?oX2kh%$s%(oWqcIz{fopLU;6lK<(UE|X20Fkhy(D)SY6v7enRQGH zz%e^xd^#?ljM-^-ES=F`x?F63I&AHLV#W)(X~Y3rCmkKCRzTbwkamFUnnJ9V#gS!( zrEReiL>HO5;|IPIAye|wgg|jhM6p6=u~DUB<51QZVN&1)5qmf>Vqm=N{)jnfw3>qeiWnmU2X!xI&Le}Fgg&u=1w#$) z-C04zYVE4QXqHgWwX5bJkPEHP3G<5u-_g|==%_9Ml{ULMQW8Ki0)$;D0jFCan}$Sb z@*)mrH3&zr+NQ^b83AKQkOZh@R>e-_HI0)i?*_f5@z_?D zQVFS9lwndJZ3#O;^9g<6j|wM(YQ%u_2toiCcyQ}GxBrTb1%Bp}O==n9k{G1@Oc}Yt^fWg%h`{g*c<)k{~%R zoPS7OiP9tyP6#*@EHlBki27(S%m`mewn)Tjfl-LauYe%NySX7?Q(}^kO>`oxbknPX zMHiUi$jn4?aY`bzX$&IK@hF7g*L}sV#2!458(K?sbzFoRp3GG7m`Zs2=Vbl3(bK`M)cSNZnJJKshdmc=8}$! zuWl~sV5g~@OX}v5PcmfH%_Vhn$sUa~b#qDGT+*I%sHWwjZZ7#GOHJKeQa6`KBT(I3 zQa6{>%_W^Ic^x&j|9NwX__5y9GqJ9j-`Fj8Pl{ieG~uycUBsl<7xob||9w~QAu6>uEkgr|4nBH%bDpozUut%PhT;$^lEn>c9iWyRQk+&?0?8ve} zvy>C-k(GjkJtQ|Ii5B7W_(bRe1%Ls?NV!>Mpv?PtK|@m6`6220EX;g=7BH84>2#C%%Ntxtn zsSlutaXj*s$i+2>uagT@GLwvLq_>q0e7yTIxhVMs$ftq>&IP7`TZ;U(q#-4HZGhv% z(h^*!z+*CM6n$|fWmYJ&DV^V))Cg*Wt%;p4EF4&HEk)DGUPTT#m8=5ft-?jGko8W% zt?Bw@Z$`%CmF``-`|4$YVrq^{)G5$CV_tT0ACermR!@02Gs=0CGTtgZp5JdFUE&+t zw)GMR-nQ_|^+i`-boE8YVer4c=)RC#xn<$F?zumwXY{-Ox={URFs6Or8s>bL7*o-F z*5ckG*tz1i=8@YL@>!IO+;-P7mlwgVNM}BP(l{pXnFhKG6eY6v{* z@ts}0lGnn;W84BRXgt*<5+chdsh>Qbe1q8xeL==<~Xctmn!8l|7hjnbv=rt zQ^m+?f)z#LAd4?fIaO6iGO8F3jzv3tVrHq*6PG=>_z{@_pWK}md5l3baJrK}ja$%} zWFR6zuv1P6pINH(#KWKBi3MPrxC}|0BheJ#cO01{kMwNhXEphccxCyLc_eGCStw4k z)<3y%@rR^@58WVCNu>*{1{@_Y3=mWSJo^! zQuKLnNslg$O#J(eC39s5?CB4mO_oMG`WBN2pwBw)0T)LR3dPRWS1m= ztcHvpfDC0{SIQejW~tH>|G0k1ufK@804NOoh0p;L7##@-EqEGCO#%hUSyof+V0;;hD{ zBa#iX#4TNfxqxpkX_A*h=d$-adAZ?Q3Vizxc{#d?ZznDgtBw`F_&whq@{%wHh~mbj zUBxZG5qUBC);Zn9xuvTg+}j|2{2Kw0^2VhD#T9Rh{l(Dt?mI{n-)#TRd6Vy2Ucc)g z@wc}Hly9=!?ee=9-=^HS-%{?e@2(jrUYFlJG=y>o?`Z$->EH6*-}ZfAA2IF7yAMxp z-61CC{_nP9=ZSY6mh}9cnA=4h_`VnOpBNyv zyeGPe4V&)XUmW`fv5!#SE0^1wv1f1F@_u?hM9h>0?=Am%h{*5c_Up4Z_ZRQHC;I-c z*R<$@JNk(;c8VWD(kr?SynO0)6EB{8WjuAp72eg8r(Vg;RaadTiPIifbI||NyDrLY z9xj?b5YGR#7R;DS3l9IISnBf)O}$Y^M(1xgyDi{3BcWi zcKM{~Cisfr4hWbKKub|x8;tn`tH3TqGU3qHNPr!Kc6I`v1o~{FAsUY%p|kDrU>Q}% z;@!V4Kb=#vcGV>a=`H`ceBe;5cI??P$;-*o3@H)7Nusx)reXhf#N)@$@Bh?&!~^7s z1+XO53xp^LKa#e?0z~Tpe2odnssQMra6!`Kff$fLoB~G#!$(2PX>?FTfKvsVw+7o+ z&D!-6%8<;AHLEVWg-4&btgl$mwRK>D?gMa5>ou6E$aD@^83QG$WifVPSxj?v7A(?O zDi(A)7$PCaib-z?FNn-1jyX(?s%&`$KX&*%8K8Y>%L}DtjU?oTBqCD4;>!RxB5!Kf znx<>cthF6#c!1dQxn<-Ox0GraJvBp1Lbwc~H`q=xjGEX|eQaUK_+C0oltu|%uB|5} zFo1wI%owPWJ#{xHjby;8F-JQkl+5U}Py&0;ny?HgeeIV@llOzdjY)Km)e%E0tAda& zmlWY7jEB()8in?#MNHhVFObe!0m@H0-csZjSwYDq4|*HX`T2$Y%D7%Im{O)-!AWZ~ zu^nWSmwZq<;NH+9oHNDsW>qXMAKFUp{)~Fbi>`>?`PTjOvLp-007-8f*i=0kQiIwH zl?U=y8nRM+=TU|*CM!CPBtnH`zkumY%;Pd#O2!z1KIc{1Bg3HIfhLH`>$<4)ri|UQBu^0e_%SqNLq-gjtZkU8L-MxjT)@1eDFMln zP0h9|$k{vm!NnUfJe9-J8SaPuV69IvF z1_XI2KW=UFf&^W+1L!lxrxU6-YhreEQ)Tp7+zz16*M2FnFkS{>rKEm>{z68Ev89ma znT3Fv1ITua86H{9^iuqMfoKTu*#NbPydYOCRB>!erBFWG<9k7V|ME&Jz%GKh9;X3t zXhvVM%Gj!}eAcWaSfZXR@SKTX{z7hz8wkaI4_*Nz=XvCK|1SJ@7 zWDt4TPWDvG)F!+hYukgIGsdP9nlNK7cCsyJbeJy}*Uz`N4k%>2)b2#$c|Xz}a)x6q zWYc6KVFO~bBd~~IdEHNP%OGJWvV9Z0W8&;0oCtd;>=;l=$bqD-HHZB?9ETmxQJ=*sN86S(k6XT^MCeSd~z~)4ramaNbgR15r3#3aJ;sR89 z_6OE!DD(Miuz?aFVJPY9DrQt`rEGt-Yc)#CYw6{%0mXrXbO2Kak%&Z$Vt+MdG>SsB zHg+YuygdX&-_fgvjfjieoZv5FD1E%%FjWy&Bv|<6LpTrF6}H=e&@#>l2f7&y&k@-N z3}SbDD*o!)gOYJeT3Lp*YlcCG(9wKG#apj#J?ALLXt15hS63(tB|B$GPpdI8xUlv} zvK&})*Z|qg=}68kIIFPCAmZW~CSQU)j**WeA1H(cYoAS!&2Mp)ruhRh2By=}%9xFv z!o+HKm$Ez5GUKJtjUaa-8$5yz_P!#(XJO;ThNA zTt$Q?Zv;D~4lE&#Rf}L<4kPcpW#AEHD39m4QlCrNCyRMN)` z4-1)(R^k#T%Q?(?mqb^(ZdA{E_8)(@CJy=%b(bpb^O z%48`DA2FPaD1@(Z@Ii0uR_kII&k2H!u>upm2|pEL2pEhomVx}lZKsi=qo+xR##$Ad zK?#wNS#wDroA$fqBg8vTF6-AFre3!J6}ALTfFTj`Se@O06eBQ`I0)g)8(1AUcoids zCJe3Vy%D3=K`554wyfdlDEDQ|&`wzdGI~vEml6vZ?^J~wMAne(!<<@7Jp7dyG}s^6 zP-M0nOctep^>Xk%L6xi}6g5faN+2=3l`1W;)So0p9%Z~)b+R)T*tanbje^+3K;*{4Zhph_u^Bn0qsPzP`%*e|s5 z##ulU0WbrMN8v~&loWaz78;@y;rHbFsAy#55Fl_nApZ>!wX7yM$8hDUu+E{GqLPtP zCM7P+s1{a0SdsD>wU*Seug!1mD~@=)wNSv)fvW_E5}hsc*La+^I@%1x&~PUz>7>lV z%*A;}?4*W429GEfb@n+#Db#jo07p;Xc2Jp*SMoBtNEm1XSf+?GhZBfwL43g}iRM-k zeY0v!(Lc2-W$hv}+G0}ivAxCT*R>wn4ZWj5SJJh{4n^=ha*|~KA>tiA$k=AQ2-)U; zPMLqDw)azPor_AAE;c4q%@OLj*fOSOr?mVTT~@_gSo@_g=-92F%PSf(J30qaGE*{k zO*AnE4|;dhmAHDHAqGmv7Ropdb9f99We6CCG!1!;oEMV2tE`jdCCwB06>w%Ut{fDQ zm62};(SWQ1F_{S@JS{!nyxR3grSq+rQFSSE(@~|fEl<-B6(fag2k>rVYaq3%biYBi zOTL(dK_V1bLcU12q2L|BF)H(~1n62E8yss$RPB11q&fFdjX34e&Pp2$TU|&#E*!Qv zMkQTun%|;!tvJR@c?2@5jl`^LA#&N5U(Pbjg3Q7Lr{`CIYh{gOZtjMlV|^`E#&3#t;ONEU<1W62@e$MSxDEB znmtDQ^D%qWtQaY?BsykPTnfrw(6+Gq(!VY{q8kC_4W#pu2U<~@Yf1HmLJYqY+eeA< z8Vm)=d88RiOLD{EBp*`C)h{;moZC)$jcieCbUK?oYR<^cz&w)l)atW}*`tv0PBP-Y zOnHqNk=Y3I8P+v&{P<|mk+f2fIt-g*iKM(=%HD;XD9gyKTrj=j;v{~al_^;RK?qcv zK47#(*z$pg$fuGT4XK4pL%>o1(1u}qa-7;V`{LM<%f}YtjA~1!k9+rDv2OvAS6hXu zO6T>Wz{WD0u6I%t(slY_ky*@4n;};WH45y~!rV`@z^M zDb3}`kTQtQ2`i>W_;($7rqrl9U1S+Gqo3L0@*&HIk0e)-U8SQz2TJobkK|r(JUKF( zja+j;US+O;$9_dR2XX<`1fC?bZjp&_G6toyzEyiBb~1eEwY#gbyoxnnhxBA6?X19T z$HD`r3^+9a>9@Xj?=A_U#n~{DB{onmIgEn zM;a3$;B85UguF;fNX!N$84+!)X3b&ASfD`2Si9O%_SDliujoOpMx;J^o|6?lnWS?F z5|U)w3~2->=5DE78lgC-TyS{OMh!=Os8zqA_r75|w~m5Y!hh6o=%M zic=cRh0xnb{gNh-*pOxQ3^c|c4+M>!d)%yT4+Aw!Q9;Kz52&M59bo~WEUdYt-DH!)KEqKM#0njb;|FabnZ8oe zN7hEowsb2!xCyJ;v zqosPlR1C2{nk4E9&~8%0eeF8KbZzCmniUFfbgL?)RG5dX8O$mCofraS{y!4!;Z+X7 z`&rmsQWI%8W-CP40vU+Modt+phw}-dd922)mcT13U!=vt;04PqS|kxpDsD3{>r4X< z0^FvlNVVm!k3z;XqjuG)g(IWpvRACMrtmmxRcnd^6Y6u$RY_V;(kt|Gq$Bo$C~y>$4019tXW5!o z8=ER$ubnf#_6T*NvCf#3o#e^dJ;!k;W1aqd*xLSZu-1Mxgp>3HBaImVDJ(r;a!|u? z5E86NkR2gv?2L}s)pt`loo8P9kdXkv6*1KS!1!3d(U;lVEekK&8*jJDRfb^;#$MzW zER^^>A#6u&32cpAl-P}vzAv=C3+SL$on}-zz%uF+<9aMVnk0!pgFqEy6a+3$o1_j+ z!e0($toK^7;ez>Wi%czt{KNQ;S%NYtH4cTqW?d4gM{T7Xi5u3eAa5BiBvOm9LJ3I3 z!ic_!;hX?PipQGRO|G6*vCOQk7O_{~I5=j?-KWbovZG$r?2ykuD z-O1xkcqhql?nqDoAR8Bim5cEnEi*)k;RWnCdF9lW4Kf~(%yU>p^bngbd3M2XGD^rC zc%>B2PfhwIqWePaN=b@G=pg5JtxDt6fwJWbD~zLz2HmP!Ss)CsnlN2_$l<{aal)c` zv7@2MvWc|2(KC?FX?s(-5QA^6q3Hyh%9w>}M=3I++thZsu$c8)sp1844)k4&`j5J< z<4QqOW~oFnEEOD1StdlXQe`ea98N$W7+X@(p-$sQ#Kag1M!lW38q7G*KaE4r!h=s9 zP~1)Axh5Ys3V@*}k8fIr$2p_c^1+o+>tlDZrVwUXC#Wb6rWP#Qc&}8B98eT07=m_R zvxnwFZ~#-$#HFhe+``I7ldw7K5F4u!brzxGCP^YJB{JDN`A?a>N-VR7OGi7$RM|40 zQ7=ap`9up@^`{xt9(KxXU%RVfNeC}aJf(R+J|gx0C%=v8d+LfK3*?L@r7A`#k{C-j z75K$SS71pnIlgU>XS+{o3}i9#zQj>s=??I?VyK8w{ZNzPM;GeH9O@|_Jlhj-V+;av zJZ629T^@l^XHY;R`dT$1L`DO`)F7WxcL_9`8srNm8a+xpa3Ib}_|I9@2u&82H<>G+ z9HVba)^Vg>1yzik>Cz8_6Bb7Sa9Eq9^}xQ$RO)>ut(Ep{a4H;WfQsdGz$+W0vRGss z$HWoYsmrLCQ7f@w=daapRMn`{PlZC@P)MR2k;Kq}FCl(TvEWXS)=F?`iTBiUnosB~ zdl}zp;4U2kh(IF)WhOmyfP2iWaxoXl2TtG$*cxoWpb7{al-v^Rmu>*&BP^$=FBrA! zO*5osRa@~df5Frbzx?WB6y<0|89DJ9X*wqjw$q?h13ZZ63qeeJyeqH(8?+5^ejH;_ zgzRdAomwT0qO}8J#4AbbfTYhW>|IWfXmlk?I8S$e5#@EF4+A*uLmchHAc)z^;9m zczxIMv32-;9e!Vj-+w}gNGDTC9e$6ovI6_B!|&_x`%esy=yYz?;rDw&{nz35b@+WH z(?uPAUx(j!LjBj__r>8ENcB4Wz7D^y!|zkve;s~b9kft~-`C;yb@+XCfJ8@Ye;t1R ziSYkA{Jsvqugb?)hu_!X_qCw^b@+W9eqV>*H?Pgr;rAVZ|8@9%C1{}zzpsgbPeVlN z@cTOazEea<9e)4!c>g;5z7D_tqyULJ{JsvqZ^!-D;rF%Ek=Nn(cQxNm!nZp7Uc&zC z@cXMS9DdVHu$|Qgtk>c9b@+W9eqRgqUx(lSx8e78@~fY%_|m?)w?*G`dw)q>_S%Z4 z)M-|5m3N7G@ljgnlTV8*IK(Ltv?TLQk*%7fl4J|;U`!-2gyeP7gk+}_uv6|Mlye&Q z7MI*TW`8mD+;zTqa7KzD-5W_A;#0#X6Wx}o3{&bI@%Pu_W z!+WM=7pr31+V3y-e7EbNS&djc<$`%aId}2kWUr6zd2g@WZ@#^2$l=SFOy8r>|OB6bC)sbg1Zd^~wR_yE9fEA};t|Q&IeC#>#=B|J9Tm z`n{%pV(G2V4iFE2ugUtyf1A5Wj9WrDb2fXSjp$!WY-O=P9h^036BibB2dv_YU1jYX)!dS z)X0Ni5%*J`rhmO_6})g6g~j;SAKEOAU%F~-X333<2MiK3du-|^^dcQ?2~*nc=gS>J{_OcLl>{LJvm79KJu<^rAH=@3|YN0yVTA3)ql>_th7}e zKWz20pID@srY8-|kUw(Ap zH*W1By3ScMqWRi^-$GpAms?xEt+cO42&y-wRhxzcx?%ISM3SNg6~IYXA)uj-A(t{dAL_Ab4= znES=Hv7P$Hscv_<+4dg3>r}4P#7^Z(-*qZiYGQ|S^iAo@P6bQt?oe>*b*1k*l{4l0 zbt+f-u2VT{59LbV&Foar-b2CCcb&>Pdni}>u2VU859LbVmCMa+pPFiEZgzT=^vh0< zR7(@5bIH<|olEX%T+|K&Gqb!h)W07ZhJQ~(qwZ;J(l0w#zo*er)8Q%J$ZklYIx+5) zHmy7#7oLA>Ke6JJw&Tk8ubt9%a{2PeFSngqzP$0vZDY%qFMhdgLizIWQ`;^mU(PwT z?VR%E&9mCRSiT(fm9{g=m&Ms_*QJ+B$F_a3$L^f~%8IrD;<3}(_7<0oZ5vuH-ZHlB z*z)DeyD$5mR{8e*#>%C7YumB=Rvt*+#Z;WrR$vV9)U;URp0e|V! z)OK{a&eNMJ@9@#-m5N4>Ya3lIFlo-+gC%TfY30SI4&33`U$~=!sh{pyB6CtPJ1H6D!Me%f$AM6Z>CS z>F@U!Rhseeh1JW?N{6q{5*|3{qP9cIJu&*C%1?`ns~c#gJy$CK_C=MRdt`Cj3A;PU z5@Q~sl@!vdzby4y>6bscxVqP@wAV_1=`pFTP;NuRq)MCTPO2<|sY~0wu=}BQX%UDG z9?d_-tIV{Eyvm#!BPuiPeZH;!X{nK=pPubkk1q^Ga(qjFx!mOi??(r3qpl|G&oRu8h14)V;1mwG3x4Dutb?Ss5@MP-bSUr{|e zPP#fuzw91YR>zpQ(x^M)wl9?XciP=;qjz_Qo6fu~cejlw|9#RW)r}&1znppeeQ)u@ zODc`pbxC`p#OwD|T9sc}-6}V2Rq3ZYF0HhxIJt7tTrs)2Dpf2T%gUR2Z`Pm|rU0rF+q{}-t=8Y!y=~Y+o-ao#)tyq5GuFE?-uzUc#|Mg0f3inl-^y4ckO)6el zX_9wkrzez}H0c|aCf)muN|VM-Z9A!|`K7I&4w_Dzo}OBn^{-Fu*h>q~hB_5KczAqBDA zY~P>J)=eC9Rb~3;9;meDk*g}LS@}SvHLpL==`p1a+Bt)l{^r${*35da(wdhathDB) zYbvdo^vw>BDR2F6TtjO{UfXt9^|Z>}DLYL5<@#$YGyL9bD~-GJp-SVnJk&O(`lqwb zZ~y7t*Hzl~i|Z78G#v}NX_ zm9}hnv~5gzK&IT#c5^4~qDl{`Xk&HFE7eLn%g%ey8}=mP?@7Y%AmEqf`@a|O>zY@) z>QmRe>Y5j}p;R=eYhEl1QP;f6n}eupUUELvHLu+gR$cR|YhI}Vy{>u5y`iporOqW) z{J*1lospdTSlbm{dXO5{1(g8Fq{w~Ywu8jmPqeAMcYk^KC08FKR==>mzxe!i1^93+aB4sQdG=)s?FJboBZ&pw$qOm4b$<(jo#HZ zy3pM?+EkA=m1Ni8HFI-f{{z+x8>+Zh_2{9IhIf?Wj?yeL52s~{N2rP|Zt1sXjF^AG zn#2C#pCC#h8`_bF)i%-RBHS zipQv7PPeUrNA@|JB=1g6c1$V z@Zm--v_*Vrs%nrx7z1A_e5K^XmTWOLya7-F*sz1c!$Lw?vNuE5_ChOpp=(#_lC*gI z+S7_b?HWrI*++@f&RE-{AbCDW^RLmHBz`7Yx}$LeZ9G|Nwa`xUp%zRxi0NgLzC&aV zSp{wKq2P*jP4^)$PjjyC{1>7E^RDJW>!Zd#L?C1;cRgt5VU2Q4A3n-ZtXc$(Y$<7Z zF{5fFrAe(_Gkid+R~!Qy7QIJ4U`dZ2Ny_NFWbuB-9^@%<8I`u@&h$RAUZY zv}7}f=-DQNd>py3SfvzPJD0?*arw1J9tF#H2c9d9RO=Cmkx92rB5Q~dm=-W~Njhax8 zKbasJcBlv*2-T-AQ{J&6X`>YLabw%pjvyVkDN60f|tOwHG>G-bLvI$V1QyxdlUVpcI4_%MPn zPe=>SArd*nki?GXsS2cWaD6km)Lqat4h>*JvPjEMk3&~PCU2M=Agj^HF%A&3l+?e> zXHa`+I%Ss4n2k=y)Gf6;&8?(fFJ`@znDw(2=Zn|3ZR;&|p1k(U^#iPafYlE$B0x?V zcizQgPe1XbuUuR|z&JAN2Uz_8>nvORzjT28NN?(uX!F;;F(CJ+6d!%y#p`-?5vvBx z>ncW%_@Gbn^h0Y$=8{Rly1|{kU77o|@fM#S@j*ZFrJXB!H*bDqEuZ`OJizBcKHp3> zKl0GYqlzA6aBy}&Hwb623Ein=IEG~qy#zWP7}j?_y!jisWXrD~A|N8Gupl7fInn=( zht_5n9k!tVz!KIx`P&;0UElT4tYnK-+$|&E}s@=p7h!E z&|Oa#Wturo)fk;nHQ-|eY>!ZD!JB7!YNVHy^UP8u<-GBchi}}k2)i;&MeuIH2gP4j;jbuysjc+;Jph&{R zu6Zf3Qz5W1G?+GWAoP>)n0T<|!Ox0F%*Qj6g`-g3^R^uZ@Hk1rYq$-is1IuzOg`v~ zsBXghWra~UqgJ3my&fBzuU)I?b3!xd_7@)Aw}3FMdI5?+1PL{iIuKA1Eu{NUO=BIg zlhuomV~Z*gpD^S24m3@$^r0Eq@Z_QTLXroIEd*YYhn5~eL&EMrVc6bO)OOF&AU}mo zMNc#Z9!QNfv<|q0QzFo_g2(* z%W+{ZmE(c=fk^=8DnIjJhldtfucDtRz>}lG4@V!stENIQ4{JQwTMuq=`p0jl&&jA( zyfS&4npJBl{H@Pj&Jn$!$H8E8 z{Cp_+8e7ofYcHN^Ek0wUIltF>yiK z7HTHe7+sOe=oVI6UcqRCQOR*t6=G;Y(^Z6E^C-^+c=i5o{qNiRQocl{!*Wa#xYUro0YwQOD zlIv$b)o0*df9Wb#KiloVDJf-x>Xy zc==~fjY#4rzWb%~#lQV58Ik;~*Sfv*qJt(uo=r^;O&x}LoY07Hk~y9WNy%HbHng6w z4r@bJ;ndpj`ibk-W*4nk8^pOMt?QZm^rUsGx*nQU!Qv^0xnlf#&kRl;|MI%4d**(e zrog!3oONqHtx4vBbJty)`*HGm|6W~YdpDu`fXKwG^>_7i>;y?e)$@yXw zz@p5!k&^(;6`%kK3Q*Ej74_9c+bECal{=ncEVB#0^$e*rvWiuu(pd4^XBKtM{6RAI zy=O*u$^A=Z5nywBbvCp4^*=uI*S$YQUyr|a!yxgi*&p;Tbw_g2e?D{W#o`CItsl|6 z`TS>-&F8Nl(_6I8*f1g)IeGm(!^HT<*N;ec-nPESf#Sh~`F7aK^~!{zT&U<@a@fiT0ee>*mnz;f9|{C$$dq1DFt3RSYF<9 z{<9;JhYs6tt0VTFOo4MQ+R(d)XgZ7ncU&qjU+&B0#u*z9*+(4Ew)Uvxz#BL8`V_;T z@V%HioLc7Gq|fbuF~L`OVw?IGl~fB zTT2cS7r(z@k!XCiZIsCG+;Dt9{qrdkZuPUon4U|I5JW|`^pmy8f6?AkCVcaGTk z-iG7${G`=SE;+DPfu!++4G#<#w+98fEXrtlJfQ`KRbGG-yKf>%NJe3C@rUC}! zBFq4WX%m~EWOK*+?nKy1!Z;<)-Z{JV-!MhjteHF-A04=9LT1%)3@hS6ds83MAsHUl zN7XiM2%|OK@c?YWcMb8g9>lg}lb(h(4;vVe8cUZp(%5!+C0&KbIKar@2dR~$kH6yn z+|bYuBNyY3j`b4sm+Qbet&n6$voQULzW=)Ipk&KM%Yh!%uD8S{T?}g1TO##VMC;H^ z!wVpNoX`&9fK3?)7Fn5~7sraHX!NYAl%Y{rPB2Jf^_K`R-k9$7E>2-uXWOSC-ZWhVcekW-wKV0m#ihQwQ)0gXOt-jXkYpn|R@ISrQ zzL1iDAZ0g>S zdpW0ro3)+Mu8s(JnUb30iPf8 z`4OKV^LdfaOMG7DvxUzqe15{`r+j|K=jVKW!RKH3{2QNt=krTGuk!g7pI`HNjnC`J zqUYY|9~Bh@2^DBBzD|&kmdnZP#U^e~->|HRSfL>ReIt!b8Bl}+%jR?h~&NrTff#t{P83%zj*G}AM7W7 zJ&sE&l;4U|xcv7^Rob=Ga-tjl=#bq8!aqPr-Z%jNS2ZF~2CqA7(mge_G%r~xb>tl;Kx@bRdM z86vin>d7oss^_jz+q^;I=&`)d!C&0Q`|KZ5&$Fk<%Wqx6r8ibyjyr$?UB}DIdk^Ar z?SyS@U1WMb)7HRWx?qjt*z|Q|LFCi`vhhG^_x%>YT|1$I0}A0KlpO>p7z`~)TBOCe z-7P5HTA>!<-o{a^n)s9n+dyR2G#lq@kYea%>b_c43a|I}(~5CMjbhiC+Z_Z6z~+`8 z8lDuy<}L$d zizZLP_ZNWi1#s*7h=zt1xx8i&n^B}Ct9#FVCReCkttR^XvPCWEwX2lySXC5XdGrGD z(3EXG3gE0Af)6a|k~EOih+$w|#ZlL_Gpk*Ab*oyiYuA_}eHVPRZ5Y+7 z4Q20F3uEoanl1`;kTrM3f?2iiVAzW}=>208Ih{Z35IKO3Ek9-^l41wl{WX?5WPA?_K&d18Y z7aw@ZtdCo#Gare=(LL09HUMxAp6RQO3Sc6#BmDeT7=j|(oT3t76Yx?%msnZ~+a6&D z!0Q8(4Yagn6l>S2r2hB3QnOy+ZD?@>>ui59cG&j8BQ>oG=3=pB)6N*=zYMX!x)X&C zn};2s3j@!Q(JD`EWcwIe^Q;vcUwU+aj8$Q&)gCLQ<5J6-pdCwR^;&7E?Rb1e|6;~_ zrLign9gK!=0(wCBBS3=H#%O9<5NW_8d@YUDNl`u+dfD?qVcCp70;CZtM6Eb%icD!U z88c(WDyFF>=cVH@&kcu=4iFUp8rW|EXvJO{6_Q$SGHQ;*XV+{yqd;g`ty;z7cP&@N zkJfDKRUq=qMED3C0?`cLWt${qgFV~BZa`E`vbvYtG`uJT(HOI7@B)Y^gi?6{VG>rL zhXK^oyEd*Elq`61+x@wG#9o5619S_km~ftepO{Xlsvsk=-{i)Df9jKej1^bA=JZs{ zs+bp1#DYO9h|iPObdFFp0w@;gj^?wid0_O+&;v*`>}?EcL&4;5!@$YG&{YTUlShn| z5ty7;X+X-f(^?mWJXM^Dt1*}_1GT? zPxQ+`#tfOULJx9#Zf5Nfb33ji#M4?+wG%}TaXmFUQt?ac?ibrzS$DPH3U3lIVg;Gg z82}fvmGMLG@&gqCHqtSgYN{0`(^s}&YgBBMH1L`d{^*AUf`y1)h90xx9NSI2^Og}K z7IOCa^q8q&$Wv6)(N#r{L)~X%Gc7}Gd=mjEU2_?=>g7|ecC}(-Dah)A=Ka#6j&l?+ z7N?Hwg1AJQ3>}}v&9NZygG7t1k}-^tjcdp=Lhx2VI1SYU!>ajM@-&z8K|H<{i8Bos z17QcZg~}@CT_X@?t`lJ-)*TD!O%ElwY<&(Xu~wzTV74t(ht{t1jfvZ*u_1VVKr9!s zIhI9IY)4@n6Jbffo&z8@Q9q{j9Otp_6P=1)1%%EI*+N_^j0xvO<_G}uPTw{tDaDb3 zjpGP+=UN|edc3_a_T2_u ztvcFJ1$0t{hn%x)qEa&A95&cAVh?FDG?UzL87;;P{A87?I$A7aWI8$8Gv=kuA)`c>AH$maZ(e|KrrYhP;KfilG^2Qb0uj-zABz1MqzGnMjpVrl# zynpld_fE`d;&nq&zfz7q8e<7% zyq|AB1`??8eb<~O`fS}kMJ7J$ZkcX_Yn)mvjvCc4>QrDzlBj9&l|`jN{riMy)T9@W zup2B}?E2yMkJ~HH{bc(W#mC#WPdq|zFdH+-4H5!Lo2p0+_Pfg zS9csDzIoq{gG6JrW3+gF3b#L4vm-CM#@v4KYHmM!+g=02X*ceG!c>0uhv_>G6+bv> zpPu3m@s2Nw$ulT2@sb@AMRX5WTXyc~FLqu+x#OC+9XWN!AaU8H+>XDC+m|lgaizGl zg{zAv?+8WDo4LAW*jq!yUQ;NtK(^(c=?Cv4R`t5;Kyk|y9>4PzzAHT?cNs;7&!zII z=l1R`Uf)TBzAC@lcO^w$y5p?@;sbem&0=l`ZF;M}m~uI{d&vhJ_5HUF5UK!khgPZ%G*n4 z^b&{6-~|g;aC^rL9{;TD?9w0lUBw?3-^i=peASLK#jRi4ambQU2liZgHDBL;8>PlR zdcgkTdU<>Qb(DH}!fywPzg*43pNqMD^wJqU#pG*toGvCVr|_%S>78L>!j0TsDm!k{oE`ndkL2wiTex3|w`5cj!>tB=Al-NcyWK?dt^8tWD}N|;#7FYhm2dgT(8)c;6-_%X5Cdf8 zA1(j=0pf2>{B^!uMHHSOG}$??F8zI8{Amt~RBlQ$x7YDTOXssZx5>Mw<#a8* z--Nj|<-P(uCg*5?`Lj?RVrTPWu%P{*k=hj|nLnZrgF8*k3M_QbRZ0 z#vl6J$n9Udy*pSOe>=AaUde5#0Z+CzbtN9z#k4=!0mtw-_^x{h1{-?3wwUN7{p#qi`hI4f z=OFp|d;hte*_oYfv$M1NJkK2d&57B6{s#26tfup>e-}>m7Gq3rGyBrTmTzeeYelQT zl}&W!MN7C+Uie!sxf@WmG@u(XIP_(jWimCK2x!IqO#xJ|hNmz!AyDTnMBcY#SwxxK z^zKV7-%#zPfT}I?l%jIWz;c}JZJXtuc#I6tcJDFztz|Y9Wz|i|^t7hx-+}Oc8qj&) zS?19P-vOGxx27+OS-zsjY$CMfmQSe#598bwme1)cp26BHElZOR6nx{2#HA;Sypgz^ zp>+%`VQ4!;KQeTXp&W)zGjyDx%M86>$ih$>Lk}4Gj-fYB0{Vubq6}SPs0>3F8LGz6 zJ%$>cOa{1}!M2=vnW1hB-DGGWLnj&ffT2u=K4IuSLtij-o1uja-C}4tL%SLJiJ_+q z?PAEv(60=gVJMp+2cYD{zZksBnGS}wGV~8aiy10-3eX$y#1caodd_(j7}~*5Erz~j z=q-lUGZe$nL5ALCXe~oS7~00r7>2GeG?}5t$qdeB@F7FrFqF>FYKCqww3(qF7}~>- zi=kr-U1jJ3Lwgyz$JPa*k=qW?Qkl$%Q7Z@tRkei`Or;8-Nk(k9`Jk{GfuG>xHk41LMaCWeTiXAJ$o(9aBQW9TqL2N>GLQ1U4T zA24`{q4NygW#|uv?lYt@^pc^a3gXu zhVC#liJ_wm&0^>gLyH*lFtm!HJq&GP=wF6*_h#@kgTFEKD?{fQ%4H~rp<@iW8QQ?m z6NY|c$oDLu>kNf4w34BU3~iy1?=4~QLJu9WHlfz-W)`Ah-&;1)reJpzMgL&QFda}g zf3Qq8!FjM&pahqt2Ro7}Q_0C$(<*7}fQ#E|ZwcVDapDhlbw{)X8Witl5 z(FMyS6XT)kz#o=}I!LQ}I|J0&ix#UvI)2%b>m?0Cy9UfIQh~IKmJn6&nnf8*Xa2GT z_=qTqd<>@a`nf@A4RUKx(0F%qEE6h3cuTU6u8}#d>!mt9hPrQ^wtI2c>N%8#s!`7{lgZenz}6$ zO$>(6>JJ=&>Ws&-*hC}ldB(GcIGFZ7^%!H(xj_0W|NfHALP0+p^n`lku2Uj71W_$|~oH<$NBw3Ucq{ z!KEG=@>lUC^y?NTg;-yjXvx@TxoDQhPkI$zqFq(i;SSU=LCD`lh!B*En{Lllp0mGo;Q7?{GzOw zD9I^N)p`{<_o`Z77&&)RtaVh~8rCi*YMtyDpZ_RbnEY#@!r&A~l=`%mwGPUpI6~Bp z+SYgjtX$XnsR3@RXB}yPk@cMg6&;Cj&# zb}^}$b-6(*--5M43V?Lq;M(;L4(Gv_!V0VKHr5pe{a9P;bpr(biUF>PvEDMk#T~4V z4RA##>yHN5JJwqRAvZv^?P|3cq_?_TVFI}FT|KNhIz-4Ut_t?__Jbn)9EH@CUe=pN zq0@b=s||2kKkEep95%pPKs6j>)eKn$hv8Ns`K^+L>FKStA!>NM6-!+@>ziP886cRj zKqR-w2Ub>NFMuGk&zSl-fVa`jtT+F#6TNfc}M#F6-KOYd3?T z`Gi-~c=$u>XGU(q=x;{Q;jHH9X>Vp#T>b*hnDb*gom0q&S)ooRrb zlC5SPOsiq7t3p1v!e)M)gA=zbp`@QJzVz*WS5lsvn*B4Ne9w=%49%`F(;9AKJXH0X zWgV=8^y_R4!#%ZN4i=6O>1(s~9CC(#X?;^tdnF!z|WP{P&} zKrjJ*lzP2%89Eyt9;`|(L`j{zFa@UN;C5`>!4tx3s@7k#?T~^=EA8bd`W>yaO8JK8 zQ8M>5hhvcOj@FURd=&0W z-}KR$5|+YvM}La;=d{;qtF7Pj0u#Yt>Ohf+Ww4L$KvS>W9`3J#)?42;3iAG@#%*}j zjtXfIw0bwTnBa~YS~Rar80|TYedNeSYa63X`9ExoaIYcx9Po zd{zIg);PKQ&9PuU<_BrD(ub7~{%2ykn^_mKzK~ zQbk-$*;>1H^E#F8jh_SI$ZJB{jVQy@sk_&D%^;79N2@@tO&Q(GR;1RE7{P`6t(^^t zUwx0#**T@(R4p>CV@+h+=^oD)gM-O^$gt|P?65V%bXj%&mDh~@9855-3J1`%OI{Pa z`6yF>hc3yo7Eg;VSDp?�_P=Y-q*AV^-3M3Z7XZAK@eTZvj;6gtZ!_bkPc`v8SvT zbtbyL7Y&ZAS1ydQx@f@^|0hP{$1~QJM!~q-)+%XF%Z4ckUuzV}{tZ*;ImhbHjiA&_ zOySvU*76iLxSYSLd%^mb(E?1|!aRZ~&V+i#9z!!tm#s_lh$DzAk2{od{uDH&ToJ19 zJDTy&6)U#zS5+ufQP7YE!PS{PTyJob8`k;;<;HVN@tJG9u>LJugjNTZ^P}fhx4+tU z(>mX17CMCml4k%zRq|bHcU^+Yv0CA`O0{q*11R-5?rjr#lrKZ0C)oYfGrRQ{ot);6 zeiyrksd8kcL$GAQC1V`55DTx$6U zwcx(BZ5|l}8D(kpmhy#F(MQ%F@<>yVhFw=59QLk7M&z~frRj+iV^`&0tKCEmJG#d6 z&GJxs-q97Me4be+m}o^e+|qH+6nX~tT5#w}WZH)niqpXc6?|#ZVfd#zURrT@5GscI z6}6DsXR@6&2;PpB;Ib;afDQIdO2Ff(P8q$lFr^i;!G1_4d#hCb7!R1KOsQRdw#)w^ zBpjvc`$X>O_pz!nDn&F^XM=6nMkN#L zSTbu*fxLQ@i2G2z( zVt2D&1Qn(XRkk=K6sZ^>R!^<2WrO_?o@dW(m6%*dZ@s;a4SM(K2VU1z_T98cIJC+` zil3v=is)zF5savBOExhcsxlhb3i}918TV~9Df(r_8WemAH6}H!>Zj_y$+OLzbn=#$ zoN=LIC>@vx6FZ{`tE7-4hTDzOsaGlnQPO!>>By?~0Cl;kmq%ecZLe3RLR;D1`A@x( z@i;Q(-iR_zsTbSWuwF%a`eeQGmXtFR#-RE;|Dn{Nq;Dz(Ql=-$PhkUv7a|3*-`1k+ z%N48B^{JH$Q%-OLKgtMzhyxvLM+_eCrW&F5{=ghtN%g#w4c6$Dh+SQ5zD9GOP4SL{ zRv0m-wtj>&$gFNW{D)^?SHV-DA(TDS9iQi2uJBSZNpfu~0Vow#MIwQIPo_qL|Wr9!1H@Xu)Vf7VGC_+vApI((J)MkH`LKK-^uc(3!U)9M93f3C^h75%XZmxrL za2%9c3T51l$~$VirpxSbm9lJJ3r^B12T*HYD23nWGP2C`S`MzWbS2fjygoB*CyWMe zHru+Zlvy@x;?fLwJhhD$fmF``J@afme5n5;xF>k5IGk?0^tvZ+F0@6N7!ReGke7Oss#V2C5Nw3MD*v5rzfP46Aa|@*PuJtFD{O~#0=A2ql-sUq zP2D)8+(ofDtFS3ZmFQzF%80L8h9Z|&4WY<=SRB8uu_>V{IB;P;!pFX(f-f1Uqh3E2 zYxlRRcudQyvFQewa$%O1wz(BjmD*ITYj}-gH`)&ALfdSzMd?xi#ZX4UYDEcJ9-=O9 zvF+6bcsL*$ieVl$g!F#3@ES706wLzmwK-U?ASKnrBK!A@ z4PH*ZqLf*zhA-9Yf}U5-wjDI6n0aGN;%3$e()-i>cb)VhudtWHao^Ve>6aDDGtviyXJFMqP?rRMlU%24CJ~_k%eJ z_Pi&p{!BBp?V{BNhgD=?3&5HeCigtHhDkLMF7TBV?df6{O32{snP%(5ymd1hgW+aKeBykVjR~Eb1?F8zv7u-x6GQA zl=UB$Ar$uC6#Tcl1};lJu0yScc??3eXWUk1SI{|J=t@aZ(a7Po%23MYTKX#Axxx@t8Dudq5Ex0(XAnMuoi5#= z{#}||FCx&KT9mo2b_AV`$I_`%mPbS==y9#I&9$pg#6DamB$T&9354)kEXtLjtq-di z!}(2w9m*y=uT5vXY!gu)B5&q^i~1*XzBIsun_=5D0i|ekZN7YtwxzA z>x98)Ea|IneDfRHmm55=7PTm@V_lrF*m+y|CJ(rj-h!E+xUO}pQFc7c%AZZ_unR(w zh1}*|opRKwyuFet)69-@nNZl_BcY@Yt6Q9IEw_fM;VpRVnVeRgEe)4TN~-Hicdy_X z!lG6@7EDzMZS0kObUD(hlql5wdY60rw*9zK4SX_PP})4LXj&n(u5pYVUWbSY?qH8L z#Ju98pgnaB+qA2b{qX-ZM`g#_vH3_32D4@cm#6xB8Ll-QM;L)3>TXAD*N3=PgP4>r9XPV)lYBSfW&7 zKl>NBe7t1|RWJJ6191TgkZ=;|o5zl)4&nh9CfpLm7))iBvuF29y*jeh>h4CXp?0{! z!U-Ae3{D(c49@MZ||6wGwy4xnZ7$NPh=gCCdt`}Ym@EKI);~x zl*Yb}tG!n|Dtdw)Pu7GK_mTZqgTx0Eg+IW{dw#=8|=8J5}sGL3d~c6lJ7Pwq{?r! zpE1ahLvVXh%MeipH&G3XsOFnk$)vRRwgnbFr(qctzr~JQ$b6J2%IwgnnEEHfo^F(3 zGpqPo+Tiq>-G(Z1ryU0ZF0O)WzFUdZ*oAEjnd~cc9Bv_IfNLh@$Ud zGK$p$4+ae<^fPSKh39pDgD;}LIazkSzl8q!t6OK;hO~HrI~Tmi7)ITGhyDk@#Le5D z-|e_60*ICJ5I3DU!`}?1@{58|!)Cs;yr=MSJzPB6-z=`qUgy1>DSW3)ZHhE5uCj9MxX#O$M_DnA1JtcsJZ~Zfk7f<-oI7ld zCDVH=7(xkU8^brX*smEB?}e&R@aEncr@xA{+i{^P1#vM8pQvo(Kx!4)xCqs*)Yy*> zE&@*-r+w%D)=258;5o7e+fgI8eTPAoN4nCl$m)%KY3tI)g()JsF`n*gyn}Exd~d68 zZ;OY0UX-tK@uX-QI@kVz{fUV-R&nQja@6??%<2EU6qf7@r780QfT zqQTc1S5#de+j0NHc_dfDEuPpP8o0bh(e;_#Vc>X$Ab>jWs#cP+R5d^Scro>b9d~`d zEcGNU>F$W9*~>gpJI^`Bn;60)`G=N3HQ2|22a$qbf9?#V0DngrHLjooj)G)HggL5F z_wmi(LR4((M_E5Q{MEq1j-Pe1!m~x}G|Z`97IC28QfTXA3vPwRIm*)YbPa1Y9S>z= z10A~za>gxtpsG;JVKZ=ObRcbh5A53ZW`(Ir2H1NCJ1`^vBW-+hMA&{(tpu77`nm~W zj(-i#g^eB6R9s01E^+dgRD(-9VD%(Zdh}Pfra@HpUo4#|WgJ@#3J(ZjKuS?`ho*(_ zsJ*Gb8e7h>$4mYI(uY)Vz#S1mMSS3AVZ6$!A(0LkaLL5-t45I@HVdTT6JSSXRC3_* zS15P`q8hy%)ijW<_|5QFpGV~z55jnqRpYC^YUZm!Q6E=-l_?_^U(*526Pes$ik{ZA zp!%w|1FJ_eF%d@Q%DRpV|C>oSo;3HTW#=%)C!+H=ZtZzTNp-Bj|2-ZB)s{vMT;y>T z3iZNlNNqBl!CNt$%RAyJ$wx(a^7s?ZK((-|1GkTYvpcR1c6XF9@S>S^U2UXtT6v;X z8EW;isZmeNyNrE|16%-z&r1o>*5_#3j6EbZCMDH&q8OW1DJSiFf-a z^zA2jHnXs%qp-R&*|FK+Sv3sxHlJ9383qB=3Fjz!?r~0l-5EXWBX{VwU5MflC*n_iL3^ z+#H@>VG;*!;WMZ}!f$O$(3Tf%L)9p=16ziW#d8_G)KQ6okF^a~-^^nj70Syd&eD3B z$d&KFr@7JAS8bio)&S)4YU)Bqq>pf_EnhpT=pbKF2dLa{9OVoWnurq@%Tw^{8yZ|V zKL@CcZyoau91nMms#0p-^QYAFsA~QS$5<1UiNT)e{dl4q-Hm~tle>xs2w(^;_>tG= z?RFt_>3dj(BWrjK3#Htj*e_b%!hQjAK}tB+u7b+>o(+qTp)%o=R63@#y0*@Nw^2A# zK^mTW2)$U2qdtANp?xuW?-QJT|NiM!NnTP46AxV%&fJ&S5hLedd~+wnl%|wsG5++{ z-)KzLEe>VyV3k4lHj!g?It_sEd5rfB?XHXY5Ef^YZl|tcAV(#!Ds$R&6L#*U^cgZUBvVVI&)ymiDRWsICaEDX+u=eoD zx_s%c5{fwgy8x0VbSb0;2RLzJ5s9K{JcnvzQ70TC$&5GwlQi!D^kU2>?Te_CAm_>d zG+_EKU5Ze~*X@1P@(?E;XG%furdM=Vm~)G_h_PzRQuN5K1u5c!7C<$ka7-y&(g_nS z<-FT)QI%85nPX&PWyAAB8TBmuRRhFLJypUPp?u1{>I4rJWzX&!stQ(c8cR~;M7$bg z?*?9!nZi_XC8u{qOHWxG?ThUitk5p6Sw`_%s#?`o1N^Fqlv~zWT-B-ms&@E{M(y=# zI zn$yXNT|mg0+n}5Q@7}9OaUpS~DY6poV7A6OaWp(+!LvX4UTsNNCq4wQI-``S8&?J} zgyclnbWU7Z@_P*R`?b3hm$pKYv*U^$++e|{l!gI%awrbdYC$K@^yJEQPJZ!wV{fM) zUan`E;vS#v>r6M%vF&h_P!GliNA*rKIiu;O*cC9!n-8NXz`Yhc_|7H?$H7$?wGR(r3|{)AZ_G( zzWO`zcRpp4C@kUvikPBDdQ~7tZA?z_$-HEREblKdSp`qwxs*%{L>NmfNkQ-TDo68b z!Ws{p##8^A9Nn&035vL-m82CPLo+^0W^Kw!!VB8?Ts4A5e~vCDO~m`YGF=xxcfvmh z4o`806MG1Q`n}ue8Cdn%`+5hf6|4~I~szbsg#q&jaKnes+y3T}yB7jH^+ z+V}CLRXcn8>UT`;#jMaGLxv}*Q=eMuMVhm}QAVZwIBmqRww8F)i;82 z#zLbKR^qFRzcxDYzLVZQ){Ztkd-J`YRw572tj$h1q|77lHS%Ck$_MvQ%T}kws6`G3 z$=ld$HaZUPA73=TM+dXLS77ybI49vKm5u8zdEqp+VBauG*w(MG%Gt@AoKSdOrlt48 z)92g$N~vPM@B$VBe|@ApySYoRZ~|TiH=q6dY5L)Qekx+0bF7Izs|jl%pHBxIfVEiN z$gmbP?EtLL_p0tewmut+@HvjZs;+hjTs4b%Z{e36a*j7K9!i+aFp6B=A8*i(?C-1I z{nd%HpinSTLMfg${$8*ebd)y$A@HhIShd(-7+25&IR8SDgXa{ya6u7E`{S+V=>rN= z_b(wg>y-16UR?cg#)(Hi!Y<21*tNz3D=K`P^D&OfjWMqB@%MYbqtyY;y@Pw{cjpun zUhQ@k1+4&pp^$3dY|!(xapjQ z`D=*hF!EuTz7qZ#ga^*&29>9NE76|zw|OxNkA4gN>AeudPSCXd=)P8rX4S?+%A;=f z9Wui)HPJ`0Hc`ARWfYJ1qj8f48}2}s#ye4)JC5>dd2xE}!@Qy+Izn#e@#tr?7eiC0!`95ZkPBP`IE6=}dzy$xKoGYAK zYC{5*-!nFKocSZ}>wR>xJOx6Ficp*E_(FIAGrk73@^Sg^Z0XvoGb?`|7q&eSCimai z){0Ta!}t)@rl9MRL6Na;KI@)P%BuLnF1T(|hQAdgp=@~sXDq?bg<%y6Hi?egFA-EwdI<%Nd3Ut90m2%@lT@h5L*3hDKcI&%%8dS^$JH`~T zEmD<~8`5|vzKH^hzMX#`?7}So^C?Kc6nKT<<=2#Z#nlgCuiA_Y9HY$<2}6U`k0o8O zBT{l|X_vz&iC4J|rtC&TimH`mT*mM!jCpPz@hY`~3wB4U6VoM4)H)0)rw&HC@=hF& zMls6y0xEm?d)(IjS;>W+RSL4-px`%H4IB*#r-T3EmhDVc7jCSCNxrd!RWoMu`M(+t zw$=ZSZY5db3(|s4!vYC5GDO|4=^Yxv(BPwKsV(7NXL1wQ6qD&0 zrNleR(bL)Y@TI0}n6GNn6!jx}Te_ZIk&+(_3sk+Eqr1pXvLG%AxXV(pj>Ci0=$5Xm z{Mm7H?**xp*62OBZ;o|Vp}{*)Usyp-Dfjtkqs`2={s z-nbThC~i31Q}$P=NPAa?>1Va4y$jpGSNp+*NEgLvXGe6W{{i^O6+5}GQ32GSqR`bZ z?-ix!z9T}^h0dXnNg|q3Y#eUXvmcr%9ugLS8*=WI+|2 z;QC7^!QN0v8HzU~2P6JQ~15)SgdV@N~w-;STzL9_c-p z2n`7y=Y4!SWRfcdk7dW&r0imDm^zIH#XRuZRhfw}{7fpu$AUK3np@deWt5d74iFji77<*0Z92fSQw_P}W zc%!Y9rFLeB%Ll7TR(fxnr9Y4Zl*F;n`-(EFDBsnrvxf%v1_G?emVrt z0&!p7qfZW7qi7_#GN{w9)*uzN)HT{fA0}b+(aHhTW&)l7O#PPI1~5cFde8eF_BC$R z*z$S<`!8qXCuCgTK*?6}s$^WXS?$8PK}dXz{&DY-N`#xEq`)h4+oAXxA^9o(4cTwmPjELDy@FO`(WeC1Fng9UHbw~W5qLF z@GBYQ&$0t{9d_YjPf*^GRnQTa$KVY5&4ot~trzGM9ZEZoV}!eZ<6T@8PwYJP@+Ky0D+s5N=V;K?=D zM|e0y7z21k*mfBtakmD1J_&Jm9D&9gJm2N;ejsG&N#_NVtEuI;*xX8{xB!VeCPmVa zjgt$hjd$2Z6AG)ET4izJvPE!l{hkc@tcm^#Y9Twj5ke7b_+;~>%J5=yOHzUxdwg@} zf5>mCA#QA=dKnp(Zkh`>BCl#A)D}SqsZd#My~kESC_G>!(pOI_985Vcy7}o>W>+7$ zGV@3yNQ+4OxgdfpEG&%Mi)d_b4<4}|>tgsU>?o02IZq6R5|95QA(FD66bR8T`RY7z z;f64O^AzGVu!-!CsY8UP@^t(Krmn*PY~+nz@-hV<ngzX1iCI=*}fLFk*UhecT=C z=gV+l1{H9_NC6BXx!3d`l3Yh0)ar%Yux=~bK{%pB4Wk;Jyeg66>&`H}RQ3JbP>Cy* zy?R{BA3}eOg4H5_H&hs42x)uqVByQ=(`xDlD=E+ogC!I^&I+XICEZ1-%?HyX^z-<% zVqE9KWiH+Jj0lFrdTbNqx+Oc`Vw2UXG{T8WxNd9-S}`|X{Z|;4AbRJmZUKE zJ%e42(5P#fiRKl?jw^%kZD~j!*12r#6a^X}k1B6$#i8~d53qWgt`%X>hm1fNf)d{LSz zbWJwyBRWKJ|Eq`D_5Rnb3W-`Z?@If&D)%3_{%I+0p53qvf{QH{iJ1-J{M3+|Zrm&h z<#k;t%|4I7`xI^%wDA*NIPjIQzWcwCxt7*NjW;HHN4#J?cPo6dG8v}2SF{`6nSj+A zxIZ_*Jq_Ks-Iv_Y-gLv}3fQuV8#|qV?zi024RBgB_h20)S95okX^q<3!tHB9`fE#f zc}{P(a+l%sS!*3H)7FhU<&cWlF%c+iE@{=h$MDYW+%?d_>bNMK6~ogYU~mWbNCTYH z(LK}vpLB9#EeONBShv|k1J<+Cu+UP4HgrXIvp0DCg|Kdz*q_wVZamH*neZ17hHMc< z9P#dc2|e63O^k=CPW%{>kooT? z?sU#H9kUl#zf5$0%b7hVyDR!2^XvB<#ns#??kFH?^E7u0&b(I*k5-;1yQ^|$)EBHz zrAlMG5YeqK&7Fx}OfTadw1+d@Z<-hnRn2F+*Bju2IquyCc)-l6DGZ;_J(lsQ6TOjzNlBgt@6Igdt=FFCPK3SGqeIV2#!81RbOb+3t#J?;3X_9pCl6 z8(W~%_2Q7bvTCu;UCAgke!cs-0Z!Y1DbS_f{>csRUMk2fx z?!^o@P6kb9V6{nHyzW1{aeHI--XR|P*_}c2=fG1<+2Q`cL@mosaP_^Ooj ze^aKrtwBPs;UtRy^s=V<;a8q0!K2C3LY+IxO1We_Hm%no{7xK0`^F_K=|w67d5+qs?W#eqNW$?$##8L)D5a?hg&H;x+da1KjeL`x^nnRPP(^ z?~Pplo9=D~n0U)Q&PSKvxZ{pBaywXf-x0Z&t!!rm{Ls$&CtwAq8;`FA?CEBMDd0&D zuQ7mOYHF_gJ0rKoeV!FzIQxK2nSh@?a@R2=?EZ(XFXvLEpQE7a_%GZ0*Z8$3?m*<=FnPupr0YJOR0B*Y=m|3FUQ^fu?UEMx_<68^ z1^lvz2a_RSSb%4O0aBpHXqT#1%wx1e9S!o7GK6_TJeX-I*0Z<=rjS8G#evEl=2>Wv z7L@eVHVT|B?ZFNsT+;J*%6PE7iGb~8U$v%LdCvipPT>e-C(;! zdg|hX@t?85epS)4$N-}&d+-L5!%pHwdJgL#F4CGB zo(8y}y#{CeZcPs?oq)?~dkz@jsJb2)bjfWM?KxYXMXfzq1~{>;2RbFWQSW$Wn<%U+ z%m<=mzDuKHFeJHMVLmR#c(6PHhN#dE9@sDeCw26EVt_9?d2kUYx$9#+KO10TR}Y?7 zN$!R2+z|nn^zh(I2>}cD^6WKofA8(tVSv=vgAXi(;h+AVD@N|EfgWXm8wYzH!dV@J zh9Zb(PfAEYLvO@;x1G}oJa@oDRp2lWma2f;hI{b2r+}W3p0^F~?GHQ)42D~yJZFsD zLu0rx!aHZ22QMWGIAen67bCapN1mrf?to7{;|=iYB+qh#!TgzLr;!^s&4bVkBH{aF z&trpO^XHzZ0($Q4nVvt5+*7kXM~vJxUwYyVFk}Jisz|7q>N%r>yB2u{8uYPCJlpk5 zT^Gj?>*C5G@RJbQbO^#S8-5aPT!v{ltp)^^fC_jRouBQ$Qyn64y!h=l{cqn1Z zZmm*Rdg66_*J{rnI;5;?Sm~HaS-b@>WmBv~%7k)DyRQnwfxxs$VlaKj_fp?Ve=d2$JQZ}p zS35E2!rV-GPU=v}-JUHv^yeP5PpD(|dGO^kgz95-^Z<`h{1!Dvek%eqg=n&|bUPcGoKVH<)^14hwkVrf0qZesG(otmubWbck=;n7+A-Df?xGa<;Hf6ur{XXRgR_{Lbol2-A7}qMOvd& z%6*<4fOzTSfoFvQPI<)pkmQ#8ht0i!Uq0sjLBN_%*boSq^^|uU0S7<#yfo4Ms&E1j zpSQ!3P{gb1SHo}wc23t&6Ht88_`uUnh5BfH1P`UDk33D(wgMXT0Cc=O!7ACakcJUt zTrDZA!7UK5v!90DS-?j{G@P0QEElM)HAy(&Sd+v8jx8*KfMbIs3UF+ogaD59k@&x{ zArkyIR*(D}>nmY@QI2E&#uk)-zp?ct+Aml*)GsnQ%x`QdiSY|g4)7ZrF428si%DqT z*g}c-IUFvC9Mu;kIHWJib39-0a4=sa9LYDfsD$y2Eg&&`fpPp^G?at)qWEH+g~Rru z0LSbF%MMP0C0Z|7I8-kR9ONWag7n6QAwn<0!J=M{&x;a|Ine|2qHP?NH@1m{p>Q!W90wOH90V8Da{ydqa`fBS^bG9PSSeU^&;EtpNbJo&eep9=<{ag#KQDwLe-xza&e0`J`|vvH< z@dzMLV2;|MBlt zR#Nh7^wvzB$W|~l@lHO>k`Lp>#^reNrOK*%jq&n(jh(uqW4vrq6SMMQV#87slh5VJ zOHI6x2QxS>{x|E&f3yDjZ&uEKvu^(>2fpS(o-f3sZw&C>pxb^pIv0eAC`K+$|y z&|P1B{`KJr$ww@n4=a%mE0qrm&xe)Ghn3HVMdZUO0+adItFN4oSS24;Egx1RA66?L zRwp0Ec9*q*?Qp6X;MZ7zNq%oE><1$6f~kpx8GVHn$%h5z!-Dc*q4}`VUQD^dweBX< z5^7OaYl`GkPOegho;dU*svI&;P`sWjEvL0Lk-Ab&J8LRtY>}O1i(F2sf>sq$u2;}{ zntr4P5n8)2{_6B|Uh@LMm0BL5zNE$urUcj*!P+N& ztl?+=1N=OC%v&`(nc&<_C%O1j3a^C3N*`3xI{EQN0Gfk#12f&F0+qF%e#QpAnBpsI zoqXOXL_by5x|*KTKR``8sBM(i&(GM`*VCFPjKe{C6r~L_t)+fdv@sR78LjCMRp5=p zmR0a`p(cJR)h>Y6T%lW4P%*onsQ4i@tO}1HUG=W2l{J}e(1dDQtzvBEL5zX*9-8Q) zjnz=^RrRo%_KnH3m*&>c%K5A+Nb758RVuPA2WwYY+M(i!@;qDymY5EAZDisFa@Ejk zncP&mCQb@j)TyR6#q=i~t*KQkraK^sF|fo#6F1UB=6FI?YH1Z~vunzdrp|&}(t0+# zCG4hhR-gIE`g{R?PJGSz>*&K;s9_VWtfg(ht4JMcqX%rb(X2A!!yKlR+8Cr=bO9vO zgS0wY#rX4vw6gJq-$-mf1V8OY7lt&p@hI37W__smv|QzGV2O7ouUYF zu~uW z>?(8dr$2%6d?QnMi$Dt(VOJO`&NI_Pw}Za(XZ%ds$4q}w>v~$bWHv+K&DIm$brWwI zne0s?VQ(7A3O!+G8W=m%NZ5=ZIix4-Oao(Q8p$I)VP_f`JJU%1)f4`k2w=a~*HiYU zfwDJ^guQ7b>`fzKZyL!;J=sD5(OOsWAkA{LG|S0yt{g1Qa;`MXvC=H3O0yUQv)BW( zc$#Lh24*=>n#GSa%W={yexq3qlV&+fn&l{ImVbyb%fdIy3O6V2h6nS8Sx%8=IYgS} z3~81lq*+doX7R4fa(*<+zrL8|^k|mDqglKyvm71Ga&k1w!O<-L>|&Nvqgf7(X8AK0 zvm6=Ca$+>&z=*S@Sq_Y5IWL;!xM-HsqFJ0Sv#e0F92L!SQZ&m!aju*b&EgrE}5=&2m08%kj`G?vYszhh{z-CZqZXG6uZ5%(9luvWU#GWX!Tk%yKF; z%c0OLXF{`_2+iUPndLlamgAsVR)JX#gJ#iqvm6D@auPI)Mw>;I&2kJ(Hp`*FEP88} zoz5&;J6Du;uITJsQQ5h2{F^HZJ6H78Eb2N}j(&4RS?7wb&XsfDT+!6IqNsDl9Wu+A zZ>}8q=8BHaEh9QQS5(w28ah`LbS@5i?grWr{9j4ehFS&FavIYR*C@;BsGd}4qzwo5 zMI&t-l6#G`L?naW1m_Z(@+Nk|CA9xdEe1(}#<0XosB2?vs7q)?W32;{yNzMomQd3s z+Pg@;YNB;Pa=D3Q)_Du}H%n;dTatP1Ev-l363k6v+2QzUHJKK^rPV{+QWrk(J?-m9 z*I#Ib!JgDq>lrD0w?6~l3RbL-7k|DuF*gP1ue1E$sGj&E-EOKir=X9uLR7h#)&_g= zh-TQsf25Vow6~GmYzEu+Bb95e4MH-xxz-!W+2&ebB-L7A3;dBjZ6TbyTWIe9d)@*) z&Y^NGwRT8`wgh&ZQd;6d>NxFh36|qzYK0yjr%tW3jz|{i$<7cDHmL77&1r*CJWglYXng{Y^DwOQ!!E6g(C59hGOzynp@gsA!{hW>TM@EP zPhPauYMbs+t+&yz?bP{gjO=z=^R||N#OEC?x#)Hl`Rxpd48{F$#Z;{lM}zNf+Md=< z8wIAscG?$6-0ifFkPMB{K16aMMjMT!O?z!Zpa_^S9|Ec_pc^q-HxAJrXf$U#Rp@}B z+)h(EXz%H)eqV#N!y=0Ajt|lhWxY12EjwyMk*w;d#UpvqQ5%V5L?>xLRwtoFch<%t zb7g02#oMV&EVOSsO^-#lZqkZa4Bt(%#X@~uC+#z z*r%K^?;_|qzdm!=7@JCbJx3) zS)?br0Z*+bil3xUdZHUA=|oS|caoxdf%7DN+6$_9l1}%6Dk8JDBvX51yiU^9-dLt5 zsd*nP#gnwL4~Fd|h4s}22cG0EuDygVj=0SFQjh8dS3pFKzM3ysj`u~i_*AYhx|vBW z`bqM2KMZ~*{nJkyfn-pB?aj#hEaet_#>93-y^AW$15_WV+qA7eH1&4c0Br!mM*y;Et*nRAuzLN0@}8l;s$DykenE( zO+?amkTw;`fkE0dBrOJO(~;~QEG&)UwaLJ?=t=b<(9_+te26vyNwEa&qd=+Ye2@?R zx+|D&exTLYV<026aS_T*fbG~#|0G}}o>Ix7+WSbR4@D`yS52&63Z<;2DDyL|zQip~ znX3hYW8^SY{gkc`gEBs)0mD(}Q_32yeT?Lt5oq#L+B-t9=#iL|r?hIMHU>%HdxA}Q zPa6-+`5tPulK=bK2Z4NloA`ZoRM)3DH?of&Kso1qjE0qL@1q7Q4f+6GwbGv-K(v)& zM`@oRIW!7wu~M@{!7>sd+Deh5v0ALOV6K}bVFK@s(cVKw zzp>gVYBNYHBX`$Y?UZ-3VP_EDHG7PyY%e@ zZ6K1rCtxD(Qv8Pyc9)KRh{3x{4L(8-@6v{kgckfUhVm{Yf2@6g#P%^Nx=X!25p4e_ zg4OsGX6`P1`6*5rcgg)JOvYX6F%cCl#@A?ISxna^VzL%fuSwdcNKQ?Hnk}Y|lO;2A zGG=!%wfGFm< z{v~hxSLpVFwNyV@i>07WwE~GJmU+5Q*WTy!m+9JAPOBwrA8`70vNnp-XUV#(#1w6m zE^FwwkoD|4>M=uWK#^b9!iON?2w55Zx#q_OntiSf<#fg8m@27!-x?Gsxt7P=C@?;u zwyyfj_`w%id7RT`e*ts(9G{51GQDCp3|0Dn9}D|Vm2(-bNYBTX6QNn zF&ifSIW?Jsso6n&=D?iopj~q?Lp$&vxM;!-nm-rgzJp%O#klXF31&#yL6^*!(jC;| zOX1w`CDddGm6!)r-9fYF33h*;aQ6QS{nNW!P8DSEAb>&}A{)?d5>nUXs^lm*pUIcAgPZPd@30qIM zztP4;isWDiL~L^V7`F7K2dHj=57LCi+GjXWyB2G+k$kiSC;x-=?-Feul38im*GNi{ zwiHPQ2}_fu+I(O~^`z%Ajet3qX>*W#^sV+Kk{90!Hv2pD_aON#*OHOUT`rja3U~zv zY5od~(^}fJ0z2+ndbR>P$Xe>L5`N-Z+PG5dTXZcC$%`-?7IUxjOiF z4K!yh4O|O#T1$J@daJZnK)D%}3sA~zRN3Zxt%2T@-4&5vv67K5k@gU_&)@J?*3yOV zr5%lakam3i1G=}3GJe3Gy^a3js8IGc24{mN1!#P01a9GY zd4za*0e;@nG=Dvujw^I&Jsip_RPjgn$5L=YGZbvyoJ{NBSs~V4snhhn1Mc3n^}M={-IT%;I>VRNes1DURpW@ZPnTZ3h(8a z;Qe+s)&E7SLkO7mYRJ-Q)mAM=XBs~TOatcXObC#chbcNk8kUkFJ;~COa@(XuL$^u$ z)@>7(N86;)Ew>BjyzOv*(&>thRsR|8VmeL!Sw`T<&+xd@sq79ZH+l!WxO6(S1GW~K zJF(fN)5M+7xpca?6B>;n+a;V|?t*8SPJiu!A|g{s4Q3^@%L;yCI#vBeYMAhg_O{-& zLdVdxeW$779<45Mc&$96^!yj^0Nly~*S&0Ny9J~4vU8h4h%kvQIGyJ1mM)&&jpJH6 zmE0rKGh`2@{02?mgDP*(sXbWXHz;zi)}!bRo}Q!EA#Bc#0%=DuJBW!@i4N?=`n*9$ z_u`avgZ%bE0bup^Y4uG%P|7|CbJ41Ou*oheydT0`G;lx6g^PCVhdppni31oR7mYrE z-Q7i34!~@?sP#c?>n_@GP|5{m!Z5n%^Gx`;E^=mKxLwrZ5JX?4frsENU!~25;5%O> zzr$LmKz<6HxS%X9_vWWkkE0s?db3`PpLc0or5_H%m%d8R4r9QtQuklAUPYzIk_IT^ zXjqW8ZA0I@ahwXjF8J_QDbeAGRJrU3Y?c)K@+}lB*OXrV%60XNDMGD|Vt}tw`=i3S z^eD9EDp`(VPOs9NzoGqCY4&f>ldJUCZ<1O27>4F5eSQq{eU;AXSk>dunyZv@T-tj{ zPohsqf#ee+^UMihsdiE-ntW0?&z-~oU!|I-1pDHY49#Dsy#0JW2+iGxFG4f-qo0Tp zRFB5*)O_{6oQ?;vYXa3jj=p$94Hbnn_h~5YRqA>MJMdN7b_Nc@RVtN*DM7Qdu=QRg zPnI;M<5{TiRa$ozlXsN@v$cM@XxC(jE;Lma%^`kXkLjq4_Ad`}HL2D1OCH{~G4EqB%^eS!n0}JCS{qu)Z8T+SX(w|yqU2NOs z5W8=AK{9QJSPrB^FCTR+Wb81QhKo|*(~B}i$1h4ZN?nrVy-QI3tF-TuOjwD_Qp2dr z+F)cJyDZZeaRqw5ms(u`wwKbbV1)LP^9r`Vz0~9*CtlTBak}xU_BN-Ut6B?A zYhTme;dH_^Z2+fduHo>#jGkPBNTTq+w0@Bo6a2X`E@BgRY6Xp*>kh6ojlJgref<|~ z)&+8d8A!$P@Pz^VQ1$~_Gsj*Lhm<-yYO7KCNdpj()iEZTev#j~i` zZCK1Kns6KHmPOZZL*26Itviw|x&zT!lzRutmqlIgO6HGu(dw);3!0Ke11(yIqVg}# z6YHSX!O{4<(}^1ugxx6j%5Y|p$AS)LQD-YABa2pAr2(2%T;9Ly5g|_8FkR|ON^43wrJ&0RJpWMS_uA{7buodg5%6+tb z9esKq3t}Cey07)E#_t9t9^L@i9n-y4mEZKLDt>1uadmp$(co|jubRJ!=0CuOxQWg^ z&{`o0eh7DG6U9A54V!4)Lzsq5X%jVgEUldISW2tMu#%g|_g`sA?7vu%n`rUBT1#D|^)^KQd52m% zvCbs+1EK{~086dQ$E=s(nTyp9}4m=US~ue#DjNiw9hJH1q(Y`t%>B0x!_w!_?sg1RbU{ zJ-Pb=RUW1~FJUteQ}Rn_$YDAq$&ymJ9ZiRqB;@wQ?+*SYKT~cOB)v_!eUNN2<;Ed- zt|#4max37O+E}05UPuo6Ss=F?lJNy{`yx43AU76C@q)Q=k$66UzX|Vz2WC^d z6gITAM^~VFb05%*g1JLX59oHm+)+rn7Rr4O$&Ny~2~=;SR;X-&0pLA6*jtqNxt(70 zJT)qe>dwHnE_xHE6$=v(!dH*?+{qF4StR^S2 z+uZ9NZHZ%c@W=d&Zh=R8#_cg~1_#u$J*_;C3VEYg^JuiUSr~6Z?e|8>Pf(OM>gWXJ ziEGw^7#!COfw&eI)p3Gy_?Yz}hWK#kvJVn$rguJOLx@W8kn?6*5f7ERnZCwDy3JHS zzS#_7OMF&|nZT@qP|XBpJBaNG%q9@=62i(AtV&~+hUlCSt#yTVBs9xId`xIogeZ{+ zD!ZvkBEa3WC=sfEH$6{e7Kg}`*enInDlwXLH+D)#iFVWX#K?U&HArFx_^T(AMl3v+ z{7;o#P(fV_k+d?2Spm=JpCy5Fkra>=)+1?1QnMPw$)s>RlF}qYfg`DBGW3Q>x~N5- zo*Na1$dPfq%!M3yi8?Q^S`R>#lX{1BVCwl&&H8cu4NRQ z8dbTB7Nv$+JU>m1H0q5*qx5Pht-uph?Za9;Ms?D>qF`S%@GDy5i@Kdf4}FpAUi$5e zez=z!`V;@p4n82fr=}K6%S&1EVPKihKLW#2! zCmnJLS9ZqU@%oRQtmA!LJ92ya-(2|aw9e%*g~9~x@p_c_PK%XzMhBI6Nw<`ELZ2am z)cdhUv7XWYlS{k_xW}UH@U67PfG;VigTj;x#Q);tuUs;voG6 z5wuj2>U9%9s(Bo>CblR#OWlHqj6ze$ad+cG6ymAoU)v zF$X7!f4%*d4)dJ0!&7tw!vDtd46S3|Q<6-ubd3U(xIwj)ct>57m`4+pI7Dk9f=;Mh zcHm8jf9>o%D!FV{u~`uQH}<40u~*0|Gc4_<3`!iPASEKHnG%<1kP>%^AcEA(amF#c z4Dqj>%j#ta?JW=bV1)mTH&! zmDLP_$eGPdt3!U&IvZy2MRYhDrqV^^lieH$F*>_B1mbsga~#CD9Og`j)H%(m5NmRp znIVuMJ^4*Fu~JtqGa1E+XCgh#M#b76j&Lwj;z>Yqc2 zb7MZ8L)~+mZ6U7a#<^k+<;!DsgqWKLXT3S}GmqICqGeu&r}CNxLV@`h&dO)DN9ceteO{Fp}9(#!lfyR4;vKunNpX;`3H z3F3Gl%&eu{1(3yBno_`Q1M#i^{9Q}c3bG~@G;8~2m2`%_Ytf{r8Iq76@>ctXyw%U#(zv*WwY+gV_vzEGeV;JA zB^Iw3t~$<8mtr^>pP`6iY$-!=)bkk{UEFL6alg3P1)_QhROuPoU4k{^moyutW%Icb z;K7^vLDAy03+)@)HMC8bVbSoCxR9AkgUgutC}k3&%=9bGPM#*Q;rRbSl1G@^`|J&v(7DKeW&@yeK4pq0N2&b^#P zX^8!0%wiBQn}htyn#pkt+Zm{KSu+iekwG?^QPxa_W8{*F&XzTk;TTyNUJL1LIWr4& zD~IY`NGr>k*)(eGH5*^Mqa{k0H?t&~4PQDHGb&B@QH2epkzRl0~ zilQV<&5U%fqFLBSy{Lz5TTREpQbSE_98nW74Jx4uqG(Vhb2P-yN@j0}o|VxVVWu*A zLKFp5VKKD|nlXx=S20_vxMpy%T~&1NDEi&ZOhd=3nw4?vQ_T#8=voa!C5jd_M{R@z zn?)&abu%Tvz2;_CIu~pfqQx4dZ-FYjS)E<=uVMCq&axI}E;>?!P2Xt&)9Grm>4YsY z>~mB@+C4SRL{Kc(5|JlsnH9-@-r86(@G2tZt!4H?%u+3q)J8vyqUFs@Um9QAtl_Py zz=(!Vrptw=@TK>)G1VZyR%Tr+NR7+EsJX4twUgJyIEtdWbv40NZi z*;u1RbzmbfWhQD~4_9hYw7nksVicvRk0~XJ2551uzS%~rZ)t!+2VAhJF2@SYDel*}l>Cw>a4_Klt`lWwE)I#}2rXRrY zw&;*^6xiFyOzRkLjIa8?rc6(7LQwbl8=ERr1q>rlvoR(O+`Y^`_{K+-Wk9cQZ2~ zj?r^`DStCF1&+~u(o&mdW>OrZ5BbtGeT-HKP^!($yb$O^1t8FU(o@~$Y!H12t8z9s zeZkR@%0r<0l!ZX2DFuQ4l97hCz&U|CO$i9}7i9)rMG16}f)MBwnQ2~2GXsv%GqTbB zmN*HaK()csw=z@fnvj0cxD_sBxnH#6E|Cd=%&pBpttP!;NNY2PM$#KDv^KLs8NDGV zr42O`;5b*PnFpe=7Gpxq0G&p<#I;Z}qejvtQnfMDYb0HwVHq%6=2(c|JZ*{uw)-|1;qV6<|K&DeNn4BXl!59z%9z(5Brrr?`sD5-qKU(`58QO z#|dp0+Ou!_-eCsogyKKKtbRCo;MJObI2k>nqWy6)dPHmb3#pV~~qb<|_pH3_}*G5wr` z#(PX%C!50{UQUKnkE#C@48zAXcM9s4FRq$GG?{8Hg7`Gm+z7E^nz;d@)^u|##LwyG zHi#`V%`5$_G{5$Hah-(N)TqQ(r*TNsW*eX65)BfIh*RvF})GnWRAHO z!fP%H_npSfHS79(*CpvQ6FZgc#nn;k+L?XuNwir9wfLPnk+~e2KJ(0F5F6(qkMC4} zK8DkG`Z1s5mMlP@|4!u=nyVq6E#%OoMJUX7%D&iKpx-#!loj4=!fKT_%>c=xF17ds zzrIteCFWv?pGz=hd?&x9=0yCJU9}Yd)t}MFrRHpiiOXR48Rc1yOrO!kc4`a z?@DteLWftvy=T;J6^Fc6!{#$uv)Wt;QGN}F9&&<)`~|AI&TP&f1sK_|J@EFMS<_MEI$Y#lpiJxWH{>O?UT+rtL*dYR zOk@}6@AdGH71Ge^4Q5b0{3%r(CK4S+_13(z>xLzyA6w1J$mu2Z+KB3UNeMT>*O#NBY9v;By zVhw#gfXlzFbpD{3ntC3@*?22$KZudWXdd1?YJUiKqQYqZA^03djSr)z>{&1ydCmI&G^V3rT1yJO~??LQ;V+;e6@-b*=V7~T{o zzf=V-BVC%gqGJO0m=CEG5sMHs>kX;fy(3$?s>(FePV3nxtf|vpO7p z)|{robcROI$Xd6`ja+az|{7W@?JEjK^(e> z3jRseFJZL&r2Cf`cDrm&N9gBe45*(p{tDXXC;44vxZx^Jm_Mn&HI6%U&79=R??9#6 zgGyCDYU=LZuPY|CpVaC)=H6ZO@H!mWMR{-Fq=z?|ZlDkCqS`mnL3YuTo9M&4Xw+XA zVY?_}6!O|dyQ0kL`o*MH$6&VAG1YlJ^lK)XX&2qSWzN&FeU2lx&vDwm8ZBwwz@_x> z+xQc+i;~5SwlD1Vr!m94hp|Jcy9_ z!Q2nA;UkASd@>Iql=(9*?%9)(xUYBdGw$N9Am1-$Pl!oha7$$c#rTRd^a>jB6@Pc| zPLvh{znNnY`U#<5M7ot4clGc?TPK+QiEK5JWn{lBS`vmjEA<{!bgMIjUks~T zntxw$QtvsTJH`Gt)ruHa8N65YcMPi-M4FgZV~9R6k@z;wT8%$#A>6B z^Q%y$M$_eVFn%|Ql@PJ7lCVirjEz4}hKk=v^^>zWl$;Z!OTl>z zOTj8HQgFt#QnJe4l$L=6nNo3RN-DI?M*5nHZMIHr^+xDgYEDM0P)G z0K`&1YY0S|G}ds41!-6_c3P`DLjBTO10mj~m;|Yq5f}Q(@9EYw0zR@i(x%)0IMH#h`4P# zcL?p>s%`h~VFtHuD%^9n>f)yQ`HWUshy` zp4BRh`}XOxS(6~vXG4X(rmWd555(&1)_91#Ijo@&8**54Aj;;nCPSRhX-$V{p39mF z@iLb+4`Os~YesywACd(3@Ui_xXi+O2CEkX8|H5-)&1>wg7Y}+<@>tpb@Z(w@OTAH) zH?LLMN4>g)$vt+M4~@uVr6n_;RfIR$!{mO49_O_NL$hT*^ocukCLikN4wVQ%gWsVY z0q7HVC~JPJFT{fUsHr>T6UcB_AZxx0v<3loF2L|^0fw~;THOE-6+{i+q1=V6o)C)) zS^b^&gG!?`uS-+CVpfZ!?fa`g4g7l5v@+n+%259NRttJr*ou!-p9^!Q9gDCFcZ+at zjf%2+7qzHdjI-HY%<87?d)I;;?^=|oEbL4!ZY6=8#l_jPL?t-o_!8)?cjlJlz`&B$ zV4bjCQzUHHR6EeD6g$wV6z0=A^rDnCNWTWuqBWH8LxI^*nKMdR)u`LGY6+sNx(u>L zL$_mTYb?Z@($++X0cETy5HZWL>%+^U_3zO4ver0lE}?o^3O_V5tu|MlUYElR0G)E2 zqF;HdKSHm|a}9)6z@WWD7b|dmm#fINv%MnMfPW>f_Gy)@K2V8XnN|8##td+WZdGQL zs#P#P@6gUFR!^OML3})F;D<6ML-swZSS4b0#f8Hinp~AL`cl>EqBS4l*^+@Dx{}V( zjMJxm`*uqALN(5@Ty>0kyrNf~^GIF8>WI*Y8Vq08;QDV_lij$WMX6dio!p_JS{$ln z_0w6E#Z7txKe|qoQxsm)ib?ZoTS=k6u{I}rRhx5dRmU0zc&`pOX2ZHT?{UXZLlFU1 z>bdFaCZ~tF%$Vx&Mmn{NRx+P3{u0sXcmi!WfHK z=C#tXff4nrd{jGK-NaA}s&9>fh^TL6^JZ9@QF1{8t3w)op7$Idr5fr-oo+jH?#eeqUQwEcI7!c^kcOC4=F_x>R!47s zjVtP9OnO+Tekw}d2y+urrA8<_(cwmL>3C5!DXt?URPc_#Ya(rScfja3aR1NCZU<;L;iR_L!6 zDPC(Ny+~79!`?+o6pG<;krsub^IxQtZCK1`gDmk{SX&GpJg;oaFjhM_caes-!vuDb zzP3YmyGWt!(fKaY`Sx%CxphDnxk!gPU!!F6h}esaF>a%$xMI3(j>nsZLiG z`?^}05xU#es)I+WdAeDp{nY2>n1-v&(Kk%Gx9{AsQ=1+bYb$9?H;jr6^uC)_!ACuL zg<73CJ`^|}r=Mk>CdsHvcl6K=w5+?;D?UG(UD*N;V^_AIB!jWUJgXe+Y2~Kwr(dOu zj&Ijz_drE$pu|1l?*_@NrVNrWHi2cg?TG-OCpA`t5X8mw=_LR2tvx-1G>4#bDDK+em9{7|F^tT3j z^BZ2b=3wWVMlI4)$N;M-9wg5gfJR+S@dsKXA*Kz)S#>of9E3()O%n#8hpeX9gVC2( z)A+$w-al;I9*jw26Zs9nh}lFnhFDD@)(k9UC)=}?Ig3}=t98Unr;ff2L` zpYUKfZlY-;IlqS^S*7?WTyt!qMWfi}`%$2WU*8?|h~l_{xOOlHw(GAcTPA|~Tp7D3sjSS{l7y93P=;7NV+1Qa;Ms^-(D zU$<_dy$6LE5%l*Ils1BbreZRSpp{z0oo2N}sM9oz&OMqA7UFcu$iVw!)feG^-w7JM2xUEiB?yt~1cfZd^l*aCFUBA~L8X>p z-aLU%ggE546xnR1=1cKUYcnleifMQ=#b1VEZKmt_(Q5e(YKo}uSJ9Jrh@BGb$lzOIK+l^Fda$X)?t!~q!R0qAd+UT$H4z3QH=)}^(v3~5Qf@QO3X!yJGs6^H zFkeTKXN%P?KEINdq9@)iNzs!YZnA38<^ViAkEFU=aoS-w($lQ1_|tQVu5HEtmP?dm z8*;xyy|!7IAi}nxUhdMpZD`NClyW-;*!7rQSPmTDnSWcVJjvrFc7G<|^);qMt6K(>tvaiPU>*FcXv5 zOdYcNdNNSJE~@|(+U&xdvW(8|LgHnVZ#O#AE27=-;T1*gMo)P~)%IW#oJ9%t;`-sx z9$YibqUU>XCT7$h-FVDiG{RmwwimUymkRGgs=c&kAF7u7M0tn?`_U8c(TV-IM7l?% z4nXrBZ99N$mXmb=b+eoT4q90u8Xm;iXgN(eh{jt^FOOKoD9s_OhPPUM#PE59?Zyhd zsYp8xq1!B{H;1ggI;OzmI24#=bOBm;7<1loS`v;k-nGLRX3Oc-Vfe@S1Y&z5{GV7( z9gd*%oQps0KY||1Sq=Fkm+eQbf`~ybCCNYB3j8Ch$>F#V=B%<|`SljIm5T0$TQ$8d z)AZxG71a3{PVtv%+%fd&%XIk|y5VKY7r~){5tv&p(~$^N`(?U&0{i(iIgaZ~PTB?n z>Ups$zm*clNRyEopTOUd%hcfn8h{g~qpc^bJUB+`e%@+17GwC^I5e`>n>5t-B&MYG zwB#iIlKyYZ^OIAIH@g&F2M`_MsX1-4)&*75b7}fe_wWc)ZF|Ijj z0qS=iR~?+b6g@tVtnX983z%BY(aa0ztmo+U1*;+XpSEJf@cA8wwm*G~cTX^5_!|o&^Ha}*d?5d&QY4n*5o)GGb1UD%x(wJ-OE-EZL=U1yJBVY`iELw zvBvz7yv$V$n)|e#>{7JtDo)|ssQOK-48?zCC8upStau6ZYBm@*|6ttE$OLvO#pBbg zYpC08bm$re&Nlja6Vr70>+oe8ExK-Pf~a=Gnjc54gkvntNyENdc__tAWX2hEhCs@~ z^x~$K497?w0)dp3XyIR&4mf!*6^%l@9ivuJI4>=xH&Gati>c8q%z2Bc$9>#6cy|jO zXfYkTZ)Kxqx2;azYRw&^PJlNa%)Ti@K6k9@h@ScYml4D6U??r7rw`D{;@w3rTuhzs zTJs?yA6liT%so{8Vp?zymzkVZDXMTEos2W82Z21={b9cA1Duf9d2~pyawbSbpy?tbjjr$1>qBhIVqhqmbU%K=d*UVe~#@)hCboXy-5k#HmR(U%3 z7^kjJl=B~JEyR_7EP^=r0*B*5?Zv{vLl%E2XsYPxmeQ$v>~HLfTU4 zrrvnHW=;`m^aifKpl%<~&r`p}F;{R}xA`)l93zAC9gvacT2{UYls+7qt8nOt!Vywfmpo+I0)(nHQA6 z#z~T=s~Qj}RT+A2W1i!}ghHSkZU1mQ!w0JtV&Hgx2>6_b5`V;e#E$2PqZV^A?#(5y zcNjcvK5{AGesv7JhhBDC`ts3A`-l6*KVg)zulWF^&76kcwiNv_1~@hanyQLM)J|x_YdE3S~y18f8hG#7!CWu244Tbr6T7r z1_A~OQ?6g=C0y#8qkAo}nN!{Z-H@!P7cwboDe*7Mo)-zXDL zNP%;_?LJ=r%#CTcrln`lUk-WM$>w~w^CLQsm)-e)D|MTf9e@bbX8k|%4UTS?LCl+& zc1}7J-L9(BRavUXM2Q%70mQ=&{Bd-|;NVNsYYi!cd&z zZP$jl<86mR1jV(xL2Qg`_l5BHvByIcPivQ?>serCMtqE!IPvUUpv%(QMW|CeyAqDK z#Iq+ul!$Lnf;gMb&O;N1+wrN#Ej+Jq9f(gE6WCefuovsMP(XUSC{0RYXQ6e#NOm!S zJsaXzdb>JpN@(ZQ{x;l!!5z6W^V~#s9>nHNWRHT_mdLK9bQ9TQAU0;O^V5w)cGj4M zu}~S^O>7U+iTm&MrrOcp=A!XQ?3z$)E3G2()VVKbmpBpPdvj=l$&55NO7XZOjhz$6Xw^dG zofiFNWT9~x>?}A&i-i2q1Xw*s<&Gw(4}sdxL-+mdlsNwAZd(;x;-keL=` zv{U04?U0$GGICczV>ALnI|S0WOl%j8Q5op77HEe;)H|~s2!zHcMGrIE1#yfPDNXgW z*nT)hdz6JhQ}p%vNDs2x#i@B#)Z9n%WVIW3e^jI8!4>qr(`$23jBMy|AE{n8I}GBf z7DKYzZFSPtUSFwf4!gNlDNI*$*cEV`GAH`jTN;|v&H=GHCv3c>^aU|+a^|vY;COOD zyByvUN5Z$1ypUa=+UK_G;&@MPdkI9I!gg(1na3`Nn1>7_> z5amkQy&yJM!YGO=1vd_0tuf3u2dGwMy9&JxvRmP}c4_;h_W@M{hZDr5;V*VH#{&i1 zK#WgSyDH@>iwWZZZ7pm61<|aU9Za{&*$r?UP~JWW@v6N238GjHyC>DJh)Ov?Q!CnS zAmCD82)L1#W>rEDVqZ!_ysl)ohJYJGA>ct-@~L7s`@@aMD(G(P#tH~{(4LZ3v#a75 zZVZEf2ZbqNb@YV;)TO$89s+KRfPe>S=z0y?pcxmeSoEZ&U594X#6bH>duw7OaNjFH zWow})e5KZ{(7#U9vYX-9zc#A#E46HG*QDfiFv!2sqt@thTk2p^`$|1S?FQJS)$WgD z-!^tDs#p(o^_8~PL-t>3Xnp$(M5SQ+3Ph>~_6~@%4KM`0QmcmcC5Rav>@KvRk-gIE z>)z6h?HFG1zpBc&3&xE$rDSJEFPDVoW=Fgyq2gpl%%CS90KL- z1A(Geq|~i(KH~C@QVOl@X%HyoItUbSBLvD8OsU)2b#UCNt-T8(T|0YsJkBC8FZ>Rn zj1BDURNUTIfQq!oJjsPFhNY{}gSq6DD02r45ib5~2$Xybd3CZw|NqjjhYAYbjTUyc z+j)K6W7s~b#3=b1+TH~d0hgX2Q1F`cs4F9sz7G~xU*65m2XVQZ?W;qn$g8{E$NRXd z=j1i9roy7P4d`-rOpM3rQ+G5wUriUM$vtppKTdV~VPZ?x(;kOovmd&(PcNL6@ZwB= zyB;0yg<11B`SiAzL2T@eIpH`p>4Tws9JjX7M~+j;zV`S(GE3hN6DwO@3IU_x5U@7@ z0;U3~=>SwO+iOZO2V&-Bqot|;K%C9kawgg~5Cfjg7s5?xyMvcWbv&424i2*O;`qfN zrM%EZrY&_k@50gCLM;cdye_ zYy<}W7FsyMuJ%WMF_97UkY5-CvQjz1c5Mil9qN5rx%a9cW}xu5g{jSGWPX|^jYjLA zrgx)palqC~(%~_7wK%M7b)e-Va84{f)(+C-MF;A&#P+AfV^Nu>>CISVcAA<`v@6l+ zahRu1)1ir&3v-S~uRKk^CZfwuA8!}>-@NBeu#2G#i>&_Avr*bpG;p@P@PAWw3`2+HB#pIpK1wwQ?R=kF&A}<+KJA)=i%7nM z(ix)5TzifR5oTDvJrM>`dLD|%cS{PB*L+-(apEG>c0SsWm77w61!z20twJ*w;7UWS zf@`eW7niPBZ;Mivg}C}#LRA;qt#wTARjBcMCyLP1h45wx9WX9MxPehX`$j-ip#x{i_ssK(4@uoV2D^ta4Eu#-~5jZV=qOyw_r6YR!7nhovI`y zTZYTOB~*ABrqM094~)?jZ#i0L3l&<9e;nMcrs$-BSa}L(A090Q=*|ihcMIOlL+!U( zi4*e{I=a%H2vKd7?SZ(r29r|s)pih$VYep)l)FJ>Sc87Udbw!x8eAW+a#uVAtG?Dw z&uau5)v%WMxG=*r4c~DdLFIKShR{ei$)pQfZmY6qREB3}y>(GM%e{d>jlv=evNtSm=g~Cpp=`VgboYFjy*} zZf&g53k%DI^r*`!D+TPR&tSEHxQ!UB6%eZhgY^Q&w`8zUK(tm2HVf$5n!#28k1opC zP$t_&MJ-4R&vpu^J&3_>0ct5)XzUeGEDM7J3gG<({dqJr4!eNs=cpSps!o8u2g_0m z89tpY92d}O4ug{dE-zqkT7cgw29W~VuV-*hz{_n6E(q{Fz~B-Ck8(8D2_{!WW!_l^ z*955VB~g?c0@SD908t7&R5m)}>u-QNF5tr6b3;Z+tb&i|2MX+0`i~PwSHSbPBK+av zUk_O2i3`}mGdE zTuH_tjew3ikMshbYw!U=dl5G=C()mVDnG+@zBgb>Ys}NRWQPsU|9GBDtUA8t6#!qA zXZZyIewd2I{-OV>l)oy2LdSBET`yOacDz@>5gJrlo+`%@~BbfZb~A zhKxN8IJ$#?-Gvx*p6=suOy(=Xq^qdhFV3KcfHTDy^cL_%gMI>X=p+LL+|*!*fODnT z#&7{CJerIYa$JKk0>+l*G~)$)EX`n&fP!ThOcl^pgBb#bX&bWz?5xB|=JNRCI$ccU)qGX>eb_7Y!Z>*dD@3{t=L(34><>@Cl~! z?4^Jg8oUvZRJ-aig*cWMokw1&Zy}wz!^2Y1URFnuK+b_JQyVdggC=yumES+3=`lCn-L29_abDp z3;5JC)(sgw^fk%^0ZTQQ>;UxtE1FCb6(`M10Zy7Q0ZtN9V23)9=k3qqeSr(ui$!k8 zh&!00mk6jdl)iUZ%n~);Y80-+xcshe!0=%)!0+Q?z(0&$!eF9>IF*qRLFAWX}SW2vM z#3SU*JSO1+)+}HUA>ha&1}6k$TfyLzfG7>l2yoivoC3F0Lw#GtNiMp;!!32$4Z88y z6yVRO%dO@FH(kK`QEtc>xQ?T53kX`z;I07D;J$#rG$6uKZoaS#)!Dnl#hW`;z zeItXX0@UUb(0DEYw_+7|DImvY2CoH-(%`Lt2mm$yOd;xnbO2jGhCK{E2za8wCjs;J zvc?wy*$*=KCcx(qgC7D8Yw$|}4_ES*Bdih)pXTaXpLvu)bO9B^8N?J&ID$cJ0qRqC z`0g#hX}Nd`@W<-aPOwHo7pSK67>V7WQSlVVClyfO41?qXE@_ZbK-WmtNG)LWMFxHX z_FrO1ko4AN_$&ObS>G4U4_^+7#~l1V`5O$J#69R7`7}OQu zSPWKx_o#C31vFxUOgWDbH)K@)&K8;qI2euZ9W)m((u+Y$0sW&hXf2>i3~&1RRXZpsN72;s7+d3s9Sr0Q3~lMBn%5t-yD+N0ren z39IyTh%WsAH)P~b%F%-asGUn-a)^M>8VnQg8lOr-!y}-CAA^ws;-_ISdZs%6!42$S zmzK#`Q8|^4!FT}&u>%IIP886=pTT4SYJ~%6OcgLZ8-wWrVrFMBQ-H@QeV8K8lp_zd z3b}wg!+bZy8>t|Vg#t3=W0Q*otkPhqfQA9Av0T7=4ORkpbfyIZS!K1T3@pH4t$>^b z8LSs@y%2+q0{lua*eqa-23rNZ)nL1ThuJ-xW|ty*6$WgAi{kBd0hfNi8&azKsoHXZLBb@04H)I$=oarS2Ik1-uytpDjZ7u_F zO~A%V3~mVMRGGnF0@N-uNaDFAWI|OYcLbmJ#TvGNxUCs{5YR`1PXbD` zV~sC7{@AaS?U{TNl|CI9{1A|(BZFT8E_GrMZI)ZM6x|s_7vRw#rhpe3#8!ZJ9`S6x z$Z6s#vQ<@()q_EN7jS1t=!V?rBnrr~nyB*RE@1tXZpirFlg*_TaH&57KMmCRcg_GN zX+=dXY5|k!1w;*E;4i=)!XT4?XG0le5in~wgKPq#Ml;AEV8a*yoPTo($v>7=@(9?Y zK|Td!l~8yV=mIWmK{sS1o5%(V3sAcQLBFT~?>P*LyTD^i(xjwIjFNL%B}l+64ax`@ zjU_c;wVZ%$^BGhSpmq|1MkN997cr5Ve^QNDTqmRxqd~K&_Dqjk*f( zLK(}kMg~Bz3%EKOx*_8?R#8QCV*%IJGiV}UzGnlIW1bozBqyV+52>cu^AX0;| z0@TVP&=@b^`3VLS0X(`W^G`CFEGiXGGngu1(s>5c1*mP7V0ESdwfG3YYz3~UnItuq zQ3aT*zybw2=(%RT3s7Mge+yOcu!=8))t!-Gi2^)3Ig`_J7jO-)bVJ6_%beqC0WG2! ztQGJ;gY^Pt++vN5F7O!nZZp~J665V123rNFeV^dgb^&U~CxD#-20UP}TfpIe80;00 z{RxBp0%mI9IVdFVQ&u@FKp~$RMpSH1ThqdZ~<4x2{&Xkc*pvu1YG&d;EaIa zFAUBKi0S#tg5<3D@ac zH)MQD%+aQRkI5L=0)}70Ngd;w^0JSX_fWH8> zGz~x|0glP63fxwC6fMIVIb6V5_N${@dhNDT@Is9TIR z3JFmAbiqau0cyD$fMNn(X;8ueDF5VAtWru;yn+~%7BE1AvI5i!IIvn?fK%8?3Vcwd zR|}0pqpAzI^wr&v5m26^YtB~ZKTbeZnA8@P-t`&O6;P@XgZc`*S9x5=#tF!yp#r=Z zi!atQ2MBQiXWG;a8P{5HbaMe6TE^h>UrQmiLs_M@fN%X6v=N}z;Xxkl1e6%Spo4&i z8gvp+X%K4|0(^%s=nCM`o+ls5q`Rox7{;KdfC0l9^cFBp51qaOE{g>RHNUfsm>z z7%UQyY88Ve0@kf&uuQ}Ifz`#)E}nte<*h)V5)3^oa9 za)`l}f58z3+XNIn%3z0p*2fs^5-=u$!5#sgUdNg26QZ_yL;D^OaQ-}lLju$`Z_qd* z!0QTwZ~+akGKdhMR!>5b69UwdNdTujLY%=HslWp@yr=)gD(79mqxqs6G8*0H=*t3j z-D7Z7fZ9P0d0ZDj4;b7O(D|Y6|4~BTJzyDX>bp)$ti?JahrO^|u>xqn{|? zF3qI8cHw<$S0sxIR^O!WGT;}fPf4Y7!(pvsuF`D0(w+tP|PD_K~*Lt1SF`*pp*c$ ztQ8z7EnsSG24w{V1~VuxV08lq6$K=3%%HLhJjV18CRJU6^{*II7ofHgM7}izsI3D5 zY70Usqbhttvt*4X3%o@lqYAzo1onQjwsFp~8>JB0i`$7GiP$K+lG zma06|u0Jqxzy+MgAva_cyUfu?1f;#fAY4GSs|+Fp3|G&8Ve*8K=lHf5;FJQdRHoms z;S@lm0_Z66i154%*o%uQgwBY!ob(gKD=y&ZYi@{lpoF;_4#4eE0(hQLR1_tUMuiM zxmCi4HQu>^-Ll+}Q8@uezZc+@h`~nzNfR^pEFd6hEdKxVRY>Jztnyue+BOqz{S>ez z1%uxL9JiulStWz_pZu7THDbAd{fy&=j2V6$9ajT2{=@v4#1oa385txHup<+LL;{Xw zW{^ZctgH-@3CNb6K?(s88l)1?CI>Vy{(Xf+DH3L+5zss*gLDGaexA_CAfQ`*1{np| z1sP-(P_ZzBtOAl3Wssc#kAJfylboV5rVNAJ0@UuG$TzQmT$LCE2)I&}L7)J&4JeWn z6yUT=5e06l4uzdS`TdXLF5#9c>4uDQb=g9YfIamXlo60Vm_a!K!3`Ny5TNpa7nK#j zWE#zAi#*$o+GhH-Qk z0r`e2;4!)h3GlE=4*_b4JDBVxz{#Vp0-sf;L$JRoGzPeUGaclHjMC#cdWZr~m43si z42HX)lSh5&Gr|oTBc^fuC;?|?G8m%(&V6``Y7T?(F5ny|x*?;}JdU0$Ab0_TsRBZl zFqp1^8vnhQGnpwWWmhqnEkJFx3P0xvaJ-nOz$WDuc3V{%3thl&Ep|hO|2~dhDq!#d z2FpD{Iv!%OQo#PB3|0$Ba-6|h0n<+~ST7*?DFzz_q&&l5vw*;}47R$!V23G`}c*fwG zfQQc++z{~N1%tl?q{i}t$mW&+wH_hB9Ra$>c#Qjs{7`*p;yYG(vM%*safQ$c_G{ z0Iwsc_A^8tKV883zul1W%8T!sdSPWX=&SK}I1ZB-ngBHMW)MrjQw`z>7#fc?;tEg; zAtKXw0`4VbkU+q|L<|xMn3w_p=iekkvZiE}WCC_*kU~IJU)D$^AV(Soz5>=`0Yx~H zMu1Zt=@rQn9L2#t&k)cKbyD6_<0%E<9q1tbb&kX=B5k_>VRh%Cz>w}1?l8RQk9 zcKSu80RkK^JOvcN6{XzHF6;t!tEd|?w%2A0#RWVKW>8YVyT%NH1gJfNk!cwPuBj~i zuuCvNc^7mlrr!Uo=mw2StvJ52fC^n0R8`=Hvd|b?2qQ@i7qFAH+>jexR{?iHNTna_ z0@iQnhK$D^>_Lq9#zNG#!vIYb*sF3(vV*Vxn=63-lDIE8jWt@ifK7(FAtTW^j&7^K zA*EjrJ8!~b2N$q@CntpY$BFNv2>wLrtvZpQy9+o$Pd9|O4cK090WIe<=qupCLI(W> z_^)9wP(bK527~9S`%j!{%Y#gYDwWO3i)j`Ej|vxUWOym9phgQWucMQ4-C1uTfeV5I{1uZ)!o;xky|0?uQd8!`qY z=I9Lq0#h;A#MR3d7G+?vMN|f4Vz5m>w(Jac2w0Mn!7c$c^Dx+>z+vT<^G3mb7qD9g zRS18-@#ay0hZ7ue3CD-KA!B+0j*bvewlsqi0xni%a7w`Y>I}{Z$We#ESplg-7@TLo z|35Z0VRBJa_BUs6SwNyz46X|3ufcTzw_CHuO#$yi8AJ(~*p|U<0crtJHUHcdqIS6k zxG!Liek0?N0+&>cy~h^V(D=s%Twzb$kWp(OM?V+f=)Y3nt}>7xJLJN^TL(D*KTL@) zFpT4E1@I>wuPy5YA6>vXes)7{^fv`?cLU#aVv}8D@Y4mX|Jw~2r^a!#7gknO`j~&z zR@_JsLlL!JsnHLcaRbD50bB5PL&n!R9PJ~Z)nW$m1*na>VIZLb|GkHj#06|InF@J0 z{s*@5MuHTI%u+6F+s+`h3pjzF8*-!5De&J(+TR7NpUDjwyLPgHuT+-Fcm02X0Y^;BL!tiKsl5`e{6l}1$o6E&zIU_9q*)Yd@Fe~zQ|L`A!1G!URY z>zBi5EXeV#sQ|~f76KgKS_^P|YbyZ0@%-OGkr%4Y<_+*x?srxIck`kd+1_$_y1IZX zvAY{G`oG}lUIL!IV$fH>=hqAd2q^J}!C(zk`P1t>hKY)k=?DQ%HlqbN*^Cq5WHV8K zlg$(XPA=0Gz#Uj#GFIRJbqSX(%nhj}WmR2}0{c{g8Q&Q!Z~^OMziTJ@xW&;+1z<5* zWnhJXCLbBB7BKR&p8wYgIrNQHHVT-aGu=KZAHiNwi98(&X zhjFVO5E~;ub1sKTz5l{xbMg%rBkz~95v)IiEyR_Pc}dyGDHm|*&$uB2>((kS&I!03 zm-R0S!1}dHQ&C4bEUA_e8_7`cMI`j@Jv=@y7~m zQ%Tn5Yt6LMVUC6deD`jW;e}r4cy#R)B9>29|&y=@@(vuvF*qS%8!2HvvvA zKLzMqJVvy6uA@#iF$6f-#8!YCa_I!lH?9JURoVPYGKlX2E?Yu3WHiac(Mbdh&c`6R z01wv9RfSC@WMF;} z0RfI@g#|dC6%*iiR#JfDS!n@|XXO;YZF{_bldBFpU(qF8g_YfqF*S&zs|mPQmO)Jc zJIXPrBj9cY2K5Dm)Mn67z}FfKLguOQr`#~=YL#Z9l1aPOQh?)Ur~=1Tk`_VS(c6iJ z<5@=mj%S7d$Fpt%LOeR(o-0HdkPEoNhPWZ)L_O9YE?`dx zgOLK3H(@YF0T1VaWr9^6<3(k28wQgEEYq1z6>y>>Ys?Vvp$miA0$yt{SHS4*tTA5z zRuty*-y$JSVV4RB(WPG@z^5M@SuG&C2I~Z*)2?n5;G9&q2ykj}JAg-5vQtO9M8&D2 zy#kynIv~KQqQe55Dhe0iRMBw-c=EhAmwi7a8n3%}m_&+7qxlTZiwf#UHO(afPK{g@ z;MB+s0ZxrX32{hkwRDR)8nIUK2UNQx~xQb2ntP9?a3N z1S}cK;H?1X6le+PF`P9#AA~rQ>t_MUN3+T|0mH^H_$h$&d>n1Q>t|=}XAA+3N$9D*pGGEFdkqfx6N!*Zu6_u5KaskdcF_nP7^-0oCz_2BpB%J`K zC-~1-*WcVXoQBUVDvn#(1oYEJatf%T{mdi4aXdhP<46Gkjw6KycpR(66gjE-Mwv_O zYe^Sy>4V&mF<=cxmlY7WoIwQvc~&r}EMSEO)dXP4W>teV0X#a>j+)dFm2w+drM`gQ zYZ){YFn%3_5CKIuFlZ*AxVF(!!1}GM5h@_TCO!Vz2^qbKRXPgzs(~S3{T9~fCSbX? z+EYNv-K^0^0G51K;UfMK44OlAtfqS4Aon1Er28IXXyM;R;-a6^N|0vhUUmI)YnoRh2+ z@Lg-H;r`DRa3O+K){Dv@oo17OqPk;l6|nInC)pt&$yo-w1qA3M`vf?h`=9`i)1QwB zar*Ny0ZxBDA;9U+rv*6u`K$n^J6{msbmz+goW{HMZ~Hp^`KG8i{rQ#vr$65n;PmGQ z0ut!X`nLe5qdgJebm!+T@EA@CbNkIQ`iY;B?jx0-V12S%A|OzX@=<;!goi zSM)?%;7&YFbH)(hbj8>LoURyGfYTM@3vjw(A^}cUOe(rZ;q}h z;MhM5>IjJQj6rN@!oUzP=qrP60=|9K^M6kvf9uls5#Urue*sPf3=-f}z)%5B1$YEF6);MGQvqWa zsQI63-Kl^HqT*D*WC2bEOcUT#z)S&71%wH3Du4ty6|g|SH;=B8#X_74Sf;=M)rVI2 zaUWXg0`3p1-H_4HhojdC$oGpYd82@DUaYZ2K!UgowkzP_JZfnpyF>+RovXIoD`2+< z2Lwz=&KidW=J` zl{Fe97LX+uYa|npR)drR>g8q)Ujb_6a}+kMfSej+5U{UkT)zL6NyvskPLox@3Jr1y zQ0u_MYHk59HOMERb79s96tE51AW0zs$uuacfQM^+ohBtjrE@V(6C?mj#VaFa1vm{= zL4eZ|l?6C0QB8o;5;X;&CAk0B5#qE&eF08OG!)>pM2G;VC7KCvTB4-@rzJuKI4#i* zz@xjJY6=vuqo_D7VF++qqMHDxC3*^QTB45trzQFea9U!JfMi8={~s#EX$g-2rzJ)y za9$17XSyZEx`0Q>csJxmPf~zy6JbSrRkW!tVEyTC$m8Bdn&lE>RXI*DN5Hi54CV=F zSDnE^0S~G&SR$ag)>tm!u?DLIMAYCUp0z^aYezQ-a6I2E!0~LG0LQbP0vylw2yi^x zFTnBakOOf3b6h(rDvoOr0vyjyDu8!`@s}xtoj>COuG6z_$T(Stqb~^9U7x{a0q+|# zxVBJTf3Zm{y|0?-rl=g!;Ff>_I?Y`HGc|Z1;FSh{3plL(d?LVc{J8*+kN3 z8Ap2yaQuuX!0Nyn2?e~+Ac=r6Jy;{TfW%$(_)8^Zs8;b4@L0Q*PJrX5zXDu8c@DGH z%nB?~6&ALSK{iR^aZ- z`NEM91w33fC*Nj@oKl6&F@;rHx_}GY+6@`&CUJCI0p}+(=pevf8^{BpE&iIw8eK#~ zXW=or3vsOW65v?vs{lV+`mP^}4R8UwHOLLQPvV9OaGu0*Cuak*=dl-~M8o4et{N*O zr_N)70Ov{EWC4yB(*!tHX9{p^gekyX5}P5YDkm4Pn=y+_2pyC&~@dmADuvEYz zZDECgEZbOPwSbK~7_1X;bQgn-0-S-dMZl_kdi-ryga=dLaZa;KRGe(~3UIPHAi&Ay zu-Is+C#-N6aA70dkl~EilMML!&&l+Rs5n#JIRRNuvs)Jh#5%*^ih#H})9V5nY4Dc- z$F17}l+oO%^?O1bs}BV@Rv$BFrkqs1>R=4yd2 zrc~T)EuyseOAAYj9a`Md;*l0-wNOh2BDt+94DzuSe`~Q{3w-6G;+|=NkA+kSpW`U; zL<_aV6GCBHs5Olc`l&;^v{;}8KCDu4N43D0St_($y|4;-TL-^r@j;7?T0GEVl@_nH zxTD2EEtYCAUklPgeTDRTI#i(1^&LVdabh>rz4N`BM9&swNA>;JE-bB~LvOyjuY zC^8MmMF!(Ocu65J6#RIlGz&joGAMOTSHU*CyjzB=lcwtjv@P3hOxOWmyXeRA)>>JT zdQ4G7@wzFPmsQe4P0KPvkH|gt_nc>5oavwE`JUf-&&-+YnfIOd{XGyja-!+z*rFN5 zNHl{Ni6-VE?c_w$J+VboGLg=5qUn;@TFr@OE@G>k6HN%j7R{#1Mc%=idpRBBMAM_N zk0v=GwQ`~vN!X%kMMyN)2Z?6*AkoAfB${l4^bseT^nop!Ab~_v1CZ$deIBq7EmvsN;b|ziE-^rz#Tt6hfj) zA`)F2km!nlL|>p&3oXVM7B;ua6;^#XDy>#n^;Fa__4m^USoZp~YqzqnH!5l7`RTE8 zj-PI^eC?^lTXuVH#)J&aOzM*}XJ#QjhdVs?MB7qQ9_%Xd+T}V?V)4TPnz#CRsiBs7 ztLfZ!mzOFl{dGSJrhGm)+h|+=q|6j`F8ltlYrunZ+3B@Q&hCs`I_%aC$$|jAn_Nd) zxtHcDDKpOQh_|}`prIc1Ql)>_4Tg+4rp5{K&ARp-Fw}#dY99;cojz#P(>3gX4?Q=> zz0}|V>VX~d(&dyuyh3H3T6dWrs9OV0xHWp49#Sk)wst784#q$ zx=tQ8POHLGO|*RDxrs(cEb`SL-HL)zi-YvkPV`R8cP7ZCL3$6@<)fBh+-{Zo5PJL3 zeGZ?zuRGn}S*6RlU2uOj$Bg^C@(1-BL&XGI6_VC zJx3S4_QpXX*bS5AfT#9Z=HWh)){y_bgfdw+E|XFT`dVVy8e6%&3v z9)MgIf>XWhrMYtWLEUhxpU6`-Jxq46=^-d1MeM3RW!%<}o-M*dk{&$7pSb>4Y1cfpo|YdxH^XIEsNMyYpAtj$UY6VLU2A1os2(L3h2p$__0nAHE*NLk;Hk!0 zYCJdX!ELVs=3g66gmHs*`E*x32(_DVpE%@WTW z&FZ|HO$)nf$Z#ifAD!{AZkJCyj88(Xrxs_q@40CY?x$Y4-@J6WHvOvcnQQcxJB8`~ zxzDY~Ppf$pPE(jpVJ3wM6kefFNZ}U>1r*+)@Dhb}6pAUFpfHNUPZai0SWn?9gW=OJM|s!4&>Zp^QQrh1V!dqOgp@Fbbbh z*hJwng?A|&r!a-WGzzaHj1d1Jyg*?Gg%k>JQh1xfISKb^sG>s@4MCqM;?-`k$P_*x9xxeIvLkYr(K>}c9-^lB8NrmRFXa`T95Ui zK7cb)PDe2_+z|cHcJ`3d*Q2~_MYJ9rN|*U1e3?h0%RJH=X^zpUs(5;gKEjW#@=I`G z?Y#jN;WPa7aQStN9uz_q_~@)syiFdL~xy|0JF2l0t_waL$YKa%Rn*voLeP?3ZR{ z&!1OlT;;KsCnwTR68b;p>VL%}ie zp_VsGZ~1m)5w+hn6v3e5;}qRQg)r8heqK?+Bt^+62F81Lq6(NOT3$Zno>o4j_F8Z@ z{X`e5QDKXVb1g$@EvBDQQ;R8OgQ8qi(PFK`Hbu#(o5fm8xuRke%3>`M6|%%;&F|UT z84-Te511$3sA|`4exbZ#=^vrJCu(OnrclQ~biRXN(CQ{d)7qmplA-PHpy&jOMzMC< zS5d7+(JZT?GJhg%a&_(CQMuHG5Q|aWfU6qRqpAT@9?BUoT|pfKrWL3xz*LEn0!$yE zmH<;SY6dX*7b&`qvH`4JM0EhB^J^5{{g_U^SnC>pf7Z~{D|9N4!%3elzQ=_J5TQ-XtNW;59Cg}6{Ajc?C?7t@Oc%x~DteYkvwu~0 zCgBn4-HE=_p%|10dtoMLwwoMc+C;fFzrN3^Lvcac3)OXtZ8qu!il-K#g+U>25otGT z>pG>`4ZGWoPACRRspuHYXQ63`}BeXco*3#Wrp2BMol{TB~T^S1=kBRa)Nx4bNr9-y>z)NGJw5VbsSo5e|I@IYi3{ zx(_-1;=pcN#j1w$0XFKLiw~!vg+Ytpv?o5*N;WlAc1xx%yQqMF7dwAd!?=s77>->` zZ&WL?+#u3Qk2S1bkwM*i@fqx|*lcT(rGuLq9#S;VPthKKMRx)enS&L{5F+i9KQ$g0 zluhFW;+1eH29-xBx|gKrbiSf`TvNE+6kLdy_O;Qgc#T;zV@$#zhnN@E0q=&7oWsLp zUQl`$t*M}KMxbpE-Rs$7Xk}0sI>%y<_S(mdeLoGQF$=L8Uux{d(jFf)khb@krpm4+ zslA)sG+}V+8EQm5b9OSR!T<@=ttLf<7#d)$i%&-;hls)}IbSZV`^_fv?u@r-qt`Vp z8KFCduvBON&#whp3Ed*(p%lFp_&?Z1`H zDIr!GL=sowwjge34_$9=>7KlilsT|ZVB>{(@J(P^0lx&MDH{}Bfz|f0n?UpMSb92z*-V)3z){huYf55J_Sr$VNk#{8Ab$5 zVQ?T|I)5ft;i|KWieNv$yBETDfa$OB8(^w}(SV!aFu-K3R`fhP1z3xRcL0;=2Sx3! zD;fgZ0AufC;(HhdaKlD81u%vFtSA{)0IZc5qz%byDeaNmOwZb8tm$VrfQPVlpQ)ye zO0j&OwR|kw7tLDY#+JV&=hEn&h{YOtUVy)~jh0)K*56OjQLL2bcKxs--hD(H{fb5L ztS!RYcc$mNDl&yDDvVI{d!#zpA-1&lF@Et?WQQ*wd7(ucdExgTW+v09qlm*Ic3%HJ zc8H>DShmjEpfp9kqlvU>!&@&s5kmu&qTP5X23aO4ikeKMOxqFGrnkRS zRE)LEtUY{-sC2-;OeXW|F*MF9ZnZ!%Xr;N6n@0N*mA+tZZ*t5?-9gR9U?DLtLEago zr~oU6SsR8W!%VxdR+wo9772?TrB>12bo+x0eA(l-!V{1T${MBUtucx=rz=Xs(p_%% z#AHQXrz(0plc;pGkJ+?6+Eh{6+QV!r>1DcDS~3<&yp;xnMHE(>if>C7FEE?lA8ez6 zVevZFkMhdXNmw{4)|JK=n@tfRre8|a4x3F2lB_gREdGrpn?fr+df#jc8Q9|zNyD)Q YlUHw!#NtbX9Kv$A6J9BY)rCPtgLGA1ko0L&*9MF0Q* diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index 26fb434c16e01d6109f6a826b482ebd482a68c57..1e6af20c0b888c6f7495488352426d6b91f3611e 100644 GIT binary patch delta 63 zcmV~$!4be92n4{PqR5O0mg5n4<)4CVAjR;rne5h~I`nN~e(#91cBQZa!vfudXCzcq Qd{}mwT^SHxzS=H&e$U<&DgXcg delta 63 zcmca|oAJtR#tn-Z4GYYYixbmL\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, @@ -2472,10 +2472,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:06.340715Z", - "iopub.status.busy": "2024-09-26T14:51:06.340434Z", - "iopub.status.idle": "2024-09-26T14:51:06.519948Z", - "shell.execute_reply": "2024-09-26T14:51:06.519447Z" + "iopub.execute_input": "2024-09-26T16:46:44.842097Z", + "iopub.status.busy": "2024-09-26T16:46:44.841647Z", + "iopub.status.idle": "2024-09-26T16:46:45.015713Z", + "shell.execute_reply": "2024-09-26T16:46:45.015126Z" } }, "outputs": [ @@ -2515,10 +2515,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:06.522000Z", - "iopub.status.busy": "2024-09-26T14:51:06.521573Z", - "iopub.status.idle": "2024-09-26T14:51:06.526439Z", - "shell.execute_reply": "2024-09-26T14:51:06.525860Z" + "iopub.execute_input": "2024-09-26T16:46:45.017578Z", + "iopub.status.busy": "2024-09-26T16:46:45.017162Z", + "iopub.status.idle": "2024-09-26T16:46:45.021861Z", + "shell.execute_reply": "2024-09-26T16:46:45.021311Z" }, "nbsphinx": "hidden" }, @@ -2555,46 +2555,84 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01a52525efc245dbbd179fb0a46c9b73": { - "model_module": "@jupyter-widgets/controls", + "0615152255504030adeb967c707e7eca": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "047ffcbda41946e19a08ad3b61bf841f": { + "06b16b9d750441d08a55ba65990da75c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_39045bc38857424e8dd68bde536fe596", - "placeholder": "​", - "style": "IPY_MODEL_edbabb45d56b40b7a43858d7ec51dcae", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4e064a950004422e9793c7aa70deeb69", + "IPY_MODEL_0d9825bba69d4c9a90b0f711f057aa91", + "IPY_MODEL_e6eeb0f18d5342c7bfb2162167342fdb" + ], + "layout": "IPY_MODEL_20a37aa330ef40879ccd861ffeb2d77e", "tabbable": null, - "tooltip": null, - "value": "100%" + "tooltip": null } }, - "0944754e93274569967afe6d608ae5bd": { + "07f0cc4d1f4c48e99b208c9766cdd9dd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2612,33 +2650,48 @@ "text_color": null } }, - "0ec72a2a0065496b8b3b6ff239cb643d": { + "08583d3e3a0c448fb3615b125f05ee1e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0bed948828f141bca1ebc19b7c3ee31a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_22cbf5fe8ee943da90f0599eabdc47e9", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_1fd121ab479e40598a964ef1fe640df7", + "layout": "IPY_MODEL_d7f8a17831a8490385be98caaf69cc9e", + "placeholder": "​", + "style": "IPY_MODEL_6c87c46fe33948dd8347a75315145f72", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "100%" } }, - "0fc0d5f5c5fd4be8b001f46a67af29e6": { + "0cc4d92fb4044144b93e36fc005c9b57": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2691,7 +2744,25 @@ "width": null } }, - "0fefa1fafea24c19b8f8733109f29d90": { + "0d7259ccb2014d6e89b9d45abcaffdb4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0d9825bba69d4c9a90b0f711f057aa91": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2707,53 +2778,41 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_be38efba8d3b4a918b3d7486ef513b34", - "max": 2.0, + "layout": "IPY_MODEL_e13ece2f21e645f39a334fc1327f6255", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_2a6efe50b16d4bb4b79ecb580262a865", + "style": "IPY_MODEL_653426bf8e1847bf9f5498114910616f", "tabbable": null, "tooltip": null, - "value": 2.0 - } - }, - "10f1631d161c449eb8c661cb78376e34": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": 60000.0 } }, - "122c68d343034072a896523a1a6f59da": { + "0e3bcece048b4a29932526acdaec4e78": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4b07743ac3674137b27e138913647bd4", + "IPY_MODEL_8498411df0f144e2bef7bcba62fe928e", + "IPY_MODEL_17c264c8f3b9497392f244af3798dedf" + ], + "layout": "IPY_MODEL_3b027b03623b43ac92f91ec0db28cd09", + "tabbable": null, + "tooltip": null } }, - "141a122f5b984ea4b8d354438a45f4ac": { + "0e7b785e782d4294ab4157d8d7f8ed83": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2806,31 +2865,7 @@ "width": null } }, - "157b0b5de92c4dd39861100e0048c0b7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_46affc3b26cb4f05ba2a6b4d66e4a233", - "IPY_MODEL_e504d10254784e0fb8866b5539c1da25", - "IPY_MODEL_5e794a3967d7453396cb620ce1a5277f" - ], - "layout": "IPY_MODEL_b707e815ff5841ea8e23fc04fa7ff454", - "tabbable": null, - "tooltip": null - } - }, - "1af334042da749e9a2368c61aae4462f": { + "0efa586eba474bde8e872c4e1050d5f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2883,23 +2918,30 @@ "width": null } }, - "1af3d41b10be4701a4e58df18c1a38f8": { + "0f00fb4673354831ac246ea7823f56c9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_db5fae1c4ed24b4d84db9673debe1849", + "placeholder": "​", + "style": "IPY_MODEL_73d2026e64ed488c833fda372426273e", + "tabbable": null, + "tooltip": null, + "value": "Computing checksums: 100%" } }, - "1b540d6d72a748d0ac6d30324dc37e51": { + "10a58866c782478991eebcd95239d6d9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2917,30 +2959,33 @@ "text_color": null } }, - "1ee09afeb3644ffaa25df19feb6d7756": { + "13c48dc4b3ea4c4e9f4129d1fc35a62d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a199cd7e7b8c46dea72b37e895857807", - "placeholder": "​", - "style": "IPY_MODEL_d0d8518db2324e00900b4cc97bf83cba", + "layout": "IPY_MODEL_c7140224e2014a9f86018b2a80db9349", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1919595d2502420583ae6188632c1d18", "tabbable": null, "tooltip": null, - "value": " 10000/10000 [00:00<00:00, 246828.30 examples/s]" + "value": 10000.0 } }, - "1f4aff5948cf4d0186015296605c7196": { + "150680861ad44d349907508491bfe1e4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2993,41 +3038,54 @@ "width": null } }, - "1fd121ab479e40598a964ef1fe640df7": { + "152c7e3ade014d50839d1407d6baeb13": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f46fc8a56a0140628ce3b129a42d4d00", + "IPY_MODEL_cc44de893c454e9984744278aef97f45", + "IPY_MODEL_978a26bf6ea54d28bb363924fea9ad68" + ], + "layout": "IPY_MODEL_7b372ec9dbea4c5eab84315544b3186d", + "tabbable": null, + "tooltip": null } }, - "20c8eedf90bb40d392876c954589ff68": { + "156da93ea6724a9891da8967c6848587": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_473ee3df22174639aa2fb5a0bd0f2bf2", + "placeholder": "​", + "style": "IPY_MODEL_bd2b67ddccbc4fdaaea0035ffeb51e52", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:49<00:00, 1124.68it/s]" } }, - "22cbf5fe8ee943da90f0599eabdc47e9": { + "165f0e32eb9940e59f9f946b22aaa1bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3080,7 +3138,7 @@ "width": null } }, - "23438682b36c4a25982847f0236795a6": { + "16b101e9d5204f4983a48d53bfb6ef2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3133,32 +3191,14 @@ "width": null } }, - "26577822b8d84e4f976ede8bcd034275": { - "model_module": "@jupyter-widgets/controls", + "16b329ee1b904b20929be1a410d80ca3": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "27d69304db224bf69131404f9ec805dd": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -3204,41 +3244,48 @@ "width": null } }, - "2a62f69b0c2547d89c82d6582f304d32": { + "17c264c8f3b9497392f244af3798dedf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_16b329ee1b904b20929be1a410d80ca3", + "placeholder": "​", + "style": "IPY_MODEL_76521fcf4499458b8f8f1e422e450d73", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 63.78it/s]" } }, - "2a6efe50b16d4bb4b79ecb580262a865": { + "1916266332094832a5967cb9bf13df10": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2b2f57b523f849bbb599cc612b663ba6": { + "1919595d2502420583ae6188632c1d18": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3254,43 +3301,83 @@ "description_width": "" } }, - "2f87fcdad766443d8c4bca6afb07dd5a": { + "1b69dec79acc4f3993aa0744d8c32ef2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_62e9094b2bb5424fb496d88b57648bc2", + "placeholder": "​", + "style": "IPY_MODEL_60b4c0b99d334f02aad9eb20e84a4cf8", + "tabbable": null, + "tooltip": null, + "value": " 5.18M/5.18M [00:00<00:00, 28.6MB/s]" } }, - "309f00f07977438688cb2e2a138942ee": { - "model_module": "@jupyter-widgets/controls", + "20a37aa330ef40879ccd861ffeb2d77e": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "31aedd70a4c245c99b7449fba431ce39": { + "256a75aad92141fdbbc2bfacc1ec5a66": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3308,7 +3395,7 @@ "text_color": null } }, - "37cb8925a1ba4ade99a9002b6b26e8df": { + "259a364c31bf46d482e138d0d9607b1d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3323,15 +3410,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_86ca6bb1495e4902b7bab635f193c7b5", + "layout": "IPY_MODEL_a67d87241bce4f91bb13e7d23a32a198", "placeholder": "​", - "style": "IPY_MODEL_c8b6019d0f094ef6b63a722264c996a6", + "style": "IPY_MODEL_5a44bdf59d20444eb868f8e2c100e178", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 61.07it/s]" + "value": " 30.9M/30.9M [00:00<00:00, 42.0MB/s]" } }, - "3814212d72544fd898cb1f84a4da144b": { + "2675edd16bff40b0b42c8daeff5f5bf0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3347,33 +3434,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a7d9118a0a624247afaec6d0c70354df", - "max": 9015.0, + "layout": "IPY_MODEL_38c3afc197464cc09af95bd9849771c3", + "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_01a52525efc245dbbd179fb0a46c9b73", + "style": "IPY_MODEL_c626e7c1959e4543bf621cd4e48a04b5", "tabbable": null, "tooltip": null, - "value": 9015.0 - } - }, - "38e12e1caf544c0db7e643ca4333c637": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "value": 40.0 } }, - "39045bc38857424e8dd68bde536fe596": { + "2b3b3a12683342b7b1f3e4a7c16ff834": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3426,7 +3497,98 @@ "width": null } }, - "40c57ff589b84713af0069686d77f3b8": { + "304c8861034b44bbba4a12de67849cfb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3260b35b84244fe185eec3555f28011b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_510509e050314274aae1fd611e9e0055", + "placeholder": "​", + "style": "IPY_MODEL_fc0dbc5ad67f44d1970ac151d319ba0e", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 61.99it/s]" + } + }, + "330d908dc536412297733ff7acf50107": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_9f02a1dd3c664bfba8e685b4d2f82b34", + "IPY_MODEL_ddd88fe1f1a14aeabe63b7876fcebfbe", + "IPY_MODEL_d40d4080e5904c30908beea6c18b3f84" + ], + "layout": "IPY_MODEL_d3e45ec46ce344f091c4ff564f4fc634", + "tabbable": null, + "tooltip": null + } + }, + "33b768a7f4294ffeab6f702ca4b71d4c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d688f38225b4426ca69152695ceda860", + "max": 30931277.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_bf698e9ccdfc47d5a94fb2de5f578593", + "tabbable": null, + "tooltip": null, + "value": 30931277.0 + } + }, + "388bd680a9a044b6b3414838cfd94a55": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3479,7 +3641,7 @@ "width": null } }, - "41752bd19eff4ad7bd7f964db91e4397": { + "38c3afc197464cc09af95bd9849771c3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3532,7 +3694,7 @@ "width": null } }, - "43636151bcde46648bb7aaa89ccb8a51": { + "3908248ea2ec46ac809cab2a57825a21": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3585,30 +3747,63 @@ "width": null } }, - "438f1faf1add4ca0bf523ed0c6de324e": { + "39ecdb0609d94bfcb77249dccd8e7b0a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3a01430bac104582a0152f66469ff522": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3a77faf9cb2646f79de794a3636a0bdf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7a0e1f1ff0db4d01b7ee26a773a3e838", - "placeholder": "​", - "style": "IPY_MODEL_122c68d343034072a896523a1a6f59da", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a511b79056d34849a3da28979d986687", + "IPY_MODEL_5f213eb6e62d4ae091344ea7807998c5", + "IPY_MODEL_156da93ea6724a9891da8967c6848587" + ], + "layout": "IPY_MODEL_c3bd4b3cde4a416e8291af2ebeb6721e", "tabbable": null, - "tooltip": null, - "value": "Downloading readme: 100%" + "tooltip": null } }, - "442d008a589d43729e86f45feea617a0": { + "3abf54c4e5704bc5aef6294d9a245943": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3661,7 +3856,7 @@ "width": null } }, - "44350a751399489a9b7ac94b4aa0544e": { + "3b027b03623b43ac92f91ec0db28cd09": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3714,97 +3909,7 @@ "width": null } }, - "4472b003fd7d40f4b054d6725929c2e0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6c8fe2efdded46d4846420593a00bcd9", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_acc0b8fed3ec45c2b61e4cbdd87c0d3f", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "466806dd912b4f9da060546451df881b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5171002b8b9b40e58bc90a21fd7d4fce", - "placeholder": "​", - "style": "IPY_MODEL_a1491d109915441ca6418a110cd3b606", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "4685982b10924f9aaf548d58e65e7fff": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "46affc3b26cb4f05ba2a6b4d66e4a233": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1af334042da749e9a2368c61aae4462f", - "placeholder": "​", - "style": "IPY_MODEL_8fbebe50e8c94c68b5af42bcaa77c458", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "46d058b0dc32483b92ffc747203adfbb": { + "3f1f383ad1b34764bca27b83cec7251c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3822,71 +3927,7 @@ "text_color": null } }, - "48ba164541954d109266e55290018b2b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_cb02675b1ffa460abaf2766e247e9e76", - "IPY_MODEL_f5bdc90f13ac4085a6c87c3f16c1db23", - "IPY_MODEL_8e5227fef7bb4af398c777c479c081d2" - ], - "layout": "IPY_MODEL_442d008a589d43729e86f45feea617a0", - "tabbable": null, - "tooltip": null - } - }, - "49a7ec98f7764b44b81eb30169e07a30": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_cf2c49458b23448bbd90dd22355dc406", - "IPY_MODEL_0fefa1fafea24c19b8f8733109f29d90", - "IPY_MODEL_e1863acf55ff485abde9e3dd42efb403" - ], - "layout": "IPY_MODEL_6f45d2ba1fe14a26b1dd8d8cde2b3404", - "tabbable": null, - "tooltip": null - } - }, - "49ff26f36c4046ccaa23af5a0e1bd79a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4afd56e1be684a4ea49cf9bd94feb822": { + "43015204ba52471381708cc6a3897af0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3939,74 +3980,7 @@ "width": null } }, - "4c60fd9ec0a14e40806e9a0ac5d515de": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4d78c4c4290346e58aeff13d1e2863f2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e1f00c2ba7fa42879c2ae82deb9ae36b", - "placeholder": "​", - "style": "IPY_MODEL_10f1631d161c449eb8c661cb78376e34", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.27it/s]" - } - }, - "4e00ed69c8994d9990e59ec728ec2a31": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e5d17f8bb48c489d84f85f8b0e82285e", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2b2f57b523f849bbb599cc612b663ba6", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "4e60241fc8fc4199a601cbd4c617c5e6": { + "4334d6adca6d419fa7e126021ea11a03": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4059,30 +4033,7 @@ "width": null } }, - "4f30a386d77a45318659e949f29dd3b0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b1371d623aa5489ca6e8e84cd59f53d7", - "placeholder": "​", - "style": "IPY_MODEL_31aedd70a4c245c99b7449fba431ce39", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "5171002b8b9b40e58bc90a21fd7d4fce": { + "473ee3df22174639aa2fb5a0bd0f2bf2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4132,349 +4083,10 @@ "right": null, "top": null, "visibility": null, - "width": null - } - }, - "517b83c613bb49c9ab0cd319caf77fa4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_438f1faf1add4ca0bf523ed0c6de324e", - "IPY_MODEL_3814212d72544fd898cb1f84a4da144b", - "IPY_MODEL_bf819d4aebc746d595ddad41e330f6dd" - ], - "layout": "IPY_MODEL_959554efc55740f0a1054b9bb02b35ab", - "tabbable": null, - "tooltip": null - } - }, - "52f3a03aa4994380b8b138695e6993e3": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "5407b52abb3543878bac58e171136ce5": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "545ab14d8bdc4758954170fd6a6c7f2e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d16d5292876e41bbb948c7172eb1b022", - "placeholder": "​", - "style": "IPY_MODEL_9d21835857024eaca20e6c75bc8f65eb", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "54d86e58cead41e6ba4d5f91406ed1b6": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "554fe55159064f65be3faeef3e587efa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_44350a751399489a9b7ac94b4aa0544e", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_68ce90ceb8c5418fa6b01cf5ea89177d", - "tabbable": null, - "tooltip": null, - "value": 10000.0 - } - }, - "5568c1b377ee44e2baadcd2713472be4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "5a0315cb61bf4e1b9c52127d29ddf405": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fd18df91f07f4dd2951fcd6f5d643d2e", - "placeholder": "​", - "style": "IPY_MODEL_2a62f69b0c2547d89c82d6582f304d32", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 64.27it/s]" - } - }, - "5a31bc5cecd243f3b3fc5adfc57abc50": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "5e114e4103d94679910f2192af574f94": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c2007b76905c425eb8c5793d5064dd23", - "IPY_MODEL_eb1eb741913649deae955849ad2ff5e8", - "IPY_MODEL_8bf2df37d8174d2faa0d068cc68a2fef" - ], - "layout": "IPY_MODEL_dd4c58e0fd6948c49326bb0bb920e8c2", - "tabbable": null, - "tooltip": null - } - }, - "5e37b90b12544b039698dff9bf1b1167": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b832eb74e64e4a219cb1a39e71503899", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6cad31ec6e694b91b3e194e785d6cc13", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "width": null } }, - "5e794a3967d7453396cb620ce1a5277f": { + "4b07743ac3674137b27e138913647bd4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4489,33 +4101,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_aee54ae8933b42d09ef6de4b7c8e9cee", + "layout": "IPY_MODEL_165f0e32eb9940e59f9f946b22aaa1bf", "placeholder": "​", - "style": "IPY_MODEL_919e7f7fa04b447e977f5978a6008575", + "style": "IPY_MODEL_8c60825feef64aa4a292fcb73efacdd9", "tabbable": null, "tooltip": null, - "value": " 30.9M/30.9M [00:00<00:00, 48.5MB/s]" - } - }, - "5f57350bd59941d3b3be376c1389de24": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": "100%" } }, - "60160531292f49f6912a5e7fa5c1cd4a": { + "4c5c9bab82cf4d7bb1787ca880626761": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4530,94 +4124,86 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_047ffcbda41946e19a08ad3b61bf841f", - "IPY_MODEL_4472b003fd7d40f4b054d6725929c2e0", - "IPY_MODEL_bdf9a01c9a4d4a6186410cdd21003fa3" + "IPY_MODEL_c9b78c79f9ec4c21b9443be9011f71b8", + "IPY_MODEL_d994309e4ee24291984f6e27719ed798", + "IPY_MODEL_543ec1550b174116a83c81d026cc25fc" ], - "layout": "IPY_MODEL_27d69304db224bf69131404f9ec805dd", + "layout": "IPY_MODEL_43015204ba52471381708cc6a3897af0", "tabbable": null, "tooltip": null } }, - "60b1a0d2d540411383054bcc09a9902a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "62dcb057ad134472b2f7163715a51417": { + "4e064a950004422e9793c7aa70deeb69": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_23438682b36c4a25982847f0236795a6", - "max": 5175617.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5a31bc5cecd243f3b3fc5adfc57abc50", + "layout": "IPY_MODEL_725cf6b9493c4d97a794d0c679aa4b36", + "placeholder": "​", + "style": "IPY_MODEL_256a75aad92141fdbbc2bfacc1ec5a66", "tabbable": null, "tooltip": null, - "value": 5175617.0 + "value": "Map (num_proc=4): 100%" } }, - "63fd06eb481e40ff939a4af2d7ec11e1": { + "501fbbc332d142e1a890a35622ae19d7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_16b101e9d5204f4983a48d53bfb6ef2f", + "placeholder": "​", + "style": "IPY_MODEL_10a58866c782478991eebcd95239d6d9", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 60.32it/s]" } }, - "68ce90ceb8c5418fa6b01cf5ea89177d": { + "50b5887bf4c344448a941ef696740bfb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ac45d17c69d34562abc6d440b8927476", + "IPY_MODEL_8880ccfb8cfb4b79bd29ebf7d60fe54d", + "IPY_MODEL_d6576c007df04cb69b189749179d99bc" + ], + "layout": "IPY_MODEL_f1d92e4fad884e60ae51f8e53d0a9fe6", + "tabbable": null, + "tooltip": null } }, - "6bad8602531348f08c403c73528818d1": { + "510509e050314274aae1fd611e9e0055": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4670,7 +4256,53 @@ "width": null } }, - "6c8fe2efdded46d4846420593a00bcd9": { + "514ea03743d344d09972d77a32c7fe4e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5513cd1c30484a6f8b2a97e19a651ab0", + "placeholder": "​", + "style": "IPY_MODEL_782fbc8cafdc4589b274f5da20c87d79", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" + } + }, + "543ec1550b174116a83c81d026cc25fc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3abf54c4e5704bc5aef6294d9a245943", + "placeholder": "​", + "style": "IPY_MODEL_7a545c6253c44f2f9055a180a3261ecf", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 67.40it/s]" + } + }, + "5513cd1c30484a6f8b2a97e19a651ab0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4723,23 +4355,31 @@ "width": null } }, - "6cad31ec6e694b91b3e194e785d6cc13": { + "552d50148a6144d18fe7fdebcb4c2a2f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d36f9da2d76f49ca94d84d03416aa07a", + "IPY_MODEL_6eaefb705976491799c97d41d27e2318", + "IPY_MODEL_3260b35b84244fe185eec3555f28011b" + ], + "layout": "IPY_MODEL_5a848363cac841518da6ebb2300325d3", + "tabbable": null, + "tooltip": null } }, - "6f45d2ba1fe14a26b1dd8d8cde2b3404": { + "556e4ef19cb94604aa405d62dbf7da70": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4792,7 +4432,7 @@ "width": null } }, - "77e4c9ec41b8452c8963af0b77b5555f": { + "56a181d584f247a798b2fa5b261b7f49": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4807,92 +4447,52 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_8838da60b00243caa8a1c207f1f8be22", - "IPY_MODEL_b934aa90c70b4cfdae426a9ca50e61a4", - "IPY_MODEL_793b99f043774f62a311dedf0c93aba0" + "IPY_MODEL_a53cf182147b41ca9bc22e924e74e42c", + "IPY_MODEL_2675edd16bff40b0b42c8daeff5f5bf0", + "IPY_MODEL_501fbbc332d142e1a890a35622ae19d7" ], - "layout": "IPY_MODEL_83e0553f47d84eca8ffc9341907dd047", + "layout": "IPY_MODEL_91d2a863861b4e0f903b9413d3ed6b39", "tabbable": null, "tooltip": null } }, - "793b99f043774f62a311dedf0c93aba0": { + "5898a17eff104f3b8b3b7c0757f5ae54": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ce14eb0a9f94406f9670f2a1dc1345c7", - "placeholder": "​", - "style": "IPY_MODEL_aafc8b9a1a3a4506b5483ae08647da91", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:00<00:00, 282979.24 examples/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "7a0e1f1ff0db4d01b7ee26a773a3e838": { - "model_module": "@jupyter-widgets/base", + "5a44bdf59d20444eb868f8e2c100e178": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "7de683e49bbf4f65959e300da53047ed": { + "5a848363cac841518da6ebb2300325d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4945,30 +4545,69 @@ "width": null } }, - "7ef0c36f65034c0f9d5b161441b6e47c": { + "5dca4c3a272f4e5b98860d48cd7a7768": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "5f213eb6e62d4ae091344ea7807998c5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c91d42b9d1c342cf8093443f4eaa5204", - "placeholder": "​", - "style": "IPY_MODEL_20c8eedf90bb40d392876c954589ff68", + "layout": "IPY_MODEL_0e7b785e782d4294ab4157d8d7f8ed83", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_876f5638038442208da1fc1fcc89b85e", "tabbable": null, "tooltip": null, - "value": "Generating test split: 100%" + "value": 60000.0 + } + }, + "60b4c0b99d334f02aad9eb20e84a4cf8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "83e0553f47d84eca8ffc9341907dd047": { + "62e9094b2bb5424fb496d88b57648bc2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5021,7 +4660,49 @@ "width": null } }, - "86ca6bb1495e4902b7bab635f193c7b5": { + "6402088307ca4ebeb0e880a6da30aa44": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a961cd5b3a9f46fab76d3b67117504c2", + "max": 5175617.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c775fa235666491088a75b6255f32e3f", + "tabbable": null, + "tooltip": null, + "value": 5175617.0 + } + }, + "653426bf8e1847bf9f5498114910616f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "683dd50849a54efd82f6bcbfedf01e71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5074,123 +4755,25 @@ "width": null } }, - "8838da60b00243caa8a1c207f1f8be22": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e34ca479b1cd4338a1b034c9bdb6cdde", - "placeholder": "​", - "style": "IPY_MODEL_5f57350bd59941d3b3be376c1389de24", - "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" - } - }, - "8bf2df37d8174d2faa0d068cc68a2fef": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9a129d278f5249b2ba66adbc4c7981b4", - "placeholder": "​", - "style": "IPY_MODEL_cbb874850a0f4b8585830f781d02f6c7", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 6394.51 examples/s]" - } - }, - "8c99cd03c2204dd69d220e1911ef407b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4f30a386d77a45318659e949f29dd3b0", - "IPY_MODEL_e0c002bd6e8c4598837703a0b54dfdd7", - "IPY_MODEL_37cb8925a1ba4ade99a9002b6b26e8df" - ], - "layout": "IPY_MODEL_5407b52abb3543878bac58e171136ce5", - "tabbable": null, - "tooltip": null - } - }, - "8e5227fef7bb4af398c777c479c081d2": { + "6bdc6710af8142aba7c58c6798148a76": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_40c57ff589b84713af0069686d77f3b8", - "placeholder": "​", - "style": "IPY_MODEL_1b540d6d72a748d0ac6d30324dc37e51", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:53<00:00, 1198.28it/s]" - } - }, - "8f801276525d4e939b2f6a4ae43acb97": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_dd7812d3359f4747a8d626fa5a9094fb", - "placeholder": "​", - "style": "IPY_MODEL_26577822b8d84e4f976ede8bcd034275", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 59.80it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "8fbebe50e8c94c68b5af42bcaa77c458": { + "6c87c46fe33948dd8347a75315145f72": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5208,25 +4791,33 @@ "text_color": null } }, - "919e7f7fa04b447e977f5978a6008575": { + "6eaefb705976491799c97d41d27e2318": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_683dd50849a54efd82f6bcbfedf01e71", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3a01430bac104582a0152f66469ff522", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "959554efc55740f0a1054b9bb02b35ab": { + "725cf6b9493c4d97a794d0c679aa4b36": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5279,7 +4870,25 @@ "width": null } }, - "9a129d278f5249b2ba66adbc4c7981b4": { + "72e2143985fe46f499ca9310aa86d7cf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "73cad09577cd4329a162f8f43e7f6529": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5332,54 +4941,7 @@ "width": null } }, - "9b43cbe4d995486aa4d260f3e1778a5b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ccdd8d1027864d7697480a9f400c528c", - "placeholder": "​", - "style": "IPY_MODEL_2f87fcdad766443d8c4bca6afb07dd5a", - "tabbable": null, - "tooltip": null, - "value": " 5.18M/5.18M [00:00<00:00, 35.9MB/s]" - } - }, - "9c97151f1d7f4a49a3e2278cddb3c604": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_fb35e5a84e5942e59aa0f8e4a6d4b045", - "IPY_MODEL_0ec72a2a0065496b8b3b6ff239cb643d", - "IPY_MODEL_4d78c4c4290346e58aeff13d1e2863f2" - ], - "layout": "IPY_MODEL_e2ddf40e09984deca4602e0c72e8bfd6", - "tabbable": null, - "tooltip": null - } - }, - "9d21835857024eaca20e6c75bc8f65eb": { + "73d2026e64ed488c833fda372426273e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5397,33 +4959,7 @@ "text_color": null } }, - "9d4ba4757b8d48bba46623ad8f4b39a3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_54d86e58cead41e6ba4d5f91406ed1b6", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_38e12e1caf544c0db7e643ca4333c637", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "a02cbe5a9ae843ff95023851f56408fe": { + "74187c11715545dfbb571e697ad3074b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5476,7 +5012,67 @@ "width": null } }, - "a1491d109915441ca6418a110cd3b606": { + "76521fcf4499458b8f8f1e422e450d73": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "782fbc8cafdc4589b274f5da20c87d79": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "788c289a49054234a89ef72aa47dbb50": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b4ade25b47e1457e9269900f0df62d61", + "IPY_MODEL_6402088307ca4ebeb0e880a6da30aa44", + "IPY_MODEL_1b69dec79acc4f3993aa0744d8c32ef2" + ], + "layout": "IPY_MODEL_0615152255504030adeb967c707e7eca", + "tabbable": null, + "tooltip": null + } + }, + "7a545c6253c44f2f9055a180a3261ecf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5494,7 +5090,7 @@ "text_color": null } }, - "a199cd7e7b8c46dea72b37e895857807": { + "7b372ec9dbea4c5eab84315544b3186d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5547,25 +5143,7 @@ "width": null } }, - "a3d204cf9a5a441d9ce00a62738821f3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a4367ae0bcb046beb0aa7fa55ae59b6d": { + "7fd68800618a439c92872f907d74563e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5618,7 +5196,33 @@ "width": null } }, - "a7d9118a0a624247afaec6d0c70354df": { + "8498411df0f144e2bef7bcba62fe928e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_150680861ad44d349907508491bfe1e4", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_eb036f5b69cb4a91adf36df7f4d466a6", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "84c334be19a8458598d52bab4fff8168": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5671,7 +5275,7 @@ "width": null } }, - "aafc8b9a1a3a4506b5483ae08647da91": { + "858ad6e310a44429a4ccbefd0a93534b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5689,7 +5293,7 @@ "text_color": null } }, - "acc0b8fed3ec45c2b61e4cbdd87c0d3f": { + "876f5638038442208da1fc1fcc89b85e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -5705,7 +5309,33 @@ "description_width": "" } }, - "aee54ae8933b42d09ef6de4b7c8e9cee": { + "8880ccfb8cfb4b79bd29ebf7d60fe54d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a7425ae868e14eafae4bfb0468cec374", + "max": 9015.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_defede3efe8b41b3ba6660edba087f85", + "tabbable": null, + "tooltip": null, + "value": 9015.0 + } + }, + "8957a61b05ae4e12aca0eb2a5e277a0b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5758,30 +5388,25 @@ "width": null } }, - "afdc4d351896438cb7e9760348393ddc": { + "8c60825feef64aa4a292fcb73efacdd9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6bad8602531348f08c403c73528818d1", - "placeholder": "​", - "style": "IPY_MODEL_0944754e93274569967afe6d608ae5bd", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b1371d623aa5489ca6e8e84cd59f53d7": { + "900f06ca89244862b1783effd7d731d6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5834,60 +5459,33 @@ "width": null } }, - "b42c916027f0431eb2b8bccc86357bf7": { - "model_module": "@jupyter-widgets/base", + "909c9effd0a04202a36f69334a1adb92": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_db7b4d566c2c4c74a93bf056d2eed6a7", + "max": 2.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f6c771c77fcc45a4a4178d8748beeb3c", + "tabbable": null, + "tooltip": null, + "value": 2.0 } }, - "b707e815ff5841ea8e23fc04fa7ff454": { + "91d2a863861b4e0f903b9413d3ed6b39": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5940,7 +5538,30 @@ "width": null } }, - "b826bec48c45487bbcae63716ac684fb": { + "9345bef96daa4abfb55fa1601eccb32b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9b26696cd88e4138bbe052cd32cbbd4f", + "placeholder": "​", + "style": "IPY_MODEL_3f1f383ad1b34764bca27b83cec7251c", + "tabbable": null, + "tooltip": null, + "value": " 10000/10000 [00:00<00:00, 246652.67 examples/s]" + } + }, + "959ae504cb01405781572c8c3e5f8f73": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5993,86 +5614,48 @@ "width": null } }, - "b832eb74e64e4a219cb1a39e71503899": { - "model_module": "@jupyter-widgets/base", + "95dff653e0bb4198be1ecfaf08164b44": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b934aa90c70b4cfdae426a9ca50e61a4": { + "978a26bf6ea54d28bb363924fea9ad68": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1f4aff5948cf4d0186015296605c7196", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_bf1593492574460ca63450ff1667241f", + "layout": "IPY_MODEL_adb5aee751224ee8a1f228414df1dd9c", + "placeholder": "​", + "style": "IPY_MODEL_08583d3e3a0c448fb3615b125f05ee1e", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": " 40/40 [00:00<00:00, 63.38it/s]" } }, - "badd7c549d834045856ae809d48d9fa6": { + "981864ffd419481399deca7093834a2a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6125,30 +5708,7 @@ "width": null } }, - "bdf9a01c9a4d4a6186410cdd21003fa3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_41752bd19eff4ad7bd7f964db91e4397", - "placeholder": "​", - "style": "IPY_MODEL_60b1a0d2d540411383054bcc09a9902a", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 58.52it/s]" - } - }, - "be38efba8d3b4a918b3d7486ef513b34": { + "9b26696cd88e4138bbe052cd32cbbd4f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6201,23 +5761,25 @@ "width": null } }, - "bf1593492574460ca63450ff1667241f": { + "9db32a78998341e490d0bfe9165b1cde": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "bf819d4aebc746d595ddad41e330f6dd": { + "9f02a1dd3c664bfba8e685b4d2f82b34": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6232,39 +5794,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d89cbf3e824f490b81fa19d70eb0784a", + "layout": "IPY_MODEL_0efa586eba474bde8e872c4e1050d5f7", "placeholder": "​", - "style": "IPY_MODEL_63fd06eb481e40ff939a4af2d7ec11e1", + "style": "IPY_MODEL_1916266332094832a5967cb9bf13df10", "tabbable": null, "tooltip": null, - "value": " 9.02k/9.02k [00:00<00:00, 960kB/s]" + "value": "Generating train split: 100%" } }, - "c01e10af9cd04c4c90430d0afbaa6da0": { + "a442f3280e014780ac27c505772845cd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_466806dd912b4f9da060546451df881b", - "IPY_MODEL_5e37b90b12544b039698dff9bf1b1167", - "IPY_MODEL_8f801276525d4e939b2f6a4ae43acb97" - ], - "layout": "IPY_MODEL_ca16aad0772747da8b6cf03b6abc3643", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "c2007b76905c425eb8c5793d5064dd23": { + "a511b79056d34849a3da28979d986687": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6279,15 +5833,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a4367ae0bcb046beb0aa7fa55ae59b6d", + "layout": "IPY_MODEL_3908248ea2ec46ac809cab2a57825a21", "placeholder": "​", - "style": "IPY_MODEL_309f00f07977438688cb2e2a138942ee", + "style": "IPY_MODEL_07f0cc4d1f4c48e99b208c9766cdd9dd", "tabbable": null, "tooltip": null, - "value": "Map (num_proc=4): 100%" + "value": "100%" } }, - "c57636ad3dc54dc0926bb56946b10ab1": { + "a53cf182147b41ca9bc22e924e74e42c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6302,33 +5856,145 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b42c916027f0431eb2b8bccc86357bf7", + "layout": "IPY_MODEL_556e4ef19cb94604aa405d62dbf7da70", "placeholder": "​", - "style": "IPY_MODEL_4685982b10924f9aaf548d58e65e7fff", + "style": "IPY_MODEL_72e2143985fe46f499ca9310aa86d7cf", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 54.36it/s]" + "value": "100%" + } + }, + "a67d87241bce4f91bb13e7d23a32a198": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a7425ae868e14eafae4bfb0468cec374": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "c5b4b8f4759d4bac96dbdbe270c10816": { + "a7c9ce9c07064710be69384ca6979edc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_514ea03743d344d09972d77a32c7fe4e", + "IPY_MODEL_13c48dc4b3ea4c4e9f4129d1fc35a62d", + "IPY_MODEL_9345bef96daa4abfb55fa1601eccb32b" + ], + "layout": "IPY_MODEL_c182372db841430d9f4c29cdb63c0416", + "tabbable": null, + "tooltip": null } }, - "c8b6019d0f094ef6b63a722264c996a6": { + "a88fbadaab274cb49b7ed896c888e4b6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6346,7 +6012,7 @@ "text_color": null } }, - "c91d42b9d1c342cf8093443f4eaa5204": { + "a961cd5b3a9f46fab76d3b67117504c2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6399,7 +6065,7 @@ "width": null } }, - "c970c34254fb445a91f6a8570b2ee0a6": { + "ac45d17c69d34562abc6d440b8927476": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6414,15 +6080,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_43636151bcde46648bb7aaa89ccb8a51", + "layout": "IPY_MODEL_73cad09577cd4329a162f8f43e7f6529", "placeholder": "​", - "style": "IPY_MODEL_4c60fd9ec0a14e40806e9a0ac5d515de", + "style": "IPY_MODEL_858ad6e310a44429a4ccbefd0a93534b", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": "Downloading readme: 100%" } }, - "ca16aad0772747da8b6cf03b6abc3643": { + "adb5aee751224ee8a1f228414df1dd9c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6475,7 +6141,7 @@ "width": null } }, - "cb02675b1ffa460abaf2766e247e9e76": { + "b4ade25b47e1457e9269900f0df62d61": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6490,15 +6156,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_52f3a03aa4994380b8b138695e6993e3", + "layout": "IPY_MODEL_388bd680a9a044b6b3414838cfd94a55", "placeholder": "​", - "style": "IPY_MODEL_46d058b0dc32483b92ffc747203adfbb", + "style": "IPY_MODEL_5898a17eff104f3b8b3b7c0757f5ae54", "tabbable": null, "tooltip": null, - "value": "100%" + "value": "Downloading data: 100%" + } + }, + "b672bc6dd3974984b7a7b6bebe8959ce": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4334d6adca6d419fa7e126021ea11a03", + "placeholder": "​", + "style": "IPY_MODEL_0d7259ccb2014d6e89b9d45abcaffdb4", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 57.07it/s]" } }, - "cbb874850a0f4b8585830f781d02f6c7": { + "bd2b67ddccbc4fdaaea0035ffeb51e52": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6516,31 +6205,126 @@ "text_color": null } }, - "ccc0d279330845b8b34f60a57e76743f": { + "bf698e9ccdfc47d5a94fb2de5f578593": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c16a8793cbbe40a1ac2b15a9037fabd9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0bed948828f141bca1ebc19b7c3ee31a", + "IPY_MODEL_c2dd24dc859f4dcb9453449bac09534d", + "IPY_MODEL_b672bc6dd3974984b7a7b6bebe8959ce" + ], + "layout": "IPY_MODEL_900f06ca89244862b1783effd7d731d6", + "tabbable": null, + "tooltip": null + } + }, + "c182372db841430d9f4c29cdb63c0416": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c2dd24dc859f4dcb9453449bac09534d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_afdc4d351896438cb7e9760348393ddc", - "IPY_MODEL_9d4ba4757b8d48bba46623ad8f4b39a3", - "IPY_MODEL_5a0315cb61bf4e1b9c52127d29ddf405" - ], - "layout": "IPY_MODEL_e23cc3c422934dabbc7fa7851f6ab787", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_981864ffd419481399deca7093834a2a", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a442f3280e014780ac27c505772845cd", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 40.0 } }, - "ccdd8d1027864d7697480a9f400c528c": { + "c3bd4b3cde4a416e8291af2ebeb6721e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6593,31 +6377,23 @@ "width": null } }, - "cd9cd1e986f74424b60db3510b43826d": { + "c626e7c1959e4543bf621cd4e48a04b5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7ef0c36f65034c0f9d5b161441b6e47c", - "IPY_MODEL_554fe55159064f65be3faeef3e587efa", - "IPY_MODEL_1ee09afeb3644ffaa25df19feb6d7756" - ], - "layout": "IPY_MODEL_f8bd324861dd4e2ca40f79d16ca6ca8a", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "ce14eb0a9f94406f9670f2a1dc1345c7": { + "c7140224e2014a9f86018b2a80db9349": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6670,7 +6446,23 @@ "width": null } }, - "cf2c49458b23448bbd90dd22355dc406": { + "c775fa235666491088a75b6255f32e3f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c9b78c79f9ec4c21b9443be9011f71b8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6685,15 +6477,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0fc0d5f5c5fd4be8b001f46a67af29e6", + "layout": "IPY_MODEL_84c334be19a8458598d52bab4fff8168", "placeholder": "​", - "style": "IPY_MODEL_5568c1b377ee44e2baadcd2713472be4", + "style": "IPY_MODEL_ca3356f8aa3549c988e57f881ebb4f26", "tabbable": null, "tooltip": null, - "value": "Computing checksums: 100%" + "value": "100%" } }, - "d0d8518db2324e00900b4cc97bf83cba": { + "ca3356f8aa3549c988e57f881ebb4f26": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6711,7 +6503,7 @@ "text_color": null } }, - "d16d5292876e41bbb948c7172eb1b022": { + "cae6f31af2a640f6bcb2c87e7ed66781": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6764,7 +6556,95 @@ "width": null } }, - "d89cbf3e824f490b81fa19d70eb0784a": { + "cc44de893c454e9984744278aef97f45": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_dac045d1446b465fbaf4b6bf28f028b1", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d26c5cba80084451802d565928745bf7", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "d26c5cba80084451802d565928745bf7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "d28f34dd188a4bedb78aadba614f3e96": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ee7dcfe3d76a4bafa576315937b01a16", + "placeholder": "​", + "style": "IPY_MODEL_304c8861034b44bbba4a12de67849cfb", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "d36f9da2d76f49ca94d84d03416aa07a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2b3b3a12683342b7b1f3e4a7c16ff834", + "placeholder": "​", + "style": "IPY_MODEL_95dff653e0bb4198be1ecfaf08164b44", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "d3e45ec46ce344f091c4ff564f4fc634": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6817,7 +6697,30 @@ "width": null } }, - "dc9f46273db144d982f466b186d6ea8d": { + "d40d4080e5904c30908beea6c18b3f84": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f7c7e57c728147049f52ba691e408da8", + "placeholder": "​", + "style": "IPY_MODEL_9db32a78998341e490d0bfe9165b1cde", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:00<00:00, 287205.28 examples/s]" + } + }, + "d4db058689ca42958386a0f64cf412d9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -6832,16 +6735,57 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_c970c34254fb445a91f6a8570b2ee0a6", - "IPY_MODEL_62dcb057ad134472b2f7163715a51417", - "IPY_MODEL_9b43cbe4d995486aa4d260f3e1778a5b" + "IPY_MODEL_0f00fb4673354831ac246ea7823f56c9", + "IPY_MODEL_909c9effd0a04202a36f69334a1adb92", + "IPY_MODEL_efe22845beb449a58f2cc4d0a55a4321" ], - "layout": "IPY_MODEL_badd7c549d834045856ae809d48d9fa6", + "layout": "IPY_MODEL_cae6f31af2a640f6bcb2c87e7ed66781", "tabbable": null, "tooltip": null } }, - "dd4c58e0fd6948c49326bb0bb920e8c2": { + "d6576c007df04cb69b189749179d99bc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7fd68800618a439c92872f907d74563e", + "placeholder": "​", + "style": "IPY_MODEL_5dca4c3a272f4e5b98860d48cd7a7768", + "tabbable": null, + "tooltip": null, + "value": " 9.02k/9.02k [00:00<00:00, 1.10MB/s]" + } + }, + "d66678e7de914cb099b5b837d46979bf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d688f38225b4426ca69152695ceda860": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6894,7 +6838,7 @@ "width": null } }, - "dd7812d3359f4747a8d626fa5a9094fb": { + "d7f8a17831a8490385be98caaf69cc9e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6947,7 +6891,7 @@ "width": null } }, - "e0c002bd6e8c4598837703a0b54dfdd7": { + "d994309e4ee24291984f6e27719ed798": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -6963,40 +6907,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b826bec48c45487bbcae63716ac684fb", + "layout": "IPY_MODEL_74187c11715545dfbb571e697ad3074b", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_f241c4541bfc4e678ceec3ff46602200", + "style": "IPY_MODEL_39ecdb0609d94bfcb77249dccd8e7b0a", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "e1863acf55ff485abde9e3dd42efb403": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fc81a44ecf7c4ad1b851ab50894a6d71", - "placeholder": "​", - "style": "IPY_MODEL_c5b4b8f4759d4bac96dbdbe270c10816", - "tabbable": null, - "tooltip": null, - "value": " 2/2 [00:00<00:00, 584.78it/s]" - } - }, - "e1f00c2ba7fa42879c2ae82deb9ae36b": { + "dac045d1446b465fbaf4b6bf28f028b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7038,42 +6959,18 @@ "max_width": null, "min_height": null, "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "e20ec2fb456e4bb9bfb446110e53d341": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_545ab14d8bdc4758954170fd6a6c7f2e", - "IPY_MODEL_4e00ed69c8994d9990e59ec728ec2a31", - "IPY_MODEL_c57636ad3dc54dc0926bb56946b10ab1" - ], - "layout": "IPY_MODEL_141a122f5b984ea4b8d354438a45f4ac", - "tabbable": null, - "tooltip": null + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "e23cc3c422934dabbc7fa7851f6ab787": { + "db5fae1c4ed24b4d84db9673debe1849": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7126,7 +7023,7 @@ "width": null } }, - "e2ddf40e09984deca4602e0c72e8bfd6": { + "db7b4d566c2c4c74a93bf056d2eed6a7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7179,7 +7076,49 @@ "width": null } }, - "e34ca479b1cd4338a1b034c9bdb6cdde": { + "ddd88fe1f1a14aeabe63b7876fcebfbe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_959ae504cb01405781572c8c3e5f8f73", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f914aa3a0cc1445b81656d22ac9574b0", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "defede3efe8b41b3ba6660edba087f85": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e13ece2f21e645f39a334fc1327f6255": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7232,33 +7171,30 @@ "width": null } }, - "e504d10254784e0fb8866b5539c1da25": { + "e6eeb0f18d5342c7bfb2162167342fdb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4afd56e1be684a4ea49cf9bd94feb822", - "max": 30931277.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_1af3d41b10be4701a4e58df18c1a38f8", + "layout": "IPY_MODEL_e99ece1363c342c79e173d40cf560918", + "placeholder": "​", + "style": "IPY_MODEL_a88fbadaab274cb49b7ed896c888e4b6", "tabbable": null, "tooltip": null, - "value": 30931277.0 + "value": " 60000/60000 [00:11<00:00, 6407.92 examples/s]" } }, - "e5d17f8bb48c489d84f85f8b0e82285e": { + "e99ece1363c342c79e173d40cf560918": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7311,7 +7247,7 @@ "width": null } }, - "ea25312b42fa433294a739c7e0d5c2a9": { + "eb036f5b69cb4a91adf36df7f4d466a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -7327,93 +7263,107 @@ "description_width": "" } }, - "eb1eb741913649deae955849ad2ff5e8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a02cbe5a9ae843ff95023851f56408fe", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ea25312b42fa433294a739c7e0d5c2a9", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "edbabb45d56b40b7a43858d7ec51dcae": { - "model_module": "@jupyter-widgets/controls", + "ee7dcfe3d76a4bafa576315937b01a16": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "f241c4541bfc4e678ceec3ff46602200": { + "efe22845beb449a58f2cc4d0a55a4321": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8957a61b05ae4e12aca0eb2a5e277a0b", + "placeholder": "​", + "style": "IPY_MODEL_6bdc6710af8142aba7c58c6798148a76", + "tabbable": null, + "tooltip": null, + "value": " 2/2 [00:00<00:00, 591.46it/s]" } }, - "f5bdc90f13ac4085a6c87c3f16c1db23": { + "f16276c8203a4d9f9a7457266c1e666c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7de683e49bbf4f65959e300da53047ed", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_49ff26f36c4046ccaa23af5a0e1bd79a", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d28f34dd188a4bedb78aadba614f3e96", + "IPY_MODEL_33b768a7f4294ffeab6f702ca4b71d4c", + "IPY_MODEL_259a364c31bf46d482e138d0d9607b1d" + ], + "layout": "IPY_MODEL_f3866e54cbb646e0aa91281de9bbd202", "tabbable": null, - "tooltip": null, - "value": 60000.0 + "tooltip": null } }, - "f8bd324861dd4e2ca40f79d16ca6ca8a": { + "f1d92e4fad884e60ae51f8e53d0a9fe6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7466,30 +7416,7 @@ "width": null } }, - "fb35e5a84e5942e59aa0f8e4a6d4b045": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4e60241fc8fc4199a601cbd4c617c5e6", - "placeholder": "​", - "style": "IPY_MODEL_a3d204cf9a5a441d9ce00a62738821f3", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "fc81a44ecf7c4ad1b851ab50894a6d71": { + "f3866e54cbb646e0aa91281de9bbd202": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7542,7 +7469,46 @@ "width": null } }, - "fd18df91f07f4dd2951fcd6f5d643d2e": { + "f46fc8a56a0140628ce3b129a42d4d00": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0cc4d92fb4044144b93e36fc005c9b57", + "placeholder": "​", + "style": "IPY_MODEL_d66678e7de914cb099b5b837d46979bf", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "f6c771c77fcc45a4a4178d8748beeb3c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "f7c7e57c728147049f52ba691e408da8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7594,6 +7560,40 @@ "visibility": null, "width": null } + }, + "f914aa3a0cc1445b81656d22ac9574b0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "fc0dbc5ad67f44d1970ac151d319ba0e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index 688f732c4..7e053c006 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb @@ -73,10 +73,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:11.092091Z", - "iopub.status.busy": "2024-09-26T14:51:11.091687Z", - "iopub.status.idle": "2024-09-26T14:51:12.301495Z", - "shell.execute_reply": "2024-09-26T14:51:12.300906Z" + "iopub.execute_input": "2024-09-26T16:46:49.490061Z", + "iopub.status.busy": "2024-09-26T16:46:49.489608Z", + "iopub.status.idle": "2024-09-26T16:46:50.662598Z", + "shell.execute_reply": "2024-09-26T16:46:50.662034Z" }, "nbsphinx": "hidden" }, @@ -86,7 +86,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.303829Z", - "iopub.status.busy": "2024-09-26T14:51:12.303363Z", - "iopub.status.idle": "2024-09-26T14:51:12.322353Z", - "shell.execute_reply": "2024-09-26T14:51:12.321902Z" + "iopub.execute_input": "2024-09-26T16:46:50.664743Z", + "iopub.status.busy": "2024-09-26T16:46:50.664376Z", + "iopub.status.idle": "2024-09-26T16:46:50.682132Z", + "shell.execute_reply": "2024-09-26T16:46:50.681695Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.324450Z", - "iopub.status.busy": "2024-09-26T14:51:12.324010Z", - "iopub.status.idle": "2024-09-26T14:51:12.348557Z", - "shell.execute_reply": "2024-09-26T14:51:12.348062Z" + "iopub.execute_input": "2024-09-26T16:46:50.683962Z", + "iopub.status.busy": "2024-09-26T16:46:50.683639Z", + "iopub.status.idle": "2024-09-26T16:46:50.719357Z", + "shell.execute_reply": "2024-09-26T16:46:50.718929Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.350597Z", - "iopub.status.busy": "2024-09-26T14:51:12.350164Z", - "iopub.status.idle": "2024-09-26T14:51:12.353712Z", - "shell.execute_reply": "2024-09-26T14:51:12.353237Z" + "iopub.execute_input": "2024-09-26T16:46:50.721064Z", + "iopub.status.busy": "2024-09-26T16:46:50.720643Z", + "iopub.status.idle": "2024-09-26T16:46:50.724183Z", + "shell.execute_reply": "2024-09-26T16:46:50.723622Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.355535Z", - "iopub.status.busy": "2024-09-26T14:51:12.355193Z", - "iopub.status.idle": "2024-09-26T14:51:12.364277Z", - "shell.execute_reply": "2024-09-26T14:51:12.363833Z" + "iopub.execute_input": "2024-09-26T16:46:50.726085Z", + "iopub.status.busy": "2024-09-26T16:46:50.725669Z", + "iopub.status.idle": "2024-09-26T16:46:50.733226Z", + "shell.execute_reply": "2024-09-26T16:46:50.732816Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.366192Z", - "iopub.status.busy": "2024-09-26T14:51:12.365860Z", - "iopub.status.idle": "2024-09-26T14:51:12.368238Z", - "shell.execute_reply": "2024-09-26T14:51:12.367806Z" + "iopub.execute_input": "2024-09-26T16:46:50.735243Z", + "iopub.status.busy": "2024-09-26T16:46:50.734917Z", + "iopub.status.idle": "2024-09-26T16:46:50.737575Z", + "shell.execute_reply": "2024-09-26T16:46:50.737017Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.369910Z", - "iopub.status.busy": "2024-09-26T14:51:12.369584Z", - "iopub.status.idle": "2024-09-26T14:51:15.473892Z", - "shell.execute_reply": "2024-09-26T14:51:15.473328Z" + "iopub.execute_input": "2024-09-26T16:46:50.739261Z", + "iopub.status.busy": "2024-09-26T16:46:50.738945Z", + "iopub.status.idle": "2024-09-26T16:46:53.810159Z", + "shell.execute_reply": "2024-09-26T16:46:53.809623Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:15.476275Z", - "iopub.status.busy": "2024-09-26T14:51:15.475917Z", - "iopub.status.idle": "2024-09-26T14:51:15.485407Z", - "shell.execute_reply": "2024-09-26T14:51:15.484796Z" + "iopub.execute_input": "2024-09-26T16:46:53.812406Z", + "iopub.status.busy": "2024-09-26T16:46:53.812011Z", + "iopub.status.idle": "2024-09-26T16:46:53.821759Z", + "shell.execute_reply": "2024-09-26T16:46:53.821333Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:15.487349Z", - "iopub.status.busy": "2024-09-26T14:51:15.487005Z", - "iopub.status.idle": "2024-09-26T14:51:17.515517Z", - "shell.execute_reply": "2024-09-26T14:51:17.514901Z" + "iopub.execute_input": "2024-09-26T16:46:53.823459Z", + "iopub.status.busy": "2024-09-26T16:46:53.823124Z", + "iopub.status.idle": "2024-09-26T16:46:55.741477Z", + "shell.execute_reply": "2024-09-26T16:46:55.740911Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.517807Z", - "iopub.status.busy": "2024-09-26T14:51:17.517109Z", - "iopub.status.idle": "2024-09-26T14:51:17.536120Z", - "shell.execute_reply": "2024-09-26T14:51:17.535624Z" + "iopub.execute_input": "2024-09-26T16:46:55.743632Z", + "iopub.status.busy": "2024-09-26T16:46:55.743134Z", + "iopub.status.idle": "2024-09-26T16:46:55.761258Z", + "shell.execute_reply": "2024-09-26T16:46:55.760774Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.537976Z", - "iopub.status.busy": "2024-09-26T14:51:17.537611Z", - "iopub.status.idle": "2024-09-26T14:51:17.545869Z", - "shell.execute_reply": "2024-09-26T14:51:17.545319Z" + "iopub.execute_input": "2024-09-26T16:46:55.762948Z", + "iopub.status.busy": "2024-09-26T16:46:55.762638Z", + "iopub.status.idle": "2024-09-26T16:46:55.770290Z", + "shell.execute_reply": "2024-09-26T16:46:55.769866Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.547622Z", - "iopub.status.busy": "2024-09-26T14:51:17.547301Z", - "iopub.status.idle": "2024-09-26T14:51:17.556250Z", - "shell.execute_reply": "2024-09-26T14:51:17.555755Z" + "iopub.execute_input": "2024-09-26T16:46:55.771961Z", + "iopub.status.busy": "2024-09-26T16:46:55.771630Z", + "iopub.status.idle": "2024-09-26T16:46:55.780201Z", + "shell.execute_reply": "2024-09-26T16:46:55.779777Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.557888Z", - "iopub.status.busy": "2024-09-26T14:51:17.557705Z", - "iopub.status.idle": "2024-09-26T14:51:17.565685Z", - "shell.execute_reply": "2024-09-26T14:51:17.565225Z" + "iopub.execute_input": "2024-09-26T16:46:55.781863Z", + "iopub.status.busy": "2024-09-26T16:46:55.781594Z", + "iopub.status.idle": "2024-09-26T16:46:55.789510Z", + "shell.execute_reply": "2024-09-26T16:46:55.788979Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.567291Z", - "iopub.status.busy": "2024-09-26T14:51:17.567107Z", - "iopub.status.idle": "2024-09-26T14:51:17.576362Z", - "shell.execute_reply": "2024-09-26T14:51:17.575909Z" + "iopub.execute_input": "2024-09-26T16:46:55.791202Z", + "iopub.status.busy": "2024-09-26T16:46:55.790892Z", + "iopub.status.idle": "2024-09-26T16:46:55.799367Z", + "shell.execute_reply": "2024-09-26T16:46:55.798943Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.577990Z", - "iopub.status.busy": "2024-09-26T14:51:17.577812Z", - "iopub.status.idle": "2024-09-26T14:51:17.585393Z", - "shell.execute_reply": "2024-09-26T14:51:17.584817Z" + "iopub.execute_input": "2024-09-26T16:46:55.800996Z", + "iopub.status.busy": "2024-09-26T16:46:55.800824Z", + "iopub.status.idle": "2024-09-26T16:46:55.808181Z", + "shell.execute_reply": "2024-09-26T16:46:55.807731Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.587245Z", - "iopub.status.busy": "2024-09-26T14:51:17.586929Z", - "iopub.status.idle": "2024-09-26T14:51:17.594347Z", - "shell.execute_reply": "2024-09-26T14:51:17.593795Z" + "iopub.execute_input": "2024-09-26T16:46:55.809783Z", + "iopub.status.busy": "2024-09-26T16:46:55.809613Z", + "iopub.status.idle": "2024-09-26T16:46:55.816908Z", + "shell.execute_reply": "2024-09-26T16:46:55.816477Z" } }, "outputs": [ @@ -1306,10 +1306,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.596172Z", - "iopub.status.busy": "2024-09-26T14:51:17.595784Z", - "iopub.status.idle": "2024-09-26T14:51:17.604165Z", - "shell.execute_reply": "2024-09-26T14:51:17.603720Z" + "iopub.execute_input": "2024-09-26T16:46:55.818594Z", + "iopub.status.busy": "2024-09-26T16:46:55.818403Z", + "iopub.status.idle": "2024-09-26T16:46:55.827049Z", + "shell.execute_reply": "2024-09-26T16:46:55.826476Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 5b5c4a565..38b874be2 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:20.550084Z", - "iopub.status.busy": "2024-09-26T14:51:20.549919Z", - "iopub.status.idle": "2024-09-26T14:51:23.546779Z", - "shell.execute_reply": "2024-09-26T14:51:23.546140Z" + "iopub.execute_input": "2024-09-26T16:46:58.410038Z", + "iopub.status.busy": "2024-09-26T16:46:58.409552Z", + "iopub.status.idle": "2024-09-26T16:47:01.293395Z", + "shell.execute_reply": "2024-09-26T16:47:01.292721Z" }, "nbsphinx": "hidden" }, @@ -96,7 +96,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -121,10 +121,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.549062Z", - "iopub.status.busy": "2024-09-26T14:51:23.548756Z", - "iopub.status.idle": "2024-09-26T14:51:23.551996Z", - "shell.execute_reply": "2024-09-26T14:51:23.551554Z" + "iopub.execute_input": "2024-09-26T16:47:01.295853Z", + "iopub.status.busy": "2024-09-26T16:47:01.295379Z", + "iopub.status.idle": "2024-09-26T16:47:01.298895Z", + "shell.execute_reply": "2024-09-26T16:47:01.298449Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.553571Z", - "iopub.status.busy": "2024-09-26T14:51:23.553396Z", - "iopub.status.idle": "2024-09-26T14:51:23.556530Z", - "shell.execute_reply": "2024-09-26T14:51:23.556072Z" + "iopub.execute_input": "2024-09-26T16:47:01.300604Z", + "iopub.status.busy": "2024-09-26T16:47:01.300270Z", + "iopub.status.idle": "2024-09-26T16:47:01.303351Z", + "shell.execute_reply": "2024-09-26T16:47:01.302879Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.558190Z", - "iopub.status.busy": "2024-09-26T14:51:23.558016Z", - "iopub.status.idle": "2024-09-26T14:51:23.584373Z", - "shell.execute_reply": "2024-09-26T14:51:23.583877Z" + "iopub.execute_input": "2024-09-26T16:47:01.305038Z", + "iopub.status.busy": "2024-09-26T16:47:01.304702Z", + "iopub.status.idle": "2024-09-26T16:47:01.343430Z", + "shell.execute_reply": "2024-09-26T16:47:01.342872Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.586327Z", - "iopub.status.busy": "2024-09-26T14:51:23.585980Z", - "iopub.status.idle": "2024-09-26T14:51:23.589627Z", - "shell.execute_reply": "2024-09-26T14:51:23.589147Z" + "iopub.execute_input": "2024-09-26T16:47:01.345069Z", + "iopub.status.busy": "2024-09-26T16:47:01.344889Z", + "iopub.status.idle": "2024-09-26T16:47:01.348954Z", + "shell.execute_reply": "2024-09-26T16:47:01.348459Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'supported_cards_and_currencies', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'change_pin', 'beneficiary_not_allowed', 'card_about_to_expire', 'getting_spare_card', 'apple_pay_or_google_pay', 'cancel_transfer'}\n" + "Classes: {'cancel_transfer', 'card_about_to_expire', 'visa_or_mastercard', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'change_pin', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'getting_spare_card'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.591183Z", - "iopub.status.busy": "2024-09-26T14:51:23.591009Z", - "iopub.status.idle": "2024-09-26T14:51:23.594239Z", - "shell.execute_reply": "2024-09-26T14:51:23.593788Z" + "iopub.execute_input": "2024-09-26T16:47:01.350721Z", + "iopub.status.busy": "2024-09-26T16:47:01.350354Z", + "iopub.status.idle": "2024-09-26T16:47:01.353211Z", + "shell.execute_reply": "2024-09-26T16:47:01.352759Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.595893Z", - "iopub.status.busy": "2024-09-26T14:51:23.595586Z", - "iopub.status.idle": "2024-09-26T14:51:27.775987Z", - "shell.execute_reply": "2024-09-26T14:51:27.775330Z" + "iopub.execute_input": "2024-09-26T16:47:01.355002Z", + "iopub.status.busy": "2024-09-26T16:47:01.354700Z", + "iopub.status.idle": "2024-09-26T16:47:05.074430Z", + "shell.execute_reply": "2024-09-26T16:47:05.073775Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:27.778341Z", - "iopub.status.busy": "2024-09-26T14:51:27.777966Z", - "iopub.status.idle": "2024-09-26T14:51:28.697834Z", - "shell.execute_reply": "2024-09-26T14:51:28.697228Z" + "iopub.execute_input": "2024-09-26T16:47:05.076632Z", + "iopub.status.busy": "2024-09-26T16:47:05.076427Z", + "iopub.status.idle": "2024-09-26T16:47:05.983078Z", + "shell.execute_reply": "2024-09-26T16:47:05.982471Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:28.700329Z", - "iopub.status.busy": "2024-09-26T14:51:28.699942Z", - "iopub.status.idle": "2024-09-26T14:51:28.702874Z", - "shell.execute_reply": "2024-09-26T14:51:28.702381Z" + "iopub.execute_input": "2024-09-26T16:47:05.985551Z", + "iopub.status.busy": "2024-09-26T16:47:05.985169Z", + "iopub.status.idle": "2024-09-26T16:47:05.988117Z", + "shell.execute_reply": "2024-09-26T16:47:05.987611Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:28.704853Z", - "iopub.status.busy": "2024-09-26T14:51:28.704499Z", - "iopub.status.idle": "2024-09-26T14:51:30.723899Z", - "shell.execute_reply": "2024-09-26T14:51:30.723229Z" + "iopub.execute_input": "2024-09-26T16:47:05.990255Z", + "iopub.status.busy": "2024-09-26T16:47:05.989893Z", + "iopub.status.idle": "2024-09-26T16:47:07.971023Z", + "shell.execute_reply": "2024-09-26T16:47:07.970323Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.727734Z", - "iopub.status.busy": "2024-09-26T14:51:30.726555Z", - "iopub.status.idle": "2024-09-26T14:51:30.752360Z", - "shell.execute_reply": "2024-09-26T14:51:30.751847Z" + "iopub.execute_input": "2024-09-26T16:47:07.974754Z", + "iopub.status.busy": "2024-09-26T16:47:07.973728Z", + "iopub.status.idle": "2024-09-26T16:47:07.999496Z", + "shell.execute_reply": "2024-09-26T16:47:07.998965Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.755440Z", - "iopub.status.busy": "2024-09-26T14:51:30.754576Z", - "iopub.status.idle": "2024-09-26T14:51:30.764760Z", - "shell.execute_reply": "2024-09-26T14:51:30.764347Z" + "iopub.execute_input": "2024-09-26T16:47:08.002480Z", + "iopub.status.busy": "2024-09-26T16:47:08.001687Z", + "iopub.status.idle": "2024-09-26T16:47:08.011823Z", + "shell.execute_reply": "2024-09-26T16:47:08.011250Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.767190Z", - "iopub.status.busy": "2024-09-26T14:51:30.766574Z", - "iopub.status.idle": "2024-09-26T14:51:30.771522Z", - "shell.execute_reply": "2024-09-26T14:51:30.771112Z" + "iopub.execute_input": "2024-09-26T16:47:08.013635Z", + "iopub.status.busy": "2024-09-26T16:47:08.013359Z", + "iopub.status.idle": "2024-09-26T16:47:08.017732Z", + "shell.execute_reply": "2024-09-26T16:47:08.017145Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.773863Z", - "iopub.status.busy": "2024-09-26T14:51:30.773237Z", - "iopub.status.idle": "2024-09-26T14:51:30.780343Z", - "shell.execute_reply": "2024-09-26T14:51:30.779939Z" + "iopub.execute_input": "2024-09-26T16:47:08.019401Z", + "iopub.status.busy": "2024-09-26T16:47:08.019129Z", + "iopub.status.idle": "2024-09-26T16:47:08.025649Z", + "shell.execute_reply": "2024-09-26T16:47:08.025085Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.782231Z", - "iopub.status.busy": "2024-09-26T14:51:30.782055Z", - "iopub.status.idle": "2024-09-26T14:51:30.788970Z", - "shell.execute_reply": "2024-09-26T14:51:30.788375Z" + "iopub.execute_input": "2024-09-26T16:47:08.027291Z", + "iopub.status.busy": "2024-09-26T16:47:08.027019Z", + "iopub.status.idle": "2024-09-26T16:47:08.033453Z", + "shell.execute_reply": "2024-09-26T16:47:08.032925Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.790778Z", - "iopub.status.busy": "2024-09-26T14:51:30.790601Z", - "iopub.status.idle": "2024-09-26T14:51:30.796446Z", - "shell.execute_reply": "2024-09-26T14:51:30.795882Z" + "iopub.execute_input": "2024-09-26T16:47:08.035074Z", + "iopub.status.busy": "2024-09-26T16:47:08.034761Z", + "iopub.status.idle": "2024-09-26T16:47:08.040415Z", + "shell.execute_reply": "2024-09-26T16:47:08.039969Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.798232Z", - "iopub.status.busy": "2024-09-26T14:51:30.797967Z", - "iopub.status.idle": "2024-09-26T14:51:30.806498Z", - "shell.execute_reply": "2024-09-26T14:51:30.805933Z" + "iopub.execute_input": "2024-09-26T16:47:08.042047Z", + "iopub.status.busy": "2024-09-26T16:47:08.041737Z", + "iopub.status.idle": "2024-09-26T16:47:08.050191Z", + "shell.execute_reply": "2024-09-26T16:47:08.049613Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.808353Z", - "iopub.status.busy": "2024-09-26T14:51:30.808081Z", - "iopub.status.idle": "2024-09-26T14:51:30.813342Z", - "shell.execute_reply": "2024-09-26T14:51:30.812825Z" + "iopub.execute_input": "2024-09-26T16:47:08.051928Z", + "iopub.status.busy": "2024-09-26T16:47:08.051586Z", + "iopub.status.idle": "2024-09-26T16:47:08.056924Z", + "shell.execute_reply": "2024-09-26T16:47:08.056363Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.814997Z", - "iopub.status.busy": "2024-09-26T14:51:30.814668Z", - "iopub.status.idle": "2024-09-26T14:51:30.819982Z", - "shell.execute_reply": "2024-09-26T14:51:30.819532Z" + "iopub.execute_input": "2024-09-26T16:47:08.058659Z", + "iopub.status.busy": "2024-09-26T16:47:08.058314Z", + "iopub.status.idle": "2024-09-26T16:47:08.063716Z", + "shell.execute_reply": "2024-09-26T16:47:08.063139Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.821669Z", - "iopub.status.busy": "2024-09-26T14:51:30.821337Z", - "iopub.status.idle": "2024-09-26T14:51:30.824940Z", - "shell.execute_reply": "2024-09-26T14:51:30.824366Z" + "iopub.execute_input": "2024-09-26T16:47:08.065538Z", + "iopub.status.busy": "2024-09-26T16:47:08.065225Z", + "iopub.status.idle": "2024-09-26T16:47:08.068887Z", + "shell.execute_reply": "2024-09-26T16:47:08.068337Z" } }, "outputs": [ @@ -1449,10 +1449,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.826780Z", - "iopub.status.busy": "2024-09-26T14:51:30.826459Z", - "iopub.status.idle": "2024-09-26T14:51:30.831493Z", - "shell.execute_reply": "2024-09-26T14:51:30.831041Z" + "iopub.execute_input": "2024-09-26T16:47:08.070894Z", + "iopub.status.busy": "2024-09-26T16:47:08.070331Z", + "iopub.status.idle": "2024-09-26T16:47:08.075632Z", + "shell.execute_reply": "2024-09-26T16:47:08.075187Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb index 9404f1540..07fff34e8 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb @@ -38,10 +38,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:34.296488Z", - "iopub.status.busy": "2024-09-26T14:51:34.296076Z", - "iopub.status.idle": "2024-09-26T14:51:35.016105Z", - "shell.execute_reply": "2024-09-26T14:51:35.015553Z" + "iopub.execute_input": "2024-09-26T16:47:11.525433Z", + "iopub.status.busy": "2024-09-26T16:47:11.525274Z", + "iopub.status.idle": "2024-09-26T16:47:12.226983Z", + "shell.execute_reply": "2024-09-26T16:47:12.226406Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:35.018426Z", - "iopub.status.busy": "2024-09-26T14:51:35.017967Z", - "iopub.status.idle": "2024-09-26T14:51:35.151580Z", - "shell.execute_reply": "2024-09-26T14:51:35.151068Z" + "iopub.execute_input": "2024-09-26T16:47:12.229251Z", + "iopub.status.busy": "2024-09-26T16:47:12.228753Z", + "iopub.status.idle": "2024-09-26T16:47:12.360301Z", + "shell.execute_reply": "2024-09-26T16:47:12.359800Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:35.153697Z", - "iopub.status.busy": "2024-09-26T14:51:35.153277Z", - "iopub.status.idle": "2024-09-26T14:51:35.177588Z", - "shell.execute_reply": "2024-09-26T14:51:35.176982Z" + "iopub.execute_input": "2024-09-26T16:47:12.362311Z", + "iopub.status.busy": "2024-09-26T16:47:12.361886Z", + "iopub.status.idle": "2024-09-26T16:47:12.385294Z", + "shell.execute_reply": "2024-09-26T16:47:12.384666Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:35.179788Z", - "iopub.status.busy": "2024-09-26T14:51:35.179361Z", - "iopub.status.idle": "2024-09-26T14:51:37.765581Z", - "shell.execute_reply": "2024-09-26T14:51:37.764993Z" + "iopub.execute_input": "2024-09-26T16:47:12.387377Z", + "iopub.status.busy": "2024-09-26T16:47:12.387130Z", + "iopub.status.idle": "2024-09-26T16:47:14.927165Z", + "shell.execute_reply": "2024-09-26T16:47:14.926608Z" } }, "outputs": [ @@ -235,7 +235,7 @@ "Finding class_imbalance issues ...\n", "Finding underperforming_group issues ...\n", "\n", - "Audit complete. 524 issues found in the dataset.\n" + "Audit complete. 523 issues found in the dataset.\n" ] }, { @@ -280,13 +280,13 @@ " \n", " 2\n", " outlier\n", - " 0.356924\n", - " 363\n", + " 0.356958\n", + " 362\n", " \n", " \n", " 3\n", " near_duplicate\n", - " 0.619581\n", + " 0.619565\n", " 108\n", " \n", " \n", @@ -315,8 +315,8 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356924 363\n", - "3 near_duplicate 0.619581 108\n", + "2 outlier 0.356958 362\n", + "3 near_duplicate 0.619565 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", "6 underperforming_group 0.651838 0" @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:37.767993Z", - "iopub.status.busy": "2024-09-26T14:51:37.767425Z", - "iopub.status.idle": "2024-09-26T14:51:46.526023Z", - "shell.execute_reply": "2024-09-26T14:51:46.525421Z" + "iopub.execute_input": "2024-09-26T16:47:14.929608Z", + "iopub.status.busy": "2024-09-26T16:47:14.929044Z", + "iopub.status.idle": "2024-09-26T16:47:23.657825Z", + "shell.execute_reply": "2024-09-26T16:47:23.657226Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:46.528043Z", - "iopub.status.busy": "2024-09-26T14:51:46.527681Z", - "iopub.status.idle": "2024-09-26T14:51:46.730683Z", - "shell.execute_reply": "2024-09-26T14:51:46.730045Z" + "iopub.execute_input": "2024-09-26T16:47:23.659768Z", + "iopub.status.busy": "2024-09-26T16:47:23.659418Z", + "iopub.status.idle": "2024-09-26T16:47:23.835993Z", + "shell.execute_reply": "2024-09-26T16:47:23.835468Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:46.732793Z", - "iopub.status.busy": "2024-09-26T14:51:46.732448Z", - "iopub.status.idle": "2024-09-26T14:51:48.255623Z", - "shell.execute_reply": "2024-09-26T14:51:48.255118Z" + "iopub.execute_input": "2024-09-26T16:47:23.838171Z", + "iopub.status.busy": "2024-09-26T16:47:23.837805Z", + "iopub.status.idle": "2024-09-26T16:47:25.345917Z", + "shell.execute_reply": "2024-09-26T16:47:25.345419Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:48.257484Z", - "iopub.status.busy": "2024-09-26T14:51:48.257119Z", - "iopub.status.idle": "2024-09-26T14:51:48.773736Z", - "shell.execute_reply": "2024-09-26T14:51:48.773135Z" + "iopub.execute_input": "2024-09-26T16:47:25.347933Z", + "iopub.status.busy": "2024-09-26T16:47:25.347511Z", + "iopub.status.idle": "2024-09-26T16:47:25.745787Z", + "shell.execute_reply": "2024-09-26T16:47:25.745185Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:48.775864Z", - "iopub.status.busy": "2024-09-26T14:51:48.775323Z", - "iopub.status.idle": "2024-09-26T14:51:48.790103Z", - "shell.execute_reply": "2024-09-26T14:51:48.789626Z" + "iopub.execute_input": "2024-09-26T16:47:25.748172Z", + "iopub.status.busy": "2024-09-26T16:47:25.747663Z", + "iopub.status.idle": "2024-09-26T16:47:25.761333Z", + "shell.execute_reply": "2024-09-26T16:47:25.760829Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:48.791833Z", - "iopub.status.busy": "2024-09-26T14:51:48.791503Z", - "iopub.status.idle": "2024-09-26T14:51:48.810723Z", - "shell.execute_reply": "2024-09-26T14:51:48.810137Z" + "iopub.execute_input": "2024-09-26T16:47:25.763141Z", + "iopub.status.busy": "2024-09-26T16:47:25.762730Z", + "iopub.status.idle": "2024-09-26T16:47:25.781283Z", + "shell.execute_reply": "2024-09-26T16:47:25.780726Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:48.812726Z", - "iopub.status.busy": "2024-09-26T14:51:48.812340Z", - "iopub.status.idle": "2024-09-26T14:51:49.055015Z", - "shell.execute_reply": "2024-09-26T14:51:49.054398Z" + "iopub.execute_input": "2024-09-26T16:47:25.783262Z", + "iopub.status.busy": "2024-09-26T16:47:25.782919Z", + "iopub.status.idle": "2024-09-26T16:47:26.000731Z", + "shell.execute_reply": "2024-09-26T16:47:26.000107Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.057480Z", - "iopub.status.busy": "2024-09-26T14:51:49.057052Z", - "iopub.status.idle": "2024-09-26T14:51:49.076673Z", - "shell.execute_reply": "2024-09-26T14:51:49.076195Z" + "iopub.execute_input": "2024-09-26T16:47:26.002990Z", + "iopub.status.busy": "2024-09-26T16:47:26.002605Z", + "iopub.status.idle": "2024-09-26T16:47:26.022425Z", + "shell.execute_reply": "2024-09-26T16:47:26.021840Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.078495Z", - "iopub.status.busy": "2024-09-26T14:51:49.078146Z", - "iopub.status.idle": "2024-09-26T14:51:49.248180Z", - "shell.execute_reply": "2024-09-26T14:51:49.247594Z" + "iopub.execute_input": "2024-09-26T16:47:26.024424Z", + "iopub.status.busy": "2024-09-26T16:47:26.024030Z", + "iopub.status.idle": "2024-09-26T16:47:26.193727Z", + "shell.execute_reply": "2024-09-26T16:47:26.193198Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.250291Z", - "iopub.status.busy": "2024-09-26T14:51:49.249923Z", - "iopub.status.idle": "2024-09-26T14:51:49.260161Z", - "shell.execute_reply": "2024-09-26T14:51:49.259683Z" + "iopub.execute_input": "2024-09-26T16:47:26.195878Z", + "iopub.status.busy": "2024-09-26T16:47:26.195408Z", + "iopub.status.idle": "2024-09-26T16:47:26.205589Z", + "shell.execute_reply": "2024-09-26T16:47:26.205122Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.261950Z", - "iopub.status.busy": "2024-09-26T14:51:49.261604Z", - "iopub.status.idle": "2024-09-26T14:51:49.271258Z", - "shell.execute_reply": "2024-09-26T14:51:49.270689Z" + "iopub.execute_input": "2024-09-26T16:47:26.207533Z", + "iopub.status.busy": "2024-09-26T16:47:26.207079Z", + "iopub.status.idle": "2024-09-26T16:47:26.217141Z", + "shell.execute_reply": "2024-09-26T16:47:26.216678Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.272963Z", - "iopub.status.busy": "2024-09-26T14:51:49.272785Z", - "iopub.status.idle": "2024-09-26T14:51:49.300283Z", - "shell.execute_reply": "2024-09-26T14:51:49.299657Z" + "iopub.execute_input": "2024-09-26T16:47:26.219046Z", + "iopub.status.busy": "2024-09-26T16:47:26.218645Z", + "iopub.status.idle": "2024-09-26T16:47:26.245410Z", + "shell.execute_reply": "2024-09-26T16:47:26.244835Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.302435Z", - "iopub.status.busy": "2024-09-26T14:51:49.302020Z", - "iopub.status.idle": "2024-09-26T14:51:49.304853Z", - "shell.execute_reply": "2024-09-26T14:51:49.304388Z" + "iopub.execute_input": "2024-09-26T16:47:26.247193Z", + "iopub.status.busy": "2024-09-26T16:47:26.246877Z", + "iopub.status.idle": "2024-09-26T16:47:26.249497Z", + "shell.execute_reply": "2024-09-26T16:47:26.249056Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.306559Z", - "iopub.status.busy": "2024-09-26T14:51:49.306373Z", - "iopub.status.idle": "2024-09-26T14:51:49.326211Z", - "shell.execute_reply": "2024-09-26T14:51:49.325620Z" + "iopub.execute_input": "2024-09-26T16:47:26.251215Z", + "iopub.status.busy": "2024-09-26T16:47:26.250900Z", + "iopub.status.idle": "2024-09-26T16:47:26.269963Z", + "shell.execute_reply": "2024-09-26T16:47:26.269532Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.328491Z", - "iopub.status.busy": "2024-09-26T14:51:49.327912Z", - "iopub.status.idle": "2024-09-26T14:51:49.332250Z", - "shell.execute_reply": "2024-09-26T14:51:49.331798Z" + "iopub.execute_input": "2024-09-26T16:47:26.271736Z", + "iopub.status.busy": "2024-09-26T16:47:26.271407Z", + "iopub.status.idle": "2024-09-26T16:47:26.275486Z", + "shell.execute_reply": "2024-09-26T16:47:26.275041Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.334080Z", - "iopub.status.busy": "2024-09-26T14:51:49.333676Z", - "iopub.status.idle": "2024-09-26T14:51:49.363534Z", - "shell.execute_reply": "2024-09-26T14:51:49.362928Z" + "iopub.execute_input": "2024-09-26T16:47:26.277173Z", + "iopub.status.busy": "2024-09-26T16:47:26.276779Z", + "iopub.status.idle": "2024-09-26T16:47:26.304468Z", + "shell.execute_reply": "2024-09-26T16:47:26.303913Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.365331Z", - "iopub.status.busy": "2024-09-26T14:51:49.365032Z", - "iopub.status.idle": "2024-09-26T14:51:49.727339Z", - "shell.execute_reply": "2024-09-26T14:51:49.726743Z" + "iopub.execute_input": "2024-09-26T16:47:26.306278Z", + "iopub.status.busy": "2024-09-26T16:47:26.305888Z", + "iopub.status.idle": "2024-09-26T16:47:26.684139Z", + "shell.execute_reply": "2024-09-26T16:47:26.683504Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.729270Z", - "iopub.status.busy": "2024-09-26T14:51:49.729071Z", - "iopub.status.idle": "2024-09-26T14:51:49.732072Z", - "shell.execute_reply": "2024-09-26T14:51:49.731620Z" + "iopub.execute_input": "2024-09-26T16:47:26.686274Z", + "iopub.status.busy": "2024-09-26T16:47:26.685797Z", + "iopub.status.idle": "2024-09-26T16:47:26.689345Z", + "shell.execute_reply": "2024-09-26T16:47:26.688769Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.733810Z", - "iopub.status.busy": "2024-09-26T14:51:49.733632Z", - "iopub.status.idle": "2024-09-26T14:51:49.747657Z", - "shell.execute_reply": "2024-09-26T14:51:49.747198Z" + "iopub.execute_input": "2024-09-26T16:47:26.691398Z", + "iopub.status.busy": "2024-09-26T16:47:26.691056Z", + "iopub.status.idle": "2024-09-26T16:47:26.705013Z", + "shell.execute_reply": "2024-09-26T16:47:26.704453Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.749243Z", - "iopub.status.busy": "2024-09-26T14:51:49.749065Z", - "iopub.status.idle": "2024-09-26T14:51:49.763193Z", - "shell.execute_reply": "2024-09-26T14:51:49.762714Z" + "iopub.execute_input": "2024-09-26T16:47:26.706832Z", + "iopub.status.busy": "2024-09-26T16:47:26.706495Z", + "iopub.status.idle": "2024-09-26T16:47:26.719653Z", + "shell.execute_reply": "2024-09-26T16:47:26.719181Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.764801Z", - "iopub.status.busy": "2024-09-26T14:51:49.764624Z", - "iopub.status.idle": "2024-09-26T14:51:49.775091Z", - "shell.execute_reply": "2024-09-26T14:51:49.774491Z" + "iopub.execute_input": "2024-09-26T16:47:26.721290Z", + "iopub.status.busy": "2024-09-26T16:47:26.720960Z", + "iopub.status.idle": "2024-09-26T16:47:26.731265Z", + "shell.execute_reply": "2024-09-26T16:47:26.730702Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.777122Z", - "iopub.status.busy": "2024-09-26T14:51:49.776798Z", - "iopub.status.idle": "2024-09-26T14:51:49.786610Z", - "shell.execute_reply": "2024-09-26T14:51:49.786151Z" + "iopub.execute_input": "2024-09-26T16:47:26.732949Z", + "iopub.status.busy": "2024-09-26T16:47:26.732616Z", + "iopub.status.idle": "2024-09-26T16:47:26.741870Z", + "shell.execute_reply": "2024-09-26T16:47:26.741406Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.788278Z", - "iopub.status.busy": "2024-09-26T14:51:49.788101Z", - "iopub.status.idle": "2024-09-26T14:51:49.791818Z", - "shell.execute_reply": "2024-09-26T14:51:49.791364Z" + "iopub.execute_input": "2024-09-26T16:47:26.743488Z", + "iopub.status.busy": "2024-09-26T16:47:26.743171Z", + "iopub.status.idle": "2024-09-26T16:47:26.746914Z", + "shell.execute_reply": "2024-09-26T16:47:26.746344Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.793563Z", - "iopub.status.busy": "2024-09-26T14:51:49.793225Z", - "iopub.status.idle": "2024-09-26T14:51:49.849225Z", - "shell.execute_reply": "2024-09-26T14:51:49.848755Z" + "iopub.execute_input": "2024-09-26T16:47:26.748699Z", + "iopub.status.busy": "2024-09-26T16:47:26.748389Z", + "iopub.status.idle": "2024-09-26T16:47:26.799836Z", + "shell.execute_reply": "2024-09-26T16:47:26.799365Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.851334Z", - "iopub.status.busy": "2024-09-26T14:51:49.850848Z", - "iopub.status.idle": "2024-09-26T14:51:49.856692Z", - "shell.execute_reply": "2024-09-26T14:51:49.856243Z" + "iopub.execute_input": "2024-09-26T16:47:26.801753Z", + "iopub.status.busy": "2024-09-26T16:47:26.801330Z", + "iopub.status.idle": "2024-09-26T16:47:26.806943Z", + "shell.execute_reply": "2024-09-26T16:47:26.806477Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.858413Z", - "iopub.status.busy": "2024-09-26T14:51:49.858094Z", - "iopub.status.idle": "2024-09-26T14:51:49.869805Z", - "shell.execute_reply": "2024-09-26T14:51:49.869218Z" + "iopub.execute_input": "2024-09-26T16:47:26.808681Z", + "iopub.status.busy": "2024-09-26T16:47:26.808369Z", + "iopub.status.idle": "2024-09-26T16:47:26.818815Z", + "shell.execute_reply": "2024-09-26T16:47:26.818308Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.871476Z", - "iopub.status.busy": "2024-09-26T14:51:49.871161Z", - "iopub.status.idle": "2024-09-26T14:51:50.098032Z", - "shell.execute_reply": "2024-09-26T14:51:50.097456Z" + "iopub.execute_input": "2024-09-26T16:47:26.820494Z", + "iopub.status.busy": "2024-09-26T16:47:26.820161Z", + "iopub.status.idle": "2024-09-26T16:47:27.036629Z", + "shell.execute_reply": "2024-09-26T16:47:27.036055Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:50.099892Z", - "iopub.status.busy": "2024-09-26T14:51:50.099599Z", - "iopub.status.idle": "2024-09-26T14:51:50.107584Z", - "shell.execute_reply": "2024-09-26T14:51:50.107015Z" + "iopub.execute_input": "2024-09-26T16:47:27.038570Z", + "iopub.status.busy": "2024-09-26T16:47:27.038220Z", + "iopub.status.idle": "2024-09-26T16:47:27.046060Z", + "shell.execute_reply": "2024-09-26T16:47:27.045499Z" }, "nbsphinx": "hidden" }, @@ -3756,10 +3756,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:50.109288Z", - "iopub.status.busy": "2024-09-26T14:51:50.109111Z", - "iopub.status.idle": "2024-09-26T14:51:50.496608Z", - "shell.execute_reply": "2024-09-26T14:51:50.495787Z" + "iopub.execute_input": "2024-09-26T16:47:27.047997Z", + "iopub.status.busy": "2024-09-26T16:47:27.047729Z", + "iopub.status.idle": "2024-09-26T16:47:27.417036Z", + "shell.execute_reply": "2024-09-26T16:47:27.416327Z" } }, "outputs": [ @@ -3767,25 +3767,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-26 14:51:50-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", + "--2024-09-26 16:47:27-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...\r\n", "Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.\r\n", - "HTTP request sent, awaiting response... " + "HTTP request sent, awaiting response... 200 OK\r\n", + "Length: 986707 (964K) [application/zip]\r\n", + "Saving to: ‘CIFAR-10-subset.zip’\r\n", + "\r\n", + "\r", + "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s " ] }, { "name": "stdout", "output_type": "stream", "text": [ - "200 OK\r\n", - "Length: 986707 (964K) [application/zip]\r\n", - "Saving to: ‘CIFAR-10-subset.zip’\r\n", - "\r\n", "\r", - "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s \r", - "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.009s \r\n", + "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.03s \r\n", "\r\n", - "2024-09-26 14:51:50 (107 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", + "2024-09-26 16:47:27 (37.5 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", "\r\n" ] } @@ -3801,10 +3801,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:50.499275Z", - "iopub.status.busy": "2024-09-26T14:51:50.498755Z", - "iopub.status.idle": "2024-09-26T14:51:52.468119Z", - "shell.execute_reply": "2024-09-26T14:51:52.467505Z" + "iopub.execute_input": "2024-09-26T16:47:27.419484Z", + "iopub.status.busy": "2024-09-26T16:47:27.419066Z", + "iopub.status.idle": "2024-09-26T16:47:29.358979Z", + "shell.execute_reply": "2024-09-26T16:47:29.358404Z" } }, "outputs": [], @@ -3850,10 +3850,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:52.470295Z", - "iopub.status.busy": "2024-09-26T14:51:52.470006Z", - "iopub.status.idle": "2024-09-26T14:51:53.135612Z", - "shell.execute_reply": "2024-09-26T14:51:53.134933Z" + "iopub.execute_input": "2024-09-26T16:47:29.361270Z", + "iopub.status.busy": "2024-09-26T16:47:29.360836Z", + "iopub.status.idle": "2024-09-26T16:47:29.992524Z", + "shell.execute_reply": "2024-09-26T16:47:29.991934Z" } }, "outputs": [ @@ -3868,7 +3868,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "819cd513a50348b98c0ff3c8dd72c7bd", + "model_id": "a63bc3d27ea944c88b147aafb6e82f0b", "version_major": 2, "version_minor": 0 }, @@ -4008,10 +4008,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:53.138593Z", - "iopub.status.busy": "2024-09-26T14:51:53.138086Z", - "iopub.status.idle": "2024-09-26T14:51:53.152674Z", - "shell.execute_reply": "2024-09-26T14:51:53.152106Z" + "iopub.execute_input": "2024-09-26T16:47:29.994886Z", + "iopub.status.busy": "2024-09-26T16:47:29.994545Z", + "iopub.status.idle": "2024-09-26T16:47:30.007642Z", + "shell.execute_reply": "2024-09-26T16:47:30.007131Z" } }, "outputs": [ @@ -4257,10 +4257,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:53.155019Z", - "iopub.status.busy": "2024-09-26T14:51:53.154607Z", - "iopub.status.idle": "2024-09-26T14:51:53.305855Z", - "shell.execute_reply": "2024-09-26T14:51:53.305327Z" + "iopub.execute_input": "2024-09-26T16:47:30.009605Z", + "iopub.status.busy": "2024-09-26T16:47:30.009286Z", + "iopub.status.idle": "2024-09-26T16:47:30.156937Z", + "shell.execute_reply": "2024-09-26T16:47:30.156503Z" } }, "outputs": [ @@ -4325,10 +4325,10 @@ "execution_count": 38, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:53.308217Z", - "iopub.status.busy": "2024-09-26T14:51:53.307686Z", - "iopub.status.idle": "2024-09-26T14:51:53.823497Z", - "shell.execute_reply": "2024-09-26T14:51:53.822950Z" + "iopub.execute_input": "2024-09-26T16:47:30.158677Z", + "iopub.status.busy": "2024-09-26T16:47:30.158395Z", + "iopub.status.idle": "2024-09-26T16:47:30.654568Z", + "shell.execute_reply": "2024-09-26T16:47:30.653982Z" }, "nbsphinx": "hidden" }, @@ -4344,7 +4344,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac8a16cb60b04919938bc00b2f1342f7", + "model_id": "e7bd0490ff824dacacf614c61ccd5710", "version_major": 2, "version_minor": 0 }, @@ -4598,10 +4598,10 @@ "execution_count": 39, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:53.825382Z", - "iopub.status.busy": "2024-09-26T14:51:53.825164Z", - "iopub.status.idle": "2024-09-26T14:51:53.978845Z", - "shell.execute_reply": "2024-09-26T14:51:53.978305Z" + "iopub.execute_input": "2024-09-26T16:47:30.656660Z", + "iopub.status.busy": "2024-09-26T16:47:30.656182Z", + "iopub.status.idle": "2024-09-26T16:47:30.810730Z", + "shell.execute_reply": "2024-09-26T16:47:30.810054Z" }, "nbsphinx": "hidden" }, @@ -4653,7 +4653,30 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0cd32d52503a444d88252596c7202d70": { + "070068fc0b8a43bfa7abcf6ef25f3499": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_95db50532273485690ad795e902244aa", + "placeholder": "​", + "style": "IPY_MODEL_544dde097eb048b6ab0b9b4fe1f18d87", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 728.63it/s]" + } + }, + "32711dbdb3a2478390a85a6d959d0790": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4706,7 +4729,89 @@ "width": null } }, - "1e281a0c15b84c80941bc82a97097993": { + "37db36182460420491c4c85f86cfbf7f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_81b6b7e53ed74a599bbe694a4c8ebe13", + "placeholder": "​", + "style": "IPY_MODEL_9e1fc069d2f5460a953347e6697fec03", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "463a277a85a84149a358f0da4373af05": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "5122a12efb9046908f7d71a4dbc25479": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_32711dbdb3a2478390a85a6d959d0790", + "placeholder": "​", + "style": "IPY_MODEL_463a277a85a84149a358f0da4373af05", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "544dde097eb048b6ab0b9b4fe1f18d87": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "594b20897d9849efa4b8c38aaef6e976": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4759,7 +4864,7 @@ "width": null } }, - "2680a7b149fd4520bb536ef2dfbaa7c2": { + "6e563a24defc41f69c74511f93e3f124": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4812,7 +4917,7 @@ "width": null } }, - "2ea4ee3035ec4460b45119db4b86c88e": { + "81b6b7e53ed74a599bbe694a4c8ebe13": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4865,25 +4970,23 @@ "width": null } }, - "305f6e33eec84265b93317eacfe9b6b0": { + "8468e533025147e882087efd6a01fcf1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "31a345d79d134ece901060efdb94b165": { + "86ce6751f1af40208054f5f9ef8bc580": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4936,7 +5039,25 @@ "width": null } }, - "31b855e014d84579b8434de0e51b2846": { + "8fa4dec0afd24a3fafa704c37b9a96fd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "95db50532273485690ad795e902244aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4989,71 +5110,65 @@ "width": null } }, - "4a9cf0b92b274885a650766f05b77292": { + "9e1fc069d2f5460a953347e6697fec03": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_31b855e014d84579b8434de0e51b2846", - "placeholder": "​", - "style": "IPY_MODEL_77f44dc1b8c84ab4aef3e983303eb4a2", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 696.05it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "56f377970b0e41d0a3ab38bfadd0c51b": { + "a63bc3d27ea944c88b147aafb6e82f0b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2680a7b149fd4520bb536ef2dfbaa7c2", - "placeholder": "​", - "style": "IPY_MODEL_a9661f0b7a8d447c8c47dfe0b78f61ef", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_5122a12efb9046908f7d71a4dbc25479", + "IPY_MODEL_bfc0adceb176416a99a476073e670c8d", + "IPY_MODEL_c88a31f7518b412c8d3016b69370f673" + ], + "layout": "IPY_MODEL_cac7b4500c5543e7bcc09b5e5f741c43", "tabbable": null, - "tooltip": null, - "value": "100%" + "tooltip": null } }, - "59cfff3ec22848b6944ba0bf323bd24b": { + "bf0e05073eb84ed89e62523ce475978f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "5f01f9c656284899b3a91282330101c8": { + "bfc0adceb176416a99a476073e670c8d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5069,17 +5184,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_829ff0aad29b4270b40cd9499bc93cc7", + "layout": "IPY_MODEL_f808580136494741ad209b1398c34cb6", "max": 200.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_77633068b2ef4937bf213a3297280a11", + "style": "IPY_MODEL_bf0e05073eb84ed89e62523ce475978f", "tabbable": null, "tooltip": null, "value": 200.0 } }, - "769fa00f56a8417b92d2a48b7c419f62": { + "c88a31f7518b412c8d3016b69370f673": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5094,73 +5209,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_31a345d79d134ece901060efdb94b165", + "layout": "IPY_MODEL_86ce6751f1af40208054f5f9ef8bc580", "placeholder": "​", - "style": "IPY_MODEL_305f6e33eec84265b93317eacfe9b6b0", + "style": "IPY_MODEL_8fa4dec0afd24a3fafa704c37b9a96fd", "tabbable": null, "tooltip": null, - "value": " 200/200 [00:00<00:00, 735.07it/s]" - } - }, - "77633068b2ef4937bf213a3297280a11": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "77f44dc1b8c84ab4aef3e983303eb4a2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "819cd513a50348b98c0ff3c8dd72c7bd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_92ac731fb51746b9a27792595b65bd81", - "IPY_MODEL_e345abf69b7b4519a731b4da99441fe1", - "IPY_MODEL_769fa00f56a8417b92d2a48b7c419f62" - ], - "layout": "IPY_MODEL_2ea4ee3035ec4460b45119db4b86c88e", - "tabbable": null, - "tooltip": null + "value": " 200/200 [00:00<00:00, 796.34it/s]" } }, - "829ff0aad29b4270b40cd9499bc93cc7": { + "cac7b4500c5543e7bcc09b5e5f741c43": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5213,48 +5270,33 @@ "width": null } }, - "92ac731fb51746b9a27792595b65bd81": { + "d745bcc779404a6cb60ce4bfb57949f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0cd32d52503a444d88252596c7202d70", - "placeholder": "​", - "style": "IPY_MODEL_59cfff3ec22848b6944ba0bf323bd24b", + "layout": "IPY_MODEL_6e563a24defc41f69c74511f93e3f124", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8468e533025147e882087efd6a01fcf1", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "a9661f0b7a8d447c8c47dfe0b78f61ef": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": 200.0 } }, - "ac8a16cb60b04919938bc00b2f1342f7": { + "e7bd0490ff824dacacf614c61ccd5710": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5269,16 +5311,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_56f377970b0e41d0a3ab38bfadd0c51b", - "IPY_MODEL_5f01f9c656284899b3a91282330101c8", - "IPY_MODEL_4a9cf0b92b274885a650766f05b77292" + "IPY_MODEL_37db36182460420491c4c85f86cfbf7f", + "IPY_MODEL_d745bcc779404a6cb60ce4bfb57949f1", + "IPY_MODEL_070068fc0b8a43bfa7abcf6ef25f3499" ], - "layout": "IPY_MODEL_b805213db2034e85bcf8aa102ab8cd3c", + "layout": "IPY_MODEL_594b20897d9849efa4b8c38aaef6e976", "tabbable": null, "tooltip": null } }, - "b805213db2034e85bcf8aa102ab8cd3c": { + "f808580136494741ad209b1398c34cb6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5330,48 +5372,6 @@ "visibility": null, "width": null } - }, - "c28c38c2f92e4620b4be9878c38511ce": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "e345abf69b7b4519a731b4da99441fe1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1e281a0c15b84c80941bc82a97097993", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c28c38c2f92e4620b4be9878c38511ce", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index c8c374ab4..028fe5b51 100644 --- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:59.182546Z", - "iopub.status.busy": "2024-09-26T14:51:59.182366Z", - "iopub.status.idle": "2024-09-26T14:52:00.393643Z", - "shell.execute_reply": "2024-09-26T14:52:00.393076Z" + "iopub.execute_input": "2024-09-26T16:47:35.046121Z", + "iopub.status.busy": "2024-09-26T16:47:35.045646Z", + "iopub.status.idle": "2024-09-26T16:47:36.230177Z", + "shell.execute_reply": "2024-09-26T16:47:36.229531Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:00.395685Z", - "iopub.status.busy": "2024-09-26T14:52:00.395388Z", - "iopub.status.idle": "2024-09-26T14:52:00.398322Z", - "shell.execute_reply": "2024-09-26T14:52:00.397857Z" + "iopub.execute_input": "2024-09-26T16:47:36.232199Z", + "iopub.status.busy": "2024-09-26T16:47:36.231912Z", + "iopub.status.idle": "2024-09-26T16:47:36.234643Z", + "shell.execute_reply": "2024-09-26T16:47:36.234155Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:00.400144Z", - "iopub.status.busy": "2024-09-26T14:52:00.399840Z", - "iopub.status.idle": "2024-09-26T14:52:00.412193Z", - "shell.execute_reply": "2024-09-26T14:52:00.411697Z" + "iopub.execute_input": "2024-09-26T16:47:36.236403Z", + "iopub.status.busy": "2024-09-26T16:47:36.236223Z", + "iopub.status.idle": "2024-09-26T16:47:36.247957Z", + "shell.execute_reply": "2024-09-26T16:47:36.247513Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:00.414113Z", - "iopub.status.busy": "2024-09-26T14:52:00.413741Z", - "iopub.status.idle": "2024-09-26T14:52:05.730687Z", - "shell.execute_reply": "2024-09-26T14:52:05.730191Z" + "iopub.execute_input": "2024-09-26T16:47:36.249689Z", + "iopub.status.busy": "2024-09-26T16:47:36.249352Z", + "iopub.status.idle": "2024-09-26T16:47:40.785559Z", + "shell.execute_reply": "2024-09-26T16:47:40.785055Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index 6f20431e7..f6ca8c145 100644 --- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:08.034662Z", - "iopub.status.busy": "2024-09-26T14:52:08.034481Z", - "iopub.status.idle": "2024-09-26T14:52:09.304690Z", - "shell.execute_reply": "2024-09-26T14:52:09.304102Z" + "iopub.execute_input": "2024-09-26T16:47:42.997626Z", + "iopub.status.busy": "2024-09-26T16:47:42.997442Z", + "iopub.status.idle": "2024-09-26T16:47:44.237527Z", + "shell.execute_reply": "2024-09-26T16:47:44.236967Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:09.306879Z", - "iopub.status.busy": "2024-09-26T14:52:09.306585Z", - "iopub.status.idle": "2024-09-26T14:52:09.310196Z", - "shell.execute_reply": "2024-09-26T14:52:09.309631Z" + "iopub.execute_input": "2024-09-26T16:47:44.240051Z", + "iopub.status.busy": "2024-09-26T16:47:44.239587Z", + "iopub.status.idle": "2024-09-26T16:47:44.242836Z", + "shell.execute_reply": "2024-09-26T16:47:44.242370Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:09.311928Z", - "iopub.status.busy": "2024-09-26T14:52:09.311543Z", - "iopub.status.idle": "2024-09-26T14:52:12.757719Z", - "shell.execute_reply": "2024-09-26T14:52:12.756901Z" + "iopub.execute_input": "2024-09-26T16:47:44.244616Z", + "iopub.status.busy": "2024-09-26T16:47:44.244264Z", + "iopub.status.idle": "2024-09-26T16:47:47.619265Z", + "shell.execute_reply": "2024-09-26T16:47:47.618620Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.760355Z", - "iopub.status.busy": "2024-09-26T14:52:12.759696Z", - "iopub.status.idle": "2024-09-26T14:52:12.813184Z", - "shell.execute_reply": "2024-09-26T14:52:12.812421Z" + "iopub.execute_input": "2024-09-26T16:47:47.621966Z", + "iopub.status.busy": "2024-09-26T16:47:47.621155Z", + "iopub.status.idle": "2024-09-26T16:47:47.665245Z", + "shell.execute_reply": "2024-09-26T16:47:47.664606Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.815571Z", - "iopub.status.busy": "2024-09-26T14:52:12.815173Z", - "iopub.status.idle": "2024-09-26T14:52:12.861989Z", - "shell.execute_reply": "2024-09-26T14:52:12.861319Z" + "iopub.execute_input": "2024-09-26T16:47:47.667499Z", + "iopub.status.busy": "2024-09-26T16:47:47.667106Z", + "iopub.status.idle": "2024-09-26T16:47:47.708224Z", + "shell.execute_reply": "2024-09-26T16:47:47.707579Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.864397Z", - "iopub.status.busy": "2024-09-26T14:52:12.863906Z", - "iopub.status.idle": "2024-09-26T14:52:12.867232Z", - "shell.execute_reply": "2024-09-26T14:52:12.866761Z" + "iopub.execute_input": "2024-09-26T16:47:47.710601Z", + "iopub.status.busy": "2024-09-26T16:47:47.710188Z", + "iopub.status.idle": "2024-09-26T16:47:47.713698Z", + "shell.execute_reply": "2024-09-26T16:47:47.713139Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.868891Z", - "iopub.status.busy": "2024-09-26T14:52:12.868591Z", - "iopub.status.idle": "2024-09-26T14:52:12.871312Z", - "shell.execute_reply": "2024-09-26T14:52:12.870766Z" + "iopub.execute_input": "2024-09-26T16:47:47.715517Z", + "iopub.status.busy": "2024-09-26T16:47:47.715211Z", + "iopub.status.idle": "2024-09-26T16:47:47.717886Z", + "shell.execute_reply": "2024-09-26T16:47:47.717344Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.873230Z", - "iopub.status.busy": "2024-09-26T14:52:12.872884Z", - "iopub.status.idle": "2024-09-26T14:52:12.897801Z", - "shell.execute_reply": "2024-09-26T14:52:12.897165Z" + "iopub.execute_input": "2024-09-26T16:47:47.719539Z", + "iopub.status.busy": "2024-09-26T16:47:47.719373Z", + "iopub.status.idle": "2024-09-26T16:47:47.744161Z", + "shell.execute_reply": "2024-09-26T16:47:47.743608Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "554f0bffd2414657b0244763906a1e3d", + "model_id": "d619885062294fd49a18ec44c741a8f8", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d70c6118368a40e3b8c24ac57cc4db26", + "model_id": "77a64867c3e34ba7a1621aced3b7de9c", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.900530Z", - "iopub.status.busy": "2024-09-26T14:52:12.900181Z", - "iopub.status.idle": "2024-09-26T14:52:12.907197Z", - "shell.execute_reply": "2024-09-26T14:52:12.906763Z" + "iopub.execute_input": "2024-09-26T16:47:47.747161Z", + "iopub.status.busy": "2024-09-26T16:47:47.746829Z", + "iopub.status.idle": "2024-09-26T16:47:47.753312Z", + "shell.execute_reply": "2024-09-26T16:47:47.752894Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.908993Z", - "iopub.status.busy": "2024-09-26T14:52:12.908664Z", - "iopub.status.idle": "2024-09-26T14:52:12.911903Z", - "shell.execute_reply": "2024-09-26T14:52:12.911461Z" + "iopub.execute_input": "2024-09-26T16:47:47.755037Z", + "iopub.status.busy": "2024-09-26T16:47:47.754709Z", + "iopub.status.idle": "2024-09-26T16:47:47.758058Z", + "shell.execute_reply": "2024-09-26T16:47:47.757626Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.913714Z", - "iopub.status.busy": "2024-09-26T14:52:12.913385Z", - "iopub.status.idle": "2024-09-26T14:52:12.919520Z", - "shell.execute_reply": "2024-09-26T14:52:12.919085Z" + "iopub.execute_input": "2024-09-26T16:47:47.759672Z", + "iopub.status.busy": "2024-09-26T16:47:47.759381Z", + "iopub.status.idle": "2024-09-26T16:47:47.765633Z", + "shell.execute_reply": "2024-09-26T16:47:47.765099Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.921164Z", - "iopub.status.busy": "2024-09-26T14:52:12.920839Z", - "iopub.status.idle": "2024-09-26T14:52:12.968393Z", - "shell.execute_reply": "2024-09-26T14:52:12.967757Z" + "iopub.execute_input": "2024-09-26T16:47:47.767223Z", + "iopub.status.busy": "2024-09-26T16:47:47.766929Z", + "iopub.status.idle": "2024-09-26T16:47:47.810265Z", + "shell.execute_reply": "2024-09-26T16:47:47.809530Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.970571Z", - "iopub.status.busy": "2024-09-26T14:52:12.970308Z", - "iopub.status.idle": "2024-09-26T14:52:13.022776Z", - "shell.execute_reply": "2024-09-26T14:52:13.022011Z" + "iopub.execute_input": "2024-09-26T16:47:47.812866Z", + "iopub.status.busy": "2024-09-26T16:47:47.812464Z", + "iopub.status.idle": "2024-09-26T16:47:47.852851Z", + "shell.execute_reply": "2024-09-26T16:47:47.852088Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:13.025203Z", - "iopub.status.busy": "2024-09-26T14:52:13.024937Z", - "iopub.status.idle": "2024-09-26T14:52:13.170260Z", - "shell.execute_reply": "2024-09-26T14:52:13.169652Z" + "iopub.execute_input": "2024-09-26T16:47:47.855239Z", + "iopub.status.busy": "2024-09-26T16:47:47.855002Z", + "iopub.status.idle": "2024-09-26T16:47:47.983516Z", + "shell.execute_reply": "2024-09-26T16:47:47.982838Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:13.172750Z", - "iopub.status.busy": "2024-09-26T14:52:13.171949Z", - "iopub.status.idle": "2024-09-26T14:52:16.250921Z", - "shell.execute_reply": "2024-09-26T14:52:16.250318Z" + "iopub.execute_input": "2024-09-26T16:47:47.986081Z", + "iopub.status.busy": "2024-09-26T16:47:47.985286Z", + "iopub.status.idle": "2024-09-26T16:47:51.036136Z", + "shell.execute_reply": "2024-09-26T16:47:51.035477Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:16.253054Z", - "iopub.status.busy": "2024-09-26T14:52:16.252685Z", - "iopub.status.idle": "2024-09-26T14:52:16.313315Z", - "shell.execute_reply": "2024-09-26T14:52:16.312808Z" + "iopub.execute_input": "2024-09-26T16:47:51.038019Z", + "iopub.status.busy": "2024-09-26T16:47:51.037824Z", + "iopub.status.idle": "2024-09-26T16:47:51.097120Z", + "shell.execute_reply": "2024-09-26T16:47:51.096525Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:16.315165Z", - "iopub.status.busy": "2024-09-26T14:52:16.314831Z", - "iopub.status.idle": "2024-09-26T14:52:16.358568Z", - "shell.execute_reply": "2024-09-26T14:52:16.358096Z" + "iopub.execute_input": "2024-09-26T16:47:51.098978Z", + "iopub.status.busy": "2024-09-26T16:47:51.098793Z", + "iopub.status.idle": "2024-09-26T16:47:51.139818Z", + "shell.execute_reply": "2024-09-26T16:47:51.139259Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "52d078eb", + "id": "009eedf2", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "79b5500c", + "id": "61ca24ac", "metadata": {}, "source": [ "The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n", @@ -1338,7 +1338,7 @@ }, { "cell_type": "markdown", - "id": "f114fab1", + "id": "a456caf9", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "a6fcaf91", + "id": "8fb04f62", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:16.360590Z", - "iopub.status.busy": "2024-09-26T14:52:16.360173Z", - "iopub.status.idle": "2024-09-26T14:52:16.368057Z", - "shell.execute_reply": "2024-09-26T14:52:16.367484Z" + "iopub.execute_input": "2024-09-26T16:47:51.141760Z", + "iopub.status.busy": "2024-09-26T16:47:51.141422Z", + "iopub.status.idle": "2024-09-26T16:47:51.148936Z", + "shell.execute_reply": "2024-09-26T16:47:51.148467Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "fe87ea59", + "id": "c874c70e", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1472,13 +1472,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "6c7bf69f", + "id": "de0af888", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:16.369947Z", - "iopub.status.busy": "2024-09-26T14:52:16.369620Z", - "iopub.status.idle": "2024-09-26T14:52:16.389325Z", - "shell.execute_reply": "2024-09-26T14:52:16.388736Z" + "iopub.execute_input": "2024-09-26T16:47:51.150713Z", + "iopub.status.busy": "2024-09-26T16:47:51.150373Z", + "iopub.status.idle": "2024-09-26T16:47:51.168692Z", + "shell.execute_reply": "2024-09-26T16:47:51.168254Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "c73832aa", + "id": "abfd1ffb", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:16.391059Z", - "iopub.status.busy": "2024-09-26T14:52:16.390763Z", - "iopub.status.idle": "2024-09-26T14:52:16.394252Z", - "shell.execute_reply": "2024-09-26T14:52:16.393690Z" + "iopub.execute_input": "2024-09-26T16:47:51.170308Z", + "iopub.status.busy": "2024-09-26T16:47:51.169975Z", + "iopub.status.idle": "2024-09-26T16:47:51.173268Z", + "shell.execute_reply": "2024-09-26T16:47:51.172728Z" } }, "outputs": [ @@ -1622,25 +1622,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01c2b1694c624296b114bf1d67d63cff": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0b91ac3abe0a43e4b471a93ba3834871": { + "32e30b9f6ae44fe39cf7fc64a6d7ff35": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1693,25 +1675,7 @@ "width": null } }, - "1c5e90fb88e44d4280704d7ea69107fc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "1f603edcaca0493985e74b52602fd4e9": { + "367b8a8969924c6ebbc4a4e565e8f487": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1764,70 +1728,7 @@ "width": null } }, - "24e8217d30ae40ec8e547d17a79c5035": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4d41deb2547b43daa3622e6c0f359568": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_853967e0e61449b5b519cbd25c8830fc", - "placeholder": "​", - "style": "IPY_MODEL_70ee3acf89114ab190c8c886bcad952a", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: " - } - }, - "554f0bffd2414657b0244763906a1e3d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c8c65a735e994d1792a82e7140824616", - "IPY_MODEL_77dcb3bbbcad4620bbed6ca47c4c44db", - "IPY_MODEL_e241857383f5412490f7baca022471b6" - ], - "layout": "IPY_MODEL_af8c576b995749f49b6a3ff1a3ef7338", - "tabbable": null, - "tooltip": null - } - }, - "5569a8600bc64dfebf5774ddc1b6543c": { + "376ada55c7eb419fb04664279ec549c5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1880,48 +1781,7 @@ "width": null } }, - "70745b2f7b374dbb8bf3ac849b0ce45e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1f603edcaca0493985e74b52602fd4e9", - "placeholder": "​", - "style": "IPY_MODEL_01c2b1694c624296b114bf1d67d63cff", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1566266.10it/s]" - } - }, - "70ee3acf89114ab190c8c886bcad952a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "74ffd9bb94c34169aa80dd9d157cd10d": { + "399a4a8d381a40fbb3b3bd0966a71321": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1974,33 +1834,69 @@ "width": null } }, - "77dcb3bbbcad4620bbed6ca47c4c44db": { + "5e97117dd2b54f4bb848df455f52d734": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "5fbb63076efc4ca8a0390e49a60bcaef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c8f68f3e3778452dbb68e37345ea22a9", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c7a5057a16474398af969c555e555a5d", + "layout": "IPY_MODEL_399a4a8d381a40fbb3b3bd0966a71321", + "placeholder": "​", + "style": "IPY_MODEL_d5f8a8198b4742c8b970a37984360995", "tabbable": null, "tooltip": null, - "value": 50.0 + "value": " 10000/? [00:00<00:00, 1520336.38it/s]" + } + }, + "6148ee7e78ab462fbc2008172eaada4a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_367b8a8969924c6ebbc4a4e565e8f487", + "placeholder": "​", + "style": "IPY_MODEL_805e6cd3d19a431a8d3e7397034bb36f", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for checking labels: " } }, - "853967e0e61449b5b519cbd25c8830fc": { + "6eb939ae34e74aff8e1b4a61756cdd71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2053,7 +1949,7 @@ "width": null } }, - "af8c576b995749f49b6a3ff1a3ef7338": { + "746658ac11484b8798b05320b475118b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2106,23 +2002,95 @@ "width": null } }, - "c7a5057a16474398af969c555e555a5d": { + "7469e1771e8640a6b8e3d5abf60949df": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6eb939ae34e74aff8e1b4a61756cdd71", + "placeholder": "​", + "style": "IPY_MODEL_c1a195be8f6146beb8668dbc9343f0b9", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1085819.61it/s]" + } + }, + "77a64867c3e34ba7a1621aced3b7de9c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6148ee7e78ab462fbc2008172eaada4a", + "IPY_MODEL_a97461e5777d4317b73a65a404b18456", + "IPY_MODEL_5fbb63076efc4ca8a0390e49a60bcaef" + ], + "layout": "IPY_MODEL_c846b5991d5f4f62b33e17a83c611c2c", + "tabbable": null, + "tooltip": null + } + }, + "805e6cd3d19a431a8d3e7397034bb36f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "849c9ee3d4cf4d07acf4702cb9597a8b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_376ada55c7eb419fb04664279ec549c5", + "placeholder": "​", + "style": "IPY_MODEL_ddd711a084694a399e33772e83ec167c", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: " } }, - "c7fe0e5fdfd74b84af9f7585515c62f8": { + "a97461e5777d4317b73a65a404b18456": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2138,40 +2106,35 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_fead4528991a4aafb24e155e68de7bc9", + "layout": "IPY_MODEL_eca2c0ef792b4c58ae2be260c3551ba4", "max": 50.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_24e8217d30ae40ec8e547d17a79c5035", + "style": "IPY_MODEL_ce07695bcb5c44a48f65db8550cf5fd1", "tabbable": null, "tooltip": null, "value": 50.0 } }, - "c8c65a735e994d1792a82e7140824616": { + "c1a195be8f6146beb8668dbc9343f0b9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0b91ac3abe0a43e4b471a93ba3834871", - "placeholder": "​", - "style": "IPY_MODEL_1c5e90fb88e44d4280704d7ea69107fc", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: " + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c8f68f3e3778452dbb68e37345ea22a9": { + "c846b5991d5f4f62b33e17a83c611c2c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2224,54 +2187,91 @@ "width": null } }, - "d70c6118368a40e3b8c24ac57cc4db26": { + "c8e843b08dfb48f79b4aa5ff0094cd67": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4d41deb2547b43daa3622e6c0f359568", - "IPY_MODEL_c7fe0e5fdfd74b84af9f7585515c62f8", - "IPY_MODEL_70745b2f7b374dbb8bf3ac849b0ce45e" - ], - "layout": "IPY_MODEL_74ffd9bb94c34169aa80dd9d157cd10d", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_746658ac11484b8798b05320b475118b", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5e97117dd2b54f4bb848df455f52d734", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 50.0 } }, - "e241857383f5412490f7baca022471b6": { + "ce07695bcb5c44a48f65db8550cf5fd1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "d5f8a8198b4742c8b970a37984360995": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d619885062294fd49a18ec44c741a8f8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5569a8600bc64dfebf5774ddc1b6543c", - "placeholder": "​", - "style": "IPY_MODEL_f6897d47a5094424a94e9a8a0a058c31", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_849c9ee3d4cf4d07acf4702cb9597a8b", + "IPY_MODEL_c8e843b08dfb48f79b4aa5ff0094cd67", + "IPY_MODEL_7469e1771e8640a6b8e3d5abf60949df" + ], + "layout": "IPY_MODEL_32e30b9f6ae44fe39cf7fc64a6d7ff35", "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1012407.73it/s]" + "tooltip": null } }, - "f6897d47a5094424a94e9a8a0a058c31": { + "ddd711a084694a399e33772e83ec167c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2289,7 +2289,7 @@ "text_color": null } }, - "fead4528991a4aafb24e155e68de7bc9": { + "eca2c0ef792b4c58ae2be260c3551ba4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb index d59d7a26c..f656fc151 100644 --- a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb @@ -60,10 +60,10 @@ "id": "2d638465", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:19.810405Z", - "iopub.status.busy": "2024-09-26T14:52:19.810223Z", - "iopub.status.idle": "2024-09-26T14:52:21.040404Z", - "shell.execute_reply": "2024-09-26T14:52:21.039829Z" + "iopub.execute_input": "2024-09-26T16:47:54.535195Z", + "iopub.status.busy": "2024-09-26T16:47:54.535006Z", + "iopub.status.idle": "2024-09-26T16:47:55.733521Z", + "shell.execute_reply": "2024-09-26T16:47:55.732967Z" }, "nbsphinx": "hidden" }, @@ -73,7 +73,7 @@ "dependencies = [\"cleanlab\", \"xgboost\", \"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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -99,10 +99,10 @@ "id": "b0bbf715-47c6-44ea-b15e-89800e62ee04", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.042734Z", - "iopub.status.busy": "2024-09-26T14:52:21.042166Z", - "iopub.status.idle": "2024-09-26T14:52:21.046124Z", - "shell.execute_reply": "2024-09-26T14:52:21.045639Z" + "iopub.execute_input": "2024-09-26T16:47:55.735716Z", + "iopub.status.busy": "2024-09-26T16:47:55.735319Z", + "iopub.status.idle": "2024-09-26T16:47:55.738921Z", + "shell.execute_reply": "2024-09-26T16:47:55.738470Z" } }, "outputs": [], @@ -140,10 +140,10 @@ "id": "c58f8015-d051-411c-9e03-5659cf3ad956", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.047800Z", - "iopub.status.busy": "2024-09-26T14:52:21.047493Z", - "iopub.status.idle": "2024-09-26T14:52:21.500478Z", - "shell.execute_reply": "2024-09-26T14:52:21.499906Z" + "iopub.execute_input": "2024-09-26T16:47:55.740620Z", + "iopub.status.busy": "2024-09-26T16:47:55.740276Z", + "iopub.status.idle": "2024-09-26T16:47:56.033739Z", + "shell.execute_reply": "2024-09-26T16:47:56.033249Z" } }, "outputs": [ @@ -273,10 +273,10 @@ "id": "1b5f50e6-d125-4e61-b63e-4004f0c9099a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.502342Z", - "iopub.status.busy": "2024-09-26T14:52:21.502065Z", - "iopub.status.idle": "2024-09-26T14:52:21.509359Z", - "shell.execute_reply": "2024-09-26T14:52:21.508870Z" + "iopub.execute_input": "2024-09-26T16:47:56.035764Z", + "iopub.status.busy": "2024-09-26T16:47:56.035394Z", + "iopub.status.idle": "2024-09-26T16:47:56.042905Z", + "shell.execute_reply": "2024-09-26T16:47:56.042423Z" } }, "outputs": [], @@ -312,10 +312,10 @@ "id": "a36c21e9-1c32-4df9-bd87-fffeb8c2175f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.511294Z", - "iopub.status.busy": "2024-09-26T14:52:21.510958Z", - "iopub.status.idle": "2024-09-26T14:52:21.518230Z", - "shell.execute_reply": "2024-09-26T14:52:21.517794Z" + "iopub.execute_input": "2024-09-26T16:47:56.044596Z", + "iopub.status.busy": "2024-09-26T16:47:56.044248Z", + "iopub.status.idle": "2024-09-26T16:47:56.051192Z", + "shell.execute_reply": "2024-09-26T16:47:56.050612Z" } }, "outputs": [ @@ -418,10 +418,10 @@ "id": "5f856a3a-8aae-4836-b146-9ab68d8d1c7a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.520016Z", - "iopub.status.busy": "2024-09-26T14:52:21.519670Z", - "iopub.status.idle": "2024-09-26T14:52:21.524522Z", - "shell.execute_reply": "2024-09-26T14:52:21.524038Z" + "iopub.execute_input": "2024-09-26T16:47:56.053068Z", + "iopub.status.busy": "2024-09-26T16:47:56.052727Z", + "iopub.status.idle": "2024-09-26T16:47:56.057302Z", + "shell.execute_reply": "2024-09-26T16:47:56.056846Z" } }, "outputs": [], @@ -449,10 +449,10 @@ "id": "46275634-da56-4e58-9061-8108be2b585d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.526279Z", - "iopub.status.busy": "2024-09-26T14:52:21.525942Z", - "iopub.status.idle": "2024-09-26T14:52:21.531374Z", - "shell.execute_reply": "2024-09-26T14:52:21.530921Z" + "iopub.execute_input": "2024-09-26T16:47:56.058992Z", + "iopub.status.busy": "2024-09-26T16:47:56.058659Z", + "iopub.status.idle": "2024-09-26T16:47:56.064036Z", + "shell.execute_reply": "2024-09-26T16:47:56.063598Z" } }, "outputs": [], @@ -488,10 +488,10 @@ "id": "769c4c5e-a7ff-4e02-bee5-2b2e676aec14", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.533093Z", - "iopub.status.busy": "2024-09-26T14:52:21.532754Z", - "iopub.status.idle": "2024-09-26T14:52:21.536654Z", - "shell.execute_reply": "2024-09-26T14:52:21.536203Z" + "iopub.execute_input": "2024-09-26T16:47:56.065679Z", + "iopub.status.busy": "2024-09-26T16:47:56.065338Z", + "iopub.status.idle": "2024-09-26T16:47:56.069491Z", + "shell.execute_reply": "2024-09-26T16:47:56.068945Z" } }, "outputs": [], @@ -506,10 +506,10 @@ "id": "7ac47c3d-9e87-45b7-9064-bfa45578872e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.538466Z", - "iopub.status.busy": "2024-09-26T14:52:21.538138Z", - "iopub.status.idle": "2024-09-26T14:52:21.605533Z", - "shell.execute_reply": "2024-09-26T14:52:21.604911Z" + "iopub.execute_input": "2024-09-26T16:47:56.071256Z", + "iopub.status.busy": "2024-09-26T16:47:56.070925Z", + "iopub.status.idle": "2024-09-26T16:47:56.137855Z", + "shell.execute_reply": "2024-09-26T16:47:56.137165Z" } }, "outputs": [ @@ -609,10 +609,10 @@ "id": "6cef169e-d15b-4d18-9cb7-8ea589557e6b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.608178Z", - "iopub.status.busy": "2024-09-26T14:52:21.607735Z", - "iopub.status.idle": "2024-09-26T14:52:21.620493Z", - "shell.execute_reply": "2024-09-26T14:52:21.619924Z" + "iopub.execute_input": "2024-09-26T16:47:56.140117Z", + "iopub.status.busy": "2024-09-26T16:47:56.139904Z", + "iopub.status.idle": "2024-09-26T16:47:56.151148Z", + "shell.execute_reply": "2024-09-26T16:47:56.150548Z" } }, "outputs": [ @@ -724,10 +724,10 @@ "id": "b68e0418-86cf-431f-9107-2dd0a310ca42", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.623400Z", - "iopub.status.busy": "2024-09-26T14:52:21.622546Z", - "iopub.status.idle": "2024-09-26T14:52:21.644716Z", - "shell.execute_reply": "2024-09-26T14:52:21.644193Z" + "iopub.execute_input": "2024-09-26T16:47:56.153073Z", + "iopub.status.busy": "2024-09-26T16:47:56.152869Z", + "iopub.status.idle": "2024-09-26T16:47:56.173390Z", + "shell.execute_reply": "2024-09-26T16:47:56.172785Z" } }, "outputs": [ @@ -931,10 +931,10 @@ "id": "0e9bd131-429f-48af-b4fc-ed8b907950b9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.647639Z", - "iopub.status.busy": "2024-09-26T14:52:21.646753Z", - "iopub.status.idle": "2024-09-26T14:52:21.652233Z", - "shell.execute_reply": "2024-09-26T14:52:21.651741Z" + "iopub.execute_input": "2024-09-26T16:47:56.175433Z", + "iopub.status.busy": "2024-09-26T16:47:56.175226Z", + "iopub.status.idle": "2024-09-26T16:47:56.180488Z", + "shell.execute_reply": "2024-09-26T16:47:56.179989Z" } }, "outputs": [ @@ -968,10 +968,10 @@ "id": "e72320ec-7792-4347-b2fb-630f2519127c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.654600Z", - "iopub.status.busy": "2024-09-26T14:52:21.654175Z", - "iopub.status.idle": "2024-09-26T14:52:21.659391Z", - "shell.execute_reply": "2024-09-26T14:52:21.658868Z" + "iopub.execute_input": "2024-09-26T16:47:56.183374Z", + "iopub.status.busy": "2024-09-26T16:47:56.182610Z", + "iopub.status.idle": "2024-09-26T16:47:56.188232Z", + "shell.execute_reply": "2024-09-26T16:47:56.187733Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "id": "8520ba4a-3ad6-408a-b377-3f47c32d745a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.661608Z", - "iopub.status.busy": "2024-09-26T14:52:21.661407Z", - "iopub.status.idle": "2024-09-26T14:52:21.671252Z", - "shell.execute_reply": "2024-09-26T14:52:21.670825Z" + "iopub.execute_input": "2024-09-26T16:47:56.191098Z", + "iopub.status.busy": "2024-09-26T16:47:56.190327Z", + "iopub.status.idle": "2024-09-26T16:47:56.201327Z", + "shell.execute_reply": "2024-09-26T16:47:56.200924Z" } }, "outputs": [ @@ -1205,10 +1205,10 @@ "id": "3c002665-c48b-4f04-91f7-ad112a49efc7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.673132Z", - "iopub.status.busy": "2024-09-26T14:52:21.672789Z", - "iopub.status.idle": "2024-09-26T14:52:21.677167Z", - "shell.execute_reply": "2024-09-26T14:52:21.676751Z" + "iopub.execute_input": "2024-09-26T16:47:56.203402Z", + "iopub.status.busy": "2024-09-26T16:47:56.202954Z", + "iopub.status.idle": "2024-09-26T16:47:56.207388Z", + "shell.execute_reply": "2024-09-26T16:47:56.206865Z" } }, "outputs": [], @@ -1234,10 +1234,10 @@ "id": "36319f39-f563-4f63-913f-821373180350", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.678723Z", - "iopub.status.busy": "2024-09-26T14:52:21.678550Z", - "iopub.status.idle": "2024-09-26T14:52:21.827660Z", - "shell.execute_reply": "2024-09-26T14:52:21.827142Z" + "iopub.execute_input": "2024-09-26T16:47:56.209276Z", + "iopub.status.busy": "2024-09-26T16:47:56.208859Z", + "iopub.status.idle": "2024-09-26T16:47:56.338295Z", + "shell.execute_reply": "2024-09-26T16:47:56.337800Z" } }, "outputs": [ @@ -1711,10 +1711,10 @@ "id": "044c0eb1-299a-4851-b1bf-268d5bce56c1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.829459Z", - "iopub.status.busy": "2024-09-26T14:52:21.829100Z", - "iopub.status.idle": "2024-09-26T14:52:21.835627Z", - "shell.execute_reply": "2024-09-26T14:52:21.835049Z" + "iopub.execute_input": "2024-09-26T16:47:56.340280Z", + "iopub.status.busy": "2024-09-26T16:47:56.339886Z", + "iopub.status.idle": "2024-09-26T16:47:56.346194Z", + "shell.execute_reply": "2024-09-26T16:47:56.345619Z" } }, "outputs": [], @@ -1738,10 +1738,10 @@ "id": "c43df278-abfe-40e5-9d48-2df3efea9379", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.837607Z", - "iopub.status.busy": "2024-09-26T14:52:21.837231Z", - "iopub.status.idle": "2024-09-26T14:52:23.851625Z", - "shell.execute_reply": "2024-09-26T14:52:23.850969Z" + "iopub.execute_input": "2024-09-26T16:47:56.348028Z", + "iopub.status.busy": "2024-09-26T16:47:56.347737Z", + "iopub.status.idle": "2024-09-26T16:47:58.347461Z", + "shell.execute_reply": "2024-09-26T16:47:58.346802Z" } }, "outputs": [ @@ -1953,10 +1953,10 @@ "id": "77c7f776-54b3-45b5-9207-715d6d2e90c0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:23.853998Z", - "iopub.status.busy": "2024-09-26T14:52:23.853506Z", - "iopub.status.idle": "2024-09-26T14:52:23.867378Z", - "shell.execute_reply": "2024-09-26T14:52:23.866868Z" + "iopub.execute_input": "2024-09-26T16:47:58.351104Z", + "iopub.status.busy": "2024-09-26T16:47:58.350154Z", + "iopub.status.idle": "2024-09-26T16:47:58.364975Z", + "shell.execute_reply": "2024-09-26T16:47:58.364458Z" } }, "outputs": [ @@ -2073,10 +2073,10 @@ "id": "7e218d04-0729-4f42-b264-51c73601ebe6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:23.869442Z", - "iopub.status.busy": "2024-09-26T14:52:23.869086Z", - "iopub.status.idle": "2024-09-26T14:52:23.871992Z", - "shell.execute_reply": "2024-09-26T14:52:23.871490Z" + "iopub.execute_input": "2024-09-26T16:47:58.368010Z", + "iopub.status.busy": "2024-09-26T16:47:58.367231Z", + "iopub.status.idle": "2024-09-26T16:47:58.370949Z", + "shell.execute_reply": "2024-09-26T16:47:58.370432Z" } }, "outputs": [], @@ -2090,10 +2090,10 @@ "id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:23.873901Z", - "iopub.status.busy": "2024-09-26T14:52:23.873567Z", - "iopub.status.idle": "2024-09-26T14:52:23.878299Z", - "shell.execute_reply": "2024-09-26T14:52:23.877773Z" + "iopub.execute_input": "2024-09-26T16:47:58.373819Z", + "iopub.status.busy": "2024-09-26T16:47:58.373057Z", + "iopub.status.idle": "2024-09-26T16:47:58.378326Z", + "shell.execute_reply": "2024-09-26T16:47:58.377824Z" } }, "outputs": [], @@ -2117,10 +2117,10 @@ "id": "5ce2d89f-e832-448d-bfac-9941da15c895", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:23.880472Z", - "iopub.status.busy": "2024-09-26T14:52:23.880009Z", - "iopub.status.idle": "2024-09-26T14:52:23.917031Z", - "shell.execute_reply": "2024-09-26T14:52:23.916497Z" + "iopub.execute_input": "2024-09-26T16:47:58.381268Z", + "iopub.status.busy": "2024-09-26T16:47:58.380485Z", + "iopub.status.idle": "2024-09-26T16:47:58.395696Z", + "shell.execute_reply": "2024-09-26T16:47:58.395139Z" } }, "outputs": [ @@ -2160,10 +2160,10 @@ "id": "9f437756-112e-4531-84fc-6ceadd0c9ef5", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:23.919143Z", - "iopub.status.busy": "2024-09-26T14:52:23.918754Z", - "iopub.status.idle": "2024-09-26T14:52:24.441145Z", - "shell.execute_reply": "2024-09-26T14:52:24.440578Z" + "iopub.execute_input": "2024-09-26T16:47:58.397886Z", + "iopub.status.busy": "2024-09-26T16:47:58.397509Z", + "iopub.status.idle": "2024-09-26T16:47:58.914150Z", + "shell.execute_reply": "2024-09-26T16:47:58.913545Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "id": "707625f6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.443535Z", - "iopub.status.busy": "2024-09-26T14:52:24.443148Z", - "iopub.status.idle": "2024-09-26T14:52:24.581215Z", - "shell.execute_reply": "2024-09-26T14:52:24.580592Z" + "iopub.execute_input": "2024-09-26T16:47:58.917583Z", + "iopub.status.busy": "2024-09-26T16:47:58.916599Z", + "iopub.status.idle": "2024-09-26T16:47:59.051939Z", + "shell.execute_reply": "2024-09-26T16:47:59.051264Z" } }, "outputs": [ @@ -2408,10 +2408,10 @@ "id": "25afe46c-a521-483c-b168-728c76d970dc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.583982Z", - "iopub.status.busy": "2024-09-26T14:52:24.583021Z", - "iopub.status.idle": "2024-09-26T14:52:24.591560Z", - "shell.execute_reply": "2024-09-26T14:52:24.591052Z" + "iopub.execute_input": "2024-09-26T16:47:59.055010Z", + "iopub.status.busy": "2024-09-26T16:47:59.054194Z", + "iopub.status.idle": "2024-09-26T16:47:59.062555Z", + "shell.execute_reply": "2024-09-26T16:47:59.062018Z" } }, "outputs": [ @@ -2441,10 +2441,10 @@ "id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.594472Z", - "iopub.status.busy": "2024-09-26T14:52:24.593722Z", - "iopub.status.idle": "2024-09-26T14:52:24.601463Z", - "shell.execute_reply": "2024-09-26T14:52:24.600918Z" + "iopub.execute_input": "2024-09-26T16:47:59.065453Z", + "iopub.status.busy": "2024-09-26T16:47:59.064693Z", + "iopub.status.idle": "2024-09-26T16:47:59.072235Z", + "shell.execute_reply": "2024-09-26T16:47:59.071728Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.604404Z", - "iopub.status.busy": "2024-09-26T14:52:24.603652Z", - "iopub.status.idle": "2024-09-26T14:52:24.610627Z", - "shell.execute_reply": "2024-09-26T14:52:24.610123Z" + "iopub.execute_input": "2024-09-26T16:47:59.075116Z", + "iopub.status.busy": "2024-09-26T16:47:59.074334Z", + "iopub.status.idle": "2024-09-26T16:47:59.081249Z", + "shell.execute_reply": "2024-09-26T16:47:59.080749Z" } }, "outputs": [ @@ -2513,10 +2513,10 @@ "id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.613514Z", - "iopub.status.busy": "2024-09-26T14:52:24.612748Z", - "iopub.status.idle": "2024-09-26T14:52:24.618379Z", - "shell.execute_reply": "2024-09-26T14:52:24.617862Z" + "iopub.execute_input": "2024-09-26T16:47:59.084123Z", + "iopub.status.busy": "2024-09-26T16:47:59.083369Z", + "iopub.status.idle": "2024-09-26T16:47:59.089039Z", + "shell.execute_reply": "2024-09-26T16:47:59.088466Z" } }, "outputs": [ @@ -2542,10 +2542,10 @@ "id": "08080458-0cd7-447d-80e6-384cb8d31eaf", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.621206Z", - "iopub.status.busy": "2024-09-26T14:52:24.620459Z", - "iopub.status.idle": "2024-09-26T14:52:24.625372Z", - "shell.execute_reply": "2024-09-26T14:52:24.624794Z" + "iopub.execute_input": "2024-09-26T16:47:59.092213Z", + "iopub.status.busy": "2024-09-26T16:47:59.091430Z", + "iopub.status.idle": "2024-09-26T16:47:59.096568Z", + "shell.execute_reply": "2024-09-26T16:47:59.096115Z" } }, "outputs": [], @@ -2569,10 +2569,10 @@ "id": "009bb215-4d26-47da-a230-d0ccf4122629", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.627070Z", - "iopub.status.busy": "2024-09-26T14:52:24.626899Z", - "iopub.status.idle": "2024-09-26T14:52:24.703448Z", - "shell.execute_reply": "2024-09-26T14:52:24.702825Z" + "iopub.execute_input": "2024-09-26T16:47:59.098276Z", + "iopub.status.busy": "2024-09-26T16:47:59.097990Z", + "iopub.status.idle": "2024-09-26T16:47:59.172717Z", + "shell.execute_reply": "2024-09-26T16:47:59.172212Z" } }, "outputs": [ @@ -3052,10 +3052,10 @@ "id": "dcaeda51-9b24-4c04-889d-7e63563594fc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.705665Z", - "iopub.status.busy": "2024-09-26T14:52:24.705281Z", - "iopub.status.idle": "2024-09-26T14:52:24.718371Z", - "shell.execute_reply": "2024-09-26T14:52:24.717910Z" + "iopub.execute_input": "2024-09-26T16:47:59.174408Z", + "iopub.status.busy": "2024-09-26T16:47:59.174231Z", + "iopub.status.idle": "2024-09-26T16:47:59.187866Z", + "shell.execute_reply": "2024-09-26T16:47:59.187306Z" } }, "outputs": [ @@ -3111,10 +3111,10 @@ "id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.719953Z", - "iopub.status.busy": "2024-09-26T14:52:24.719774Z", - "iopub.status.idle": "2024-09-26T14:52:24.722525Z", - "shell.execute_reply": "2024-09-26T14:52:24.721993Z" + "iopub.execute_input": "2024-09-26T16:47:59.190093Z", + "iopub.status.busy": "2024-09-26T16:47:59.189785Z", + "iopub.status.idle": "2024-09-26T16:47:59.192749Z", + "shell.execute_reply": "2024-09-26T16:47:59.192221Z" } }, "outputs": [], @@ -3150,10 +3150,10 @@ "id": "941ab2a6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.724217Z", - "iopub.status.busy": "2024-09-26T14:52:24.723890Z", - "iopub.status.idle": "2024-09-26T14:52:24.733856Z", - "shell.execute_reply": "2024-09-26T14:52:24.733386Z" + "iopub.execute_input": "2024-09-26T16:47:59.194421Z", + "iopub.status.busy": "2024-09-26T16:47:59.194249Z", + "iopub.status.idle": "2024-09-26T16:47:59.204777Z", + "shell.execute_reply": "2024-09-26T16:47:59.204208Z" } }, "outputs": [], @@ -3261,10 +3261,10 @@ "id": "50666fb9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.735568Z", - "iopub.status.busy": "2024-09-26T14:52:24.735390Z", - "iopub.status.idle": "2024-09-26T14:52:24.741960Z", - "shell.execute_reply": "2024-09-26T14:52:24.741508Z" + "iopub.execute_input": "2024-09-26T16:47:59.206629Z", + "iopub.status.busy": "2024-09-26T16:47:59.206434Z", + "iopub.status.idle": "2024-09-26T16:47:59.212877Z", + "shell.execute_reply": "2024-09-26T16:47:59.212424Z" }, "nbsphinx": "hidden" }, @@ -3346,10 +3346,10 @@ "id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.743631Z", - "iopub.status.busy": "2024-09-26T14:52:24.743288Z", - "iopub.status.idle": "2024-09-26T14:52:24.746500Z", - "shell.execute_reply": "2024-09-26T14:52:24.746046Z" + "iopub.execute_input": "2024-09-26T16:47:59.214539Z", + "iopub.status.busy": "2024-09-26T16:47:59.214194Z", + "iopub.status.idle": "2024-09-26T16:47:59.217309Z", + "shell.execute_reply": "2024-09-26T16:47:59.216867Z" } }, "outputs": [], @@ -3373,10 +3373,10 @@ "id": "ce1c0ada-88b1-4654-b43f-3c0b59002979", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.748147Z", - "iopub.status.busy": "2024-09-26T14:52:24.747796Z", - "iopub.status.idle": "2024-09-26T14:52:28.830714Z", - "shell.execute_reply": "2024-09-26T14:52:28.830201Z" + "iopub.execute_input": "2024-09-26T16:47:59.218943Z", + "iopub.status.busy": "2024-09-26T16:47:59.218613Z", + "iopub.status.idle": "2024-09-26T16:48:03.194978Z", + "shell.execute_reply": "2024-09-26T16:48:03.194447Z" } }, "outputs": [ @@ -3419,10 +3419,10 @@ "id": "3f572acf-31c3-4874-9100-451796e35b06", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:28.832745Z", - "iopub.status.busy": "2024-09-26T14:52:28.832361Z", - "iopub.status.idle": "2024-09-26T14:52:28.835718Z", - "shell.execute_reply": "2024-09-26T14:52:28.835165Z" + "iopub.execute_input": "2024-09-26T16:48:03.197052Z", + "iopub.status.busy": "2024-09-26T16:48:03.196643Z", + "iopub.status.idle": "2024-09-26T16:48:03.200280Z", + "shell.execute_reply": "2024-09-26T16:48:03.199664Z" } }, "outputs": [ @@ -3460,10 +3460,10 @@ "id": "6a025a88", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:28.837752Z", - "iopub.status.busy": "2024-09-26T14:52:28.837357Z", - "iopub.status.idle": "2024-09-26T14:52:28.840312Z", - "shell.execute_reply": "2024-09-26T14:52:28.839737Z" + "iopub.execute_input": "2024-09-26T16:48:03.202401Z", + "iopub.status.busy": "2024-09-26T16:48:03.201997Z", + "iopub.status.idle": "2024-09-26T16:48:03.204987Z", + "shell.execute_reply": "2024-09-26T16:48:03.204434Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index f81022c48..bc30e9d38 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": "2024-09-26T14:52:32.169125Z", - "iopub.status.busy": "2024-09-26T14:52:32.168956Z", - "iopub.status.idle": "2024-09-26T14:52:33.431499Z", - "shell.execute_reply": "2024-09-26T14:52:33.430884Z" + "iopub.execute_input": "2024-09-26T16:48:06.553023Z", + "iopub.status.busy": "2024-09-26T16:48:06.552853Z", + "iopub.status.idle": "2024-09-26T16:48:07.795130Z", + "shell.execute_reply": "2024-09-26T16:48:07.794475Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:52:33.434100Z", - "iopub.status.busy": "2024-09-26T14:52:33.433789Z", - "iopub.status.idle": "2024-09-26T14:52:33.621182Z", - "shell.execute_reply": "2024-09-26T14:52:33.620609Z" + "iopub.execute_input": "2024-09-26T16:48:07.797242Z", + "iopub.status.busy": "2024-09-26T16:48:07.796966Z", + "iopub.status.idle": "2024-09-26T16:48:07.974981Z", + "shell.execute_reply": "2024-09-26T16:48:07.974472Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:33.623560Z", - "iopub.status.busy": "2024-09-26T14:52:33.623109Z", - "iopub.status.idle": "2024-09-26T14:52:33.635370Z", - "shell.execute_reply": "2024-09-26T14:52:33.634790Z" + "iopub.execute_input": "2024-09-26T16:48:07.977164Z", + "iopub.status.busy": "2024-09-26T16:48:07.976814Z", + "iopub.status.idle": "2024-09-26T16:48:07.988584Z", + "shell.execute_reply": "2024-09-26T16:48:07.988135Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:33.637192Z", - "iopub.status.busy": "2024-09-26T14:52:33.636918Z", - "iopub.status.idle": "2024-09-26T14:52:33.875845Z", - "shell.execute_reply": "2024-09-26T14:52:33.875224Z" + "iopub.execute_input": "2024-09-26T16:48:07.990211Z", + "iopub.status.busy": "2024-09-26T16:48:07.990037Z", + "iopub.status.idle": "2024-09-26T16:48:08.228312Z", + "shell.execute_reply": "2024-09-26T16:48:08.227796Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:33.877984Z", - "iopub.status.busy": "2024-09-26T14:52:33.877640Z", - "iopub.status.idle": "2024-09-26T14:52:33.905047Z", - "shell.execute_reply": "2024-09-26T14:52:33.904562Z" + "iopub.execute_input": "2024-09-26T16:48:08.230136Z", + "iopub.status.busy": "2024-09-26T16:48:08.229942Z", + "iopub.status.idle": "2024-09-26T16:48:08.256644Z", + "shell.execute_reply": "2024-09-26T16:48:08.256184Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:33.906945Z", - "iopub.status.busy": "2024-09-26T14:52:33.906618Z", - "iopub.status.idle": "2024-09-26T14:52:36.066124Z", - "shell.execute_reply": "2024-09-26T14:52:36.065509Z" + "iopub.execute_input": "2024-09-26T16:48:08.258377Z", + "iopub.status.busy": "2024-09-26T16:48:08.258198Z", + "iopub.status.idle": "2024-09-26T16:48:10.362194Z", + "shell.execute_reply": "2024-09-26T16:48:10.361482Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:36.068327Z", - "iopub.status.busy": "2024-09-26T14:52:36.067791Z", - "iopub.status.idle": "2024-09-26T14:52:36.085955Z", - "shell.execute_reply": "2024-09-26T14:52:36.085444Z" + "iopub.execute_input": "2024-09-26T16:48:10.364420Z", + "iopub.status.busy": "2024-09-26T16:48:10.364067Z", + "iopub.status.idle": "2024-09-26T16:48:10.382576Z", + "shell.execute_reply": "2024-09-26T16:48:10.382074Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:36.087636Z", - "iopub.status.busy": "2024-09-26T14:52:36.087436Z", - "iopub.status.idle": "2024-09-26T14:52:37.714159Z", - "shell.execute_reply": "2024-09-26T14:52:37.713482Z" + "iopub.execute_input": "2024-09-26T16:48:10.384193Z", + "iopub.status.busy": "2024-09-26T16:48:10.384006Z", + "iopub.status.idle": "2024-09-26T16:48:11.969211Z", + "shell.execute_reply": "2024-09-26T16:48:11.968507Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:37.716539Z", - "iopub.status.busy": "2024-09-26T14:52:37.715812Z", - "iopub.status.idle": "2024-09-26T14:52:37.730102Z", - "shell.execute_reply": "2024-09-26T14:52:37.729543Z" + "iopub.execute_input": "2024-09-26T16:48:11.971754Z", + "iopub.status.busy": "2024-09-26T16:48:11.970934Z", + "iopub.status.idle": "2024-09-26T16:48:11.985165Z", + "shell.execute_reply": "2024-09-26T16:48:11.984596Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:37.731957Z", - "iopub.status.busy": "2024-09-26T14:52:37.731617Z", - "iopub.status.idle": "2024-09-26T14:52:37.821262Z", - "shell.execute_reply": "2024-09-26T14:52:37.820618Z" + "iopub.execute_input": "2024-09-26T16:48:11.986910Z", + "iopub.status.busy": "2024-09-26T16:48:11.986638Z", + "iopub.status.idle": "2024-09-26T16:48:12.073205Z", + "shell.execute_reply": "2024-09-26T16:48:12.072548Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:37.823300Z", - "iopub.status.busy": "2024-09-26T14:52:37.822839Z", - "iopub.status.idle": "2024-09-26T14:52:38.038920Z", - "shell.execute_reply": "2024-09-26T14:52:38.038375Z" + "iopub.execute_input": "2024-09-26T16:48:12.075457Z", + "iopub.status.busy": "2024-09-26T16:48:12.074967Z", + "iopub.status.idle": "2024-09-26T16:48:12.291023Z", + "shell.execute_reply": "2024-09-26T16:48:12.290463Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.040759Z", - "iopub.status.busy": "2024-09-26T14:52:38.040570Z", - "iopub.status.idle": "2024-09-26T14:52:38.058165Z", - "shell.execute_reply": "2024-09-26T14:52:38.057614Z" + "iopub.execute_input": "2024-09-26T16:48:12.292925Z", + "iopub.status.busy": "2024-09-26T16:48:12.292566Z", + "iopub.status.idle": "2024-09-26T16:48:12.309548Z", + "shell.execute_reply": "2024-09-26T16:48:12.309129Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.060074Z", - "iopub.status.busy": "2024-09-26T14:52:38.059687Z", - "iopub.status.idle": "2024-09-26T14:52:38.069888Z", - "shell.execute_reply": "2024-09-26T14:52:38.069309Z" + "iopub.execute_input": "2024-09-26T16:48:12.311261Z", + "iopub.status.busy": "2024-09-26T16:48:12.310931Z", + "iopub.status.idle": "2024-09-26T16:48:12.320409Z", + "shell.execute_reply": "2024-09-26T16:48:12.319856Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.071813Z", - "iopub.status.busy": "2024-09-26T14:52:38.071379Z", - "iopub.status.idle": "2024-09-26T14:52:38.170054Z", - "shell.execute_reply": "2024-09-26T14:52:38.169477Z" + "iopub.execute_input": "2024-09-26T16:48:12.322060Z", + "iopub.status.busy": "2024-09-26T16:48:12.321883Z", + "iopub.status.idle": "2024-09-26T16:48:12.417155Z", + "shell.execute_reply": "2024-09-26T16:48:12.416502Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.171925Z", - "iopub.status.busy": "2024-09-26T14:52:38.171696Z", - "iopub.status.idle": "2024-09-26T14:52:38.324224Z", - "shell.execute_reply": "2024-09-26T14:52:38.323549Z" + "iopub.execute_input": "2024-09-26T16:48:12.419437Z", + "iopub.status.busy": "2024-09-26T16:48:12.419075Z", + "iopub.status.idle": "2024-09-26T16:48:12.569836Z", + "shell.execute_reply": "2024-09-26T16:48:12.569209Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.326329Z", - "iopub.status.busy": "2024-09-26T14:52:38.325951Z", - "iopub.status.idle": "2024-09-26T14:52:38.329903Z", - "shell.execute_reply": "2024-09-26T14:52:38.329357Z" + "iopub.execute_input": "2024-09-26T16:48:12.572062Z", + "iopub.status.busy": "2024-09-26T16:48:12.571683Z", + "iopub.status.idle": "2024-09-26T16:48:12.575922Z", + "shell.execute_reply": "2024-09-26T16:48:12.575355Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.331907Z", - "iopub.status.busy": "2024-09-26T14:52:38.331482Z", - "iopub.status.idle": "2024-09-26T14:52:38.335196Z", - "shell.execute_reply": "2024-09-26T14:52:38.334746Z" + "iopub.execute_input": "2024-09-26T16:48:12.577700Z", + "iopub.status.busy": "2024-09-26T16:48:12.577393Z", + "iopub.status.idle": "2024-09-26T16:48:12.581358Z", + "shell.execute_reply": "2024-09-26T16:48:12.580897Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.336922Z", - "iopub.status.busy": "2024-09-26T14:52:38.336603Z", - "iopub.status.idle": "2024-09-26T14:52:38.376114Z", - "shell.execute_reply": "2024-09-26T14:52:38.375641Z" + "iopub.execute_input": "2024-09-26T16:48:12.583069Z", + "iopub.status.busy": "2024-09-26T16:48:12.582741Z", + "iopub.status.idle": "2024-09-26T16:48:12.620360Z", + "shell.execute_reply": "2024-09-26T16:48:12.619792Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.378083Z", - "iopub.status.busy": "2024-09-26T14:52:38.377733Z", - "iopub.status.idle": "2024-09-26T14:52:38.419996Z", - "shell.execute_reply": "2024-09-26T14:52:38.419527Z" + "iopub.execute_input": "2024-09-26T16:48:12.622179Z", + "iopub.status.busy": "2024-09-26T16:48:12.621840Z", + "iopub.status.idle": "2024-09-26T16:48:12.663148Z", + "shell.execute_reply": "2024-09-26T16:48:12.662655Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.421872Z", - "iopub.status.busy": "2024-09-26T14:52:38.421510Z", - "iopub.status.idle": "2024-09-26T14:52:38.531907Z", - "shell.execute_reply": "2024-09-26T14:52:38.531268Z" + "iopub.execute_input": "2024-09-26T16:48:12.664830Z", + "iopub.status.busy": "2024-09-26T16:48:12.664495Z", + "iopub.status.idle": "2024-09-26T16:48:12.770136Z", + "shell.execute_reply": "2024-09-26T16:48:12.769404Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.534145Z", - "iopub.status.busy": "2024-09-26T14:52:38.533766Z", - "iopub.status.idle": "2024-09-26T14:52:38.651268Z", - "shell.execute_reply": "2024-09-26T14:52:38.650679Z" + "iopub.execute_input": "2024-09-26T16:48:12.772418Z", + "iopub.status.busy": "2024-09-26T16:48:12.772185Z", + "iopub.status.idle": "2024-09-26T16:48:12.871572Z", + "shell.execute_reply": "2024-09-26T16:48:12.870903Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.653171Z", - "iopub.status.busy": "2024-09-26T14:52:38.652916Z", - "iopub.status.idle": "2024-09-26T14:52:38.868009Z", - "shell.execute_reply": "2024-09-26T14:52:38.867481Z" + "iopub.execute_input": "2024-09-26T16:48:12.873520Z", + "iopub.status.busy": "2024-09-26T16:48:12.873281Z", + "iopub.status.idle": "2024-09-26T16:48:13.086985Z", + "shell.execute_reply": "2024-09-26T16:48:13.086374Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.870022Z", - "iopub.status.busy": "2024-09-26T14:52:38.869668Z", - "iopub.status.idle": "2024-09-26T14:52:39.116995Z", - "shell.execute_reply": "2024-09-26T14:52:39.116409Z" + "iopub.execute_input": "2024-09-26T16:48:13.088923Z", + "iopub.status.busy": "2024-09-26T16:48:13.088594Z", + "iopub.status.idle": "2024-09-26T16:48:13.299972Z", + "shell.execute_reply": "2024-09-26T16:48:13.299384Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:39.119063Z", - "iopub.status.busy": "2024-09-26T14:52:39.118651Z", - "iopub.status.idle": "2024-09-26T14:52:39.124659Z", - "shell.execute_reply": "2024-09-26T14:52:39.124212Z" + "iopub.execute_input": "2024-09-26T16:48:13.302064Z", + "iopub.status.busy": "2024-09-26T16:48:13.301647Z", + "iopub.status.idle": "2024-09-26T16:48:13.307803Z", + "shell.execute_reply": "2024-09-26T16:48:13.307350Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:39.126372Z", - "iopub.status.busy": "2024-09-26T14:52:39.126025Z", - "iopub.status.idle": "2024-09-26T14:52:39.360620Z", - "shell.execute_reply": "2024-09-26T14:52:39.360015Z" + "iopub.execute_input": "2024-09-26T16:48:13.309546Z", + "iopub.status.busy": "2024-09-26T16:48:13.309226Z", + "iopub.status.idle": "2024-09-26T16:48:13.528527Z", + "shell.execute_reply": "2024-09-26T16:48:13.527944Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:39.362552Z", - "iopub.status.busy": "2024-09-26T14:52:39.362361Z", - "iopub.status.idle": "2024-09-26T14:52:40.445531Z", - "shell.execute_reply": "2024-09-26T14:52:40.444958Z" + "iopub.execute_input": "2024-09-26T16:48:13.530301Z", + "iopub.status.busy": "2024-09-26T16:48:13.530081Z", + "iopub.status.idle": "2024-09-26T16:48:14.612951Z", + "shell.execute_reply": "2024-09-26T16:48:14.612272Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index f2aa83ef9..4728163df 100644 --- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:44.089068Z", - "iopub.status.busy": "2024-09-26T14:52:44.088906Z", - "iopub.status.idle": "2024-09-26T14:52:45.299550Z", - "shell.execute_reply": "2024-09-26T14:52:45.298928Z" + "iopub.execute_input": "2024-09-26T16:48:18.142269Z", + "iopub.status.busy": "2024-09-26T16:48:18.142102Z", + "iopub.status.idle": "2024-09-26T16:48:19.317022Z", + "shell.execute_reply": "2024-09-26T16:48:19.316455Z" }, "nbsphinx": "hidden" }, @@ -101,7 +101,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.301912Z", - "iopub.status.busy": "2024-09-26T14:52:45.301449Z", - "iopub.status.idle": "2024-09-26T14:52:45.304645Z", - "shell.execute_reply": "2024-09-26T14:52:45.304094Z" + "iopub.execute_input": "2024-09-26T16:48:19.319070Z", + "iopub.status.busy": "2024-09-26T16:48:19.318798Z", + "iopub.status.idle": "2024-09-26T16:48:19.321986Z", + "shell.execute_reply": "2024-09-26T16:48:19.321534Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.306413Z", - "iopub.status.busy": "2024-09-26T14:52:45.306142Z", - "iopub.status.idle": "2024-09-26T14:52:45.314151Z", - "shell.execute_reply": "2024-09-26T14:52:45.313702Z" + "iopub.execute_input": "2024-09-26T16:48:19.323721Z", + "iopub.status.busy": "2024-09-26T16:48:19.323418Z", + "iopub.status.idle": "2024-09-26T16:48:19.331345Z", + "shell.execute_reply": "2024-09-26T16:48:19.330765Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.315923Z", - "iopub.status.busy": "2024-09-26T14:52:45.315583Z", - "iopub.status.idle": "2024-09-26T14:52:45.364795Z", - "shell.execute_reply": "2024-09-26T14:52:45.364189Z" + "iopub.execute_input": "2024-09-26T16:48:19.333169Z", + "iopub.status.busy": "2024-09-26T16:48:19.332818Z", + "iopub.status.idle": "2024-09-26T16:48:19.380624Z", + "shell.execute_reply": "2024-09-26T16:48:19.379993Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.371300Z", - "iopub.status.busy": "2024-09-26T14:52:45.370858Z", - "iopub.status.idle": "2024-09-26T14:52:45.389579Z", - "shell.execute_reply": "2024-09-26T14:52:45.389064Z" + "iopub.execute_input": "2024-09-26T16:48:19.382816Z", + "iopub.status.busy": "2024-09-26T16:48:19.382354Z", + "iopub.status.idle": "2024-09-26T16:48:19.399250Z", + "shell.execute_reply": "2024-09-26T16:48:19.398688Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.391559Z", - "iopub.status.busy": "2024-09-26T14:52:45.391112Z", - "iopub.status.idle": "2024-09-26T14:52:45.395156Z", - "shell.execute_reply": "2024-09-26T14:52:45.394627Z" + "iopub.execute_input": "2024-09-26T16:48:19.401002Z", + "iopub.status.busy": "2024-09-26T16:48:19.400603Z", + "iopub.status.idle": "2024-09-26T16:48:19.404303Z", + "shell.execute_reply": "2024-09-26T16:48:19.403868Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.397035Z", - "iopub.status.busy": "2024-09-26T14:52:45.396725Z", - "iopub.status.idle": "2024-09-26T14:52:45.414290Z", - "shell.execute_reply": "2024-09-26T14:52:45.413687Z" + "iopub.execute_input": "2024-09-26T16:48:19.405876Z", + "iopub.status.busy": "2024-09-26T16:48:19.405709Z", + "iopub.status.idle": "2024-09-26T16:48:19.420365Z", + "shell.execute_reply": "2024-09-26T16:48:19.419910Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.416157Z", - "iopub.status.busy": "2024-09-26T14:52:45.415806Z", - "iopub.status.idle": "2024-09-26T14:52:45.442358Z", - "shell.execute_reply": "2024-09-26T14:52:45.441883Z" + "iopub.execute_input": "2024-09-26T16:48:19.421950Z", + "iopub.status.busy": "2024-09-26T16:48:19.421677Z", + "iopub.status.idle": "2024-09-26T16:48:19.448553Z", + "shell.execute_reply": "2024-09-26T16:48:19.448111Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.444293Z", - "iopub.status.busy": "2024-09-26T14:52:45.443936Z", - "iopub.status.idle": "2024-09-26T14:52:47.450691Z", - "shell.execute_reply": "2024-09-26T14:52:47.450163Z" + "iopub.execute_input": "2024-09-26T16:48:19.450361Z", + "iopub.status.busy": "2024-09-26T16:48:19.450034Z", + "iopub.status.idle": "2024-09-26T16:48:21.390331Z", + "shell.execute_reply": "2024-09-26T16:48:21.389680Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.452884Z", - "iopub.status.busy": "2024-09-26T14:52:47.452391Z", - "iopub.status.idle": "2024-09-26T14:52:47.459433Z", - "shell.execute_reply": "2024-09-26T14:52:47.458958Z" + "iopub.execute_input": "2024-09-26T16:48:21.392671Z", + "iopub.status.busy": "2024-09-26T16:48:21.392249Z", + "iopub.status.idle": "2024-09-26T16:48:21.398923Z", + "shell.execute_reply": "2024-09-26T16:48:21.398456Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.461250Z", - "iopub.status.busy": "2024-09-26T14:52:47.460913Z", - "iopub.status.idle": "2024-09-26T14:52:47.473767Z", - "shell.execute_reply": "2024-09-26T14:52:47.473271Z" + "iopub.execute_input": "2024-09-26T16:48:21.400606Z", + "iopub.status.busy": "2024-09-26T16:48:21.400272Z", + "iopub.status.idle": "2024-09-26T16:48:21.413278Z", + "shell.execute_reply": "2024-09-26T16:48:21.412720Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.475532Z", - "iopub.status.busy": "2024-09-26T14:52:47.475187Z", - "iopub.status.idle": "2024-09-26T14:52:47.481746Z", - "shell.execute_reply": "2024-09-26T14:52:47.481272Z" + "iopub.execute_input": "2024-09-26T16:48:21.415273Z", + "iopub.status.busy": "2024-09-26T16:48:21.414860Z", + "iopub.status.idle": "2024-09-26T16:48:21.421293Z", + "shell.execute_reply": "2024-09-26T16:48:21.420859Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.483691Z", - "iopub.status.busy": "2024-09-26T14:52:47.483212Z", - "iopub.status.idle": "2024-09-26T14:52:47.486088Z", - "shell.execute_reply": "2024-09-26T14:52:47.485626Z" + "iopub.execute_input": "2024-09-26T16:48:21.423003Z", + "iopub.status.busy": "2024-09-26T16:48:21.422668Z", + "iopub.status.idle": "2024-09-26T16:48:21.425203Z", + "shell.execute_reply": "2024-09-26T16:48:21.424767Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.487800Z", - "iopub.status.busy": "2024-09-26T14:52:47.487397Z", - "iopub.status.idle": "2024-09-26T14:52:47.491109Z", - "shell.execute_reply": "2024-09-26T14:52:47.490533Z" + "iopub.execute_input": "2024-09-26T16:48:21.426848Z", + "iopub.status.busy": "2024-09-26T16:48:21.426577Z", + "iopub.status.idle": "2024-09-26T16:48:21.430180Z", + "shell.execute_reply": "2024-09-26T16:48:21.429630Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.493003Z", - "iopub.status.busy": "2024-09-26T14:52:47.492607Z", - "iopub.status.idle": "2024-09-26T14:52:47.495261Z", - "shell.execute_reply": "2024-09-26T14:52:47.494806Z" + "iopub.execute_input": "2024-09-26T16:48:21.431960Z", + "iopub.status.busy": "2024-09-26T16:48:21.431634Z", + "iopub.status.idle": "2024-09-26T16:48:21.434083Z", + "shell.execute_reply": "2024-09-26T16:48:21.433650Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.497043Z", - "iopub.status.busy": "2024-09-26T14:52:47.496706Z", - "iopub.status.idle": "2024-09-26T14:52:47.500642Z", - "shell.execute_reply": "2024-09-26T14:52:47.500187Z" + "iopub.execute_input": "2024-09-26T16:48:21.435745Z", + "iopub.status.busy": "2024-09-26T16:48:21.435436Z", + "iopub.status.idle": "2024-09-26T16:48:21.439689Z", + "shell.execute_reply": "2024-09-26T16:48:21.439240Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.502313Z", - "iopub.status.busy": "2024-09-26T14:52:47.502139Z", - "iopub.status.idle": "2024-09-26T14:52:47.531332Z", - "shell.execute_reply": "2024-09-26T14:52:47.530848Z" + "iopub.execute_input": "2024-09-26T16:48:21.441490Z", + "iopub.status.busy": "2024-09-26T16:48:21.441094Z", + "iopub.status.idle": "2024-09-26T16:48:21.470480Z", + "shell.execute_reply": "2024-09-26T16:48:21.470043Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.533361Z", - "iopub.status.busy": "2024-09-26T14:52:47.532995Z", - "iopub.status.idle": "2024-09-26T14:52:47.537680Z", - "shell.execute_reply": "2024-09-26T14:52:47.537223Z" + "iopub.execute_input": "2024-09-26T16:48:21.472224Z", + "iopub.status.busy": "2024-09-26T16:48:21.471914Z", + "iopub.status.idle": "2024-09-26T16:48:21.476586Z", + "shell.execute_reply": "2024-09-26T16:48:21.476030Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 9b60292d7..3bffaa946 100644 --- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb @@ -64,10 +64,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:50.516908Z", - "iopub.status.busy": "2024-09-26T14:52:50.516724Z", - "iopub.status.idle": "2024-09-26T14:52:51.779618Z", - "shell.execute_reply": "2024-09-26T14:52:51.779002Z" + "iopub.execute_input": "2024-09-26T16:48:24.262882Z", + "iopub.status.busy": "2024-09-26T16:48:24.262697Z", + "iopub.status.idle": "2024-09-26T16:48:25.499956Z", + "shell.execute_reply": "2024-09-26T16:48:25.499356Z" }, "nbsphinx": "hidden" }, @@ -79,7 +79,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -105,10 +105,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:51.781880Z", - "iopub.status.busy": "2024-09-26T14:52:51.781585Z", - "iopub.status.idle": "2024-09-26T14:52:51.979199Z", - "shell.execute_reply": "2024-09-26T14:52:51.978560Z" + "iopub.execute_input": "2024-09-26T16:48:25.502185Z", + "iopub.status.busy": "2024-09-26T16:48:25.501865Z", + "iopub.status.idle": "2024-09-26T16:48:25.699662Z", + "shell.execute_reply": "2024-09-26T16:48:25.699142Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:51.981718Z", - "iopub.status.busy": "2024-09-26T14:52:51.981227Z", - "iopub.status.idle": "2024-09-26T14:52:51.994745Z", - "shell.execute_reply": "2024-09-26T14:52:51.994150Z" + "iopub.execute_input": "2024-09-26T16:48:25.702141Z", + "iopub.status.busy": "2024-09-26T16:48:25.701628Z", + "iopub.status.idle": "2024-09-26T16:48:25.714875Z", + "shell.execute_reply": "2024-09-26T16:48:25.714375Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:51.996498Z", - "iopub.status.busy": "2024-09-26T14:52:51.996168Z", - "iopub.status.idle": "2024-09-26T14:52:54.626693Z", - "shell.execute_reply": "2024-09-26T14:52:54.626198Z" + "iopub.execute_input": "2024-09-26T16:48:25.716767Z", + "iopub.status.busy": "2024-09-26T16:48:25.716472Z", + "iopub.status.idle": "2024-09-26T16:48:28.390235Z", + "shell.execute_reply": "2024-09-26T16:48:28.389764Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:54.628558Z", - "iopub.status.busy": "2024-09-26T14:52:54.628209Z", - "iopub.status.idle": "2024-09-26T14:52:55.959728Z", - "shell.execute_reply": "2024-09-26T14:52:55.959162Z" + "iopub.execute_input": "2024-09-26T16:48:28.392181Z", + "iopub.status.busy": "2024-09-26T16:48:28.391835Z", + "iopub.status.idle": "2024-09-26T16:48:29.724580Z", + "shell.execute_reply": "2024-09-26T16:48:29.723927Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:55.962013Z", - "iopub.status.busy": "2024-09-26T14:52:55.961551Z", - "iopub.status.idle": "2024-09-26T14:52:55.965395Z", - "shell.execute_reply": "2024-09-26T14:52:55.964876Z" + "iopub.execute_input": "2024-09-26T16:48:29.726682Z", + "iopub.status.busy": "2024-09-26T16:48:29.726488Z", + "iopub.status.idle": "2024-09-26T16:48:29.730597Z", + "shell.execute_reply": "2024-09-26T16:48:29.730108Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:55.967241Z", - "iopub.status.busy": "2024-09-26T14:52:55.966882Z", - "iopub.status.idle": "2024-09-26T14:52:58.123639Z", - "shell.execute_reply": "2024-09-26T14:52:58.123040Z" + "iopub.execute_input": "2024-09-26T16:48:29.732159Z", + "iopub.status.busy": "2024-09-26T16:48:29.731989Z", + "iopub.status.idle": "2024-09-26T16:48:31.766277Z", + "shell.execute_reply": "2024-09-26T16:48:31.765597Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:58.126062Z", - "iopub.status.busy": "2024-09-26T14:52:58.125463Z", - "iopub.status.idle": "2024-09-26T14:52:58.134883Z", - "shell.execute_reply": "2024-09-26T14:52:58.134421Z" + "iopub.execute_input": "2024-09-26T16:48:31.768822Z", + "iopub.status.busy": "2024-09-26T16:48:31.768118Z", + "iopub.status.idle": "2024-09-26T16:48:31.776489Z", + "shell.execute_reply": "2024-09-26T16:48:31.776003Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:58.136727Z", - "iopub.status.busy": "2024-09-26T14:52:58.136398Z", - "iopub.status.idle": "2024-09-26T14:53:00.725562Z", - "shell.execute_reply": "2024-09-26T14:53:00.724908Z" + "iopub.execute_input": "2024-09-26T16:48:31.778053Z", + "iopub.status.busy": "2024-09-26T16:48:31.777879Z", + "iopub.status.idle": "2024-09-26T16:48:34.356589Z", + "shell.execute_reply": "2024-09-26T16:48:34.356051Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:00.727650Z", - "iopub.status.busy": "2024-09-26T14:53:00.727262Z", - "iopub.status.idle": "2024-09-26T14:53:00.731306Z", - "shell.execute_reply": "2024-09-26T14:53:00.730747Z" + "iopub.execute_input": "2024-09-26T16:48:34.358338Z", + "iopub.status.busy": "2024-09-26T16:48:34.358153Z", + "iopub.status.idle": "2024-09-26T16:48:34.361978Z", + "shell.execute_reply": "2024-09-26T16:48:34.361527Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:00.733136Z", - "iopub.status.busy": "2024-09-26T14:53:00.732824Z", - "iopub.status.idle": "2024-09-26T14:53:00.736387Z", - "shell.execute_reply": "2024-09-26T14:53:00.735914Z" + "iopub.execute_input": "2024-09-26T16:48:34.363627Z", + "iopub.status.busy": "2024-09-26T16:48:34.363450Z", + "iopub.status.idle": "2024-09-26T16:48:34.366997Z", + "shell.execute_reply": "2024-09-26T16:48:34.366411Z" } }, "outputs": [], @@ -769,10 +769,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:00.738211Z", - "iopub.status.busy": "2024-09-26T14:53:00.737791Z", - "iopub.status.idle": "2024-09-26T14:53:00.740949Z", - "shell.execute_reply": "2024-09-26T14:53:00.740494Z" + "iopub.execute_input": "2024-09-26T16:48:34.368816Z", + "iopub.status.busy": "2024-09-26T16:48:34.368483Z", + "iopub.status.idle": "2024-09-26T16:48:34.371746Z", + "shell.execute_reply": "2024-09-26T16:48:34.371169Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index 1a465fa59..e035498fb 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": "2024-09-26T14:53:03.303111Z", - "iopub.status.busy": "2024-09-26T14:53:03.302931Z", - "iopub.status.idle": "2024-09-26T14:53:04.571865Z", - "shell.execute_reply": "2024-09-26T14:53:04.571288Z" + "iopub.execute_input": "2024-09-26T16:48:37.038602Z", + "iopub.status.busy": "2024-09-26T16:48:37.038135Z", + "iopub.status.idle": "2024-09-26T16:48:38.252813Z", + "shell.execute_reply": "2024-09-26T16:48:38.252255Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:53:04.574087Z", - "iopub.status.busy": "2024-09-26T14:53:04.573598Z", - "iopub.status.idle": "2024-09-26T14:53:06.166960Z", - "shell.execute_reply": "2024-09-26T14:53:06.166164Z" + "iopub.execute_input": "2024-09-26T16:48:38.254992Z", + "iopub.status.busy": "2024-09-26T16:48:38.254563Z", + "iopub.status.idle": "2024-09-26T16:48:39.600107Z", + "shell.execute_reply": "2024-09-26T16:48:39.599320Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.169408Z", - "iopub.status.busy": "2024-09-26T14:53:06.168985Z", - "iopub.status.idle": "2024-09-26T14:53:06.172322Z", - "shell.execute_reply": "2024-09-26T14:53:06.171868Z" + "iopub.execute_input": "2024-09-26T16:48:39.602525Z", + "iopub.status.busy": "2024-09-26T16:48:39.602141Z", + "iopub.status.idle": "2024-09-26T16:48:39.605609Z", + "shell.execute_reply": "2024-09-26T16:48:39.605025Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.174071Z", - "iopub.status.busy": "2024-09-26T14:53:06.173721Z", - "iopub.status.idle": "2024-09-26T14:53:06.180705Z", - "shell.execute_reply": "2024-09-26T14:53:06.180264Z" + "iopub.execute_input": "2024-09-26T16:48:39.607514Z", + "iopub.status.busy": "2024-09-26T16:48:39.607174Z", + "iopub.status.idle": "2024-09-26T16:48:39.613965Z", + "shell.execute_reply": "2024-09-26T16:48:39.613519Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.182537Z", - "iopub.status.busy": "2024-09-26T14:53:06.182190Z", - "iopub.status.idle": "2024-09-26T14:53:06.687592Z", - "shell.execute_reply": "2024-09-26T14:53:06.686965Z" + "iopub.execute_input": "2024-09-26T16:48:39.615650Z", + "iopub.status.busy": "2024-09-26T16:48:39.615473Z", + "iopub.status.idle": "2024-09-26T16:48:40.108171Z", + "shell.execute_reply": "2024-09-26T16:48:40.107601Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.689555Z", - "iopub.status.busy": "2024-09-26T14:53:06.689377Z", - "iopub.status.idle": "2024-09-26T14:53:06.695403Z", - "shell.execute_reply": "2024-09-26T14:53:06.694799Z" + "iopub.execute_input": "2024-09-26T16:48:40.110582Z", + "iopub.status.busy": "2024-09-26T16:48:40.110385Z", + "iopub.status.idle": "2024-09-26T16:48:40.116258Z", + "shell.execute_reply": "2024-09-26T16:48:40.115836Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.697090Z", - "iopub.status.busy": "2024-09-26T14:53:06.696909Z", - "iopub.status.idle": "2024-09-26T14:53:06.700584Z", - "shell.execute_reply": "2024-09-26T14:53:06.700149Z" + "iopub.execute_input": "2024-09-26T16:48:40.117906Z", + "iopub.status.busy": "2024-09-26T16:48:40.117571Z", + "iopub.status.idle": "2024-09-26T16:48:40.121283Z", + "shell.execute_reply": "2024-09-26T16:48:40.120834Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.702385Z", - "iopub.status.busy": "2024-09-26T14:53:06.702049Z", - "iopub.status.idle": "2024-09-26T14:53:07.596906Z", - "shell.execute_reply": "2024-09-26T14:53:07.596232Z" + "iopub.execute_input": "2024-09-26T16:48:40.123056Z", + "iopub.status.busy": "2024-09-26T16:48:40.122724Z", + "iopub.status.idle": "2024-09-26T16:48:40.973369Z", + "shell.execute_reply": "2024-09-26T16:48:40.972808Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:07.599111Z", - "iopub.status.busy": "2024-09-26T14:53:07.598647Z", - "iopub.status.idle": "2024-09-26T14:53:07.803313Z", - "shell.execute_reply": "2024-09-26T14:53:07.802716Z" + "iopub.execute_input": "2024-09-26T16:48:40.975414Z", + "iopub.status.busy": "2024-09-26T16:48:40.975022Z", + "iopub.status.idle": "2024-09-26T16:48:41.181534Z", + "shell.execute_reply": "2024-09-26T16:48:41.181035Z" } }, "outputs": [ @@ -627,14 +627,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pruning 0 predictions out of 138 using threshold==0.0. These predictions are no longer considered as potential candidates for identifying label issues as their similarity with the given labels is no longer considered." - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "Pruning 0 predictions out of 138 using threshold==0.0. These predictions are no longer considered as potential candidates for identifying label issues as their similarity with the given labels is no longer considered.\n" ] }, { @@ -667,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:07.805415Z", - "iopub.status.busy": "2024-09-26T14:53:07.804927Z", - "iopub.status.idle": "2024-09-26T14:53:07.809280Z", - "shell.execute_reply": "2024-09-26T14:53:07.808847Z" + "iopub.execute_input": "2024-09-26T16:48:41.183524Z", + "iopub.status.busy": "2024-09-26T16:48:41.183174Z", + "iopub.status.idle": "2024-09-26T16:48:41.187742Z", + "shell.execute_reply": "2024-09-26T16:48:41.187280Z" } }, "outputs": [ @@ -707,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:07.810942Z", - "iopub.status.busy": "2024-09-26T14:53:07.810764Z", - "iopub.status.idle": "2024-09-26T14:53:08.277163Z", - "shell.execute_reply": "2024-09-26T14:53:08.276574Z" + "iopub.execute_input": "2024-09-26T16:48:41.189424Z", + "iopub.status.busy": "2024-09-26T16:48:41.189089Z", + "iopub.status.idle": "2024-09-26T16:48:41.644167Z", + "shell.execute_reply": "2024-09-26T16:48:41.643601Z" } }, "outputs": [ @@ -769,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:08.279934Z", - "iopub.status.busy": "2024-09-26T14:53:08.279727Z", - "iopub.status.idle": "2024-09-26T14:53:08.615867Z", - "shell.execute_reply": "2024-09-26T14:53:08.615304Z" + "iopub.execute_input": "2024-09-26T16:48:41.647146Z", + "iopub.status.busy": "2024-09-26T16:48:41.646765Z", + "iopub.status.idle": "2024-09-26T16:48:41.979419Z", + "shell.execute_reply": "2024-09-26T16:48:41.978885Z" } }, "outputs": [ @@ -819,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:08.617985Z", - "iopub.status.busy": "2024-09-26T14:53:08.617788Z", - "iopub.status.idle": "2024-09-26T14:53:08.987995Z", - "shell.execute_reply": "2024-09-26T14:53:08.987382Z" + "iopub.execute_input": "2024-09-26T16:48:41.981728Z", + "iopub.status.busy": "2024-09-26T16:48:41.981454Z", + "iopub.status.idle": "2024-09-26T16:48:42.347417Z", + "shell.execute_reply": "2024-09-26T16:48:42.346860Z" } }, "outputs": [ @@ -869,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:08.990870Z", - "iopub.status.busy": "2024-09-26T14:53:08.990636Z", - "iopub.status.idle": "2024-09-26T14:53:09.438626Z", - "shell.execute_reply": "2024-09-26T14:53:09.438065Z" + "iopub.execute_input": "2024-09-26T16:48:42.350252Z", + "iopub.status.busy": "2024-09-26T16:48:42.349861Z", + "iopub.status.idle": "2024-09-26T16:48:42.790573Z", + "shell.execute_reply": "2024-09-26T16:48:42.789939Z" } }, "outputs": [ @@ -932,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:09.442663Z", - "iopub.status.busy": "2024-09-26T14:53:09.442289Z", - "iopub.status.idle": "2024-09-26T14:53:09.875533Z", - "shell.execute_reply": "2024-09-26T14:53:09.874886Z" + "iopub.execute_input": "2024-09-26T16:48:42.794577Z", + "iopub.status.busy": "2024-09-26T16:48:42.794377Z", + "iopub.status.idle": "2024-09-26T16:48:43.245732Z", + "shell.execute_reply": "2024-09-26T16:48:43.245124Z" } }, "outputs": [ @@ -978,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:09.878235Z", - "iopub.status.busy": "2024-09-26T14:53:09.877876Z", - "iopub.status.idle": "2024-09-26T14:53:10.074349Z", - "shell.execute_reply": "2024-09-26T14:53:10.073721Z" + "iopub.execute_input": "2024-09-26T16:48:43.248409Z", + "iopub.status.busy": "2024-09-26T16:48:43.247937Z", + "iopub.status.idle": "2024-09-26T16:48:43.462862Z", + "shell.execute_reply": "2024-09-26T16:48:43.462278Z" } }, "outputs": [ @@ -1024,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:10.076454Z", - "iopub.status.busy": "2024-09-26T14:53:10.076093Z", - "iopub.status.idle": "2024-09-26T14:53:10.258000Z", - "shell.execute_reply": "2024-09-26T14:53:10.257430Z" + "iopub.execute_input": "2024-09-26T16:48:43.464759Z", + "iopub.status.busy": "2024-09-26T16:48:43.464356Z", + "iopub.status.idle": "2024-09-26T16:48:43.664360Z", + "shell.execute_reply": "2024-09-26T16:48:43.663754Z" } }, "outputs": [ @@ -1074,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:10.260221Z", - "iopub.status.busy": "2024-09-26T14:53:10.259868Z", - "iopub.status.idle": "2024-09-26T14:53:10.262670Z", - "shell.execute_reply": "2024-09-26T14:53:10.262238Z" + "iopub.execute_input": "2024-09-26T16:48:43.666114Z", + "iopub.status.busy": "2024-09-26T16:48:43.665828Z", + "iopub.status.idle": "2024-09-26T16:48:43.668931Z", + "shell.execute_reply": "2024-09-26T16:48:43.668363Z" } }, "outputs": [], @@ -1097,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:10.264357Z", - "iopub.status.busy": "2024-09-26T14:53:10.264032Z", - "iopub.status.idle": "2024-09-26T14:53:11.303194Z", - "shell.execute_reply": "2024-09-26T14:53:11.302561Z" + "iopub.execute_input": "2024-09-26T16:48:43.670754Z", + "iopub.status.busy": "2024-09-26T16:48:43.670355Z", + "iopub.status.idle": "2024-09-26T16:48:44.607344Z", + "shell.execute_reply": "2024-09-26T16:48:44.606795Z" } }, "outputs": [ @@ -1179,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:11.305028Z", - "iopub.status.busy": "2024-09-26T14:53:11.304725Z", - "iopub.status.idle": "2024-09-26T14:53:11.509799Z", - "shell.execute_reply": "2024-09-26T14:53:11.509285Z" + "iopub.execute_input": "2024-09-26T16:48:44.609323Z", + "iopub.status.busy": "2024-09-26T16:48:44.609143Z", + "iopub.status.idle": "2024-09-26T16:48:44.794922Z", + "shell.execute_reply": "2024-09-26T16:48:44.794464Z" } }, "outputs": [ @@ -1221,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:11.511395Z", - "iopub.status.busy": "2024-09-26T14:53:11.511212Z", - "iopub.status.idle": "2024-09-26T14:53:11.718820Z", - "shell.execute_reply": "2024-09-26T14:53:11.718199Z" + "iopub.execute_input": "2024-09-26T16:48:44.796633Z", + "iopub.status.busy": "2024-09-26T16:48:44.796300Z", + "iopub.status.idle": "2024-09-26T16:48:44.925533Z", + "shell.execute_reply": "2024-09-26T16:48:44.925049Z" } }, "outputs": [], @@ -1273,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:11.720947Z", - "iopub.status.busy": "2024-09-26T14:53:11.720765Z", - "iopub.status.idle": "2024-09-26T14:53:12.421538Z", - "shell.execute_reply": "2024-09-26T14:53:12.420820Z" + "iopub.execute_input": "2024-09-26T16:48:44.927354Z", + "iopub.status.busy": "2024-09-26T16:48:44.926951Z", + "iopub.status.idle": "2024-09-26T16:48:45.681126Z", + "shell.execute_reply": "2024-09-26T16:48:45.680563Z" } }, "outputs": [ @@ -1358,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:12.423286Z", - "iopub.status.busy": "2024-09-26T14:53:12.423091Z", - "iopub.status.idle": "2024-09-26T14:53:12.427074Z", - "shell.execute_reply": "2024-09-26T14:53:12.426599Z" + "iopub.execute_input": "2024-09-26T16:48:45.682911Z", + "iopub.status.busy": "2024-09-26T16:48:45.682624Z", + "iopub.status.idle": "2024-09-26T16:48:45.686567Z", + "shell.execute_reply": "2024-09-26T16:48:45.686134Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index 82b2532b4..b3dee231f 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": "2024-09-26T14:53:14.827019Z", - "iopub.status.busy": "2024-09-26T14:53:14.826845Z", - "iopub.status.idle": "2024-09-26T14:53:17.796587Z", - "shell.execute_reply": "2024-09-26T14:53:17.795936Z" + "iopub.execute_input": "2024-09-26T16:48:47.942096Z", + "iopub.status.busy": "2024-09-26T16:48:47.941917Z", + "iopub.status.idle": "2024-09-26T16:48:50.871486Z", + "shell.execute_reply": "2024-09-26T16:48:50.870921Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:53:17.798905Z", - "iopub.status.busy": "2024-09-26T14:53:17.798584Z", - "iopub.status.idle": "2024-09-26T14:53:18.137749Z", - "shell.execute_reply": "2024-09-26T14:53:18.137173Z" + "iopub.execute_input": "2024-09-26T16:48:50.873698Z", + "iopub.status.busy": "2024-09-26T16:48:50.873191Z", + "iopub.status.idle": "2024-09-26T16:48:51.206267Z", + "shell.execute_reply": "2024-09-26T16:48:51.205578Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:18.139715Z", - "iopub.status.busy": "2024-09-26T14:53:18.139407Z", - "iopub.status.idle": "2024-09-26T14:53:18.143870Z", - "shell.execute_reply": "2024-09-26T14:53:18.143450Z" + "iopub.execute_input": "2024-09-26T16:48:51.208571Z", + "iopub.status.busy": "2024-09-26T16:48:51.208081Z", + "iopub.status.idle": "2024-09-26T16:48:51.212464Z", + "shell.execute_reply": "2024-09-26T16:48:51.211895Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:18.145657Z", - "iopub.status.busy": "2024-09-26T14:53:18.145384Z", - "iopub.status.idle": "2024-09-26T14:53:24.392739Z", - "shell.execute_reply": "2024-09-26T14:53:24.392209Z" + "iopub.execute_input": "2024-09-26T16:48:51.214478Z", + "iopub.status.busy": "2024-09-26T16:48:51.214009Z", + "iopub.status.idle": "2024-09-26T16:48:55.704461Z", + "shell.execute_reply": "2024-09-26T16:48:55.703852Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 1212416/170498071 [00:00<00:14, 12024376.95it/s]" + " 1%| | 884736/170498071 [00:00<00:21, 8029939.27it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▎ | 6160384/170498071 [00:00<00:04, 33857865.84it/s]" + " 6%|▋ | 10846208/170498071 [00:00<00:02, 59627773.92it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 10518528/170498071 [00:00<00:04, 38150209.28it/s]" + " 13%|█▎ | 22052864/170498071 [00:00<00:01, 82940324.14it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 15400960/170498071 [00:00<00:03, 42330857.28it/s]" + " 20%|█▉ | 33554432/170498071 [00:00<00:01, 95390224.57it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 20250624/170498071 [00:00<00:03, 44424970.42it/s]" + " 27%|██▋ | 45252608/170498071 [00:00<00:01, 103012463.70it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 24739840/170498071 [00:00<00:03, 44347437.97it/s]" + " 33%|███▎ | 56885248/170498071 [00:00<00:01, 107499259.85it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 29294592/170498071 [00:00<00:03, 44719226.63it/s]" + " 40%|████ | 68452352/170498071 [00:00<00:00, 110123131.51it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 34144256/170498071 [00:00<00:02, 45791541.93it/s]" + " 47%|████▋ | 79986688/170498071 [00:00<00:00, 111678262.93it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 38731776/170498071 [00:00<00:02, 45062771.26it/s]" + " 54%|█████▎ | 91488256/170498071 [00:00<00:00, 112623672.78it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 43253760/170498071 [00:01<00:02, 45089662.43it/s]" + " 60%|██████ | 103022592/170498071 [00:01<00:00, 113408983.06it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 47874048/170498071 [00:01<00:02, 45213443.22it/s]" + " 67%|██████▋ | 114556928/170498071 [00:01<00:00, 113923691.11it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 52494336/170498071 [00:01<00:02, 45379651.12it/s]" + " 74%|███████▍ | 126124032/170498071 [00:01<00:00, 114394535.96it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 57049088/170498071 [00:01<00:02, 44930180.76it/s]" + " 81%|████████ | 137756672/170498071 [00:01<00:00, 114936767.22it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 61571072/170498071 [00:01<00:02, 43892355.61it/s]" + " 88%|████████▊ | 149258240/170498071 [00:01<00:00, 114805892.76it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▊ | 65994752/170498071 [00:01<00:02, 43763301.63it/s]" + " 94%|█████████▍| 160792576/170498071 [00:01<00:00, 114416952.65it/s]" ] }, { @@ -372,151 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 70385664/170498071 [00:01<00:02, 43438744.69it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 44%|████▍ | 75104256/170498071 [00:01<00:02, 44425115.39it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 47%|████▋ | 79855616/170498071 [00:01<00:02, 45166993.85it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 49%|████▉ | 84377600/170498071 [00:01<00:01, 43789983.48it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 52%|█████▏ | 88768512/170498071 [00:02<00:01, 43106787.26it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 55%|█████▍ | 93093888/170498071 [00:02<00:01, 42763173.18it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 57%|█████▋ | 97386496/170498071 [00:02<00:01, 42678693.69it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 60%|█████▉ | 101679104/170498071 [00:02<00:01, 42558052.16it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 62%|██████▏ | 106102784/170498071 [00:02<00:01, 43049601.15it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 65%|██████▍ | 110592000/170498071 [00:02<00:01, 43553293.68it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 67%|██████▋ | 114950144/170498071 [00:02<00:01, 43398814.53it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 70%|██████▉ | 119308288/170498071 [00:02<00:01, 43218682.93it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 73%|███████▎ | 125075456/170498071 [00:02<00:00, 47414945.57it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 78%|███████▊ | 133234688/170498071 [00:02<00:00, 57528916.08it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 83%|████████▎ | 141262848/170498071 [00:03<00:00, 64272093.96it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 87%|████████▋ | 149127168/170498071 [00:03<00:00, 68499385.74it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 92%|█████████▏| 157024256/170498071 [00:03<00:00, 71592148.49it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 97%|█████████▋| 165117952/170498071 [00:03<00:00, 74385700.68it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 170498071/170498071 [00:03<00:00, 49690890.85it/s]" + "100%|██████████| 170498071/170498071 [00:01<00:00, 106723750.68it/s]" ] }, { @@ -634,10 +490,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:24.394624Z", - "iopub.status.busy": "2024-09-26T14:53:24.394340Z", - "iopub.status.idle": "2024-09-26T14:53:24.399279Z", - "shell.execute_reply": "2024-09-26T14:53:24.398789Z" + "iopub.execute_input": "2024-09-26T16:48:55.706640Z", + "iopub.status.busy": "2024-09-26T16:48:55.706205Z", + "iopub.status.idle": "2024-09-26T16:48:55.711022Z", + "shell.execute_reply": "2024-09-26T16:48:55.710463Z" }, "nbsphinx": "hidden" }, @@ -688,10 +544,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:24.400938Z", - "iopub.status.busy": "2024-09-26T14:53:24.400609Z", - "iopub.status.idle": "2024-09-26T14:53:24.953810Z", - "shell.execute_reply": "2024-09-26T14:53:24.953168Z" + "iopub.execute_input": "2024-09-26T16:48:55.712782Z", + "iopub.status.busy": "2024-09-26T16:48:55.712433Z", + "iopub.status.idle": "2024-09-26T16:48:56.260960Z", + "shell.execute_reply": "2024-09-26T16:48:56.260402Z" } }, "outputs": [ @@ -724,10 +580,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:24.955849Z", - "iopub.status.busy": "2024-09-26T14:53:24.955452Z", - "iopub.status.idle": "2024-09-26T14:53:25.472907Z", - "shell.execute_reply": "2024-09-26T14:53:25.472351Z" + "iopub.execute_input": "2024-09-26T16:48:56.262948Z", + "iopub.status.busy": "2024-09-26T16:48:56.262577Z", + "iopub.status.idle": "2024-09-26T16:48:56.780598Z", + "shell.execute_reply": "2024-09-26T16:48:56.779990Z" } }, "outputs": [ @@ -765,10 +621,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:25.474962Z", - "iopub.status.busy": "2024-09-26T14:53:25.474606Z", - "iopub.status.idle": "2024-09-26T14:53:25.478282Z", - "shell.execute_reply": "2024-09-26T14:53:25.477855Z" + "iopub.execute_input": "2024-09-26T16:48:56.782340Z", + "iopub.status.busy": "2024-09-26T16:48:56.782142Z", + "iopub.status.idle": "2024-09-26T16:48:56.785768Z", + "shell.execute_reply": "2024-09-26T16:48:56.785279Z" } }, "outputs": [], @@ -791,17 +647,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:25.479985Z", - "iopub.status.busy": "2024-09-26T14:53:25.479646Z", - "iopub.status.idle": "2024-09-26T14:53:38.119311Z", - "shell.execute_reply": "2024-09-26T14:53:38.118760Z" + "iopub.execute_input": "2024-09-26T16:48:56.787224Z", + "iopub.status.busy": "2024-09-26T16:48:56.787047Z", + "iopub.status.idle": "2024-09-26T16:49:09.349226Z", + "shell.execute_reply": "2024-09-26T16:49:09.348684Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "502208beacbc4eb2877f50728ccb04c0", + "model_id": "1568567d61794ef3be7306ec89cc19ed", "version_major": 2, "version_minor": 0 }, @@ -860,10 +716,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:38.121453Z", - "iopub.status.busy": "2024-09-26T14:53:38.121019Z", - "iopub.status.idle": "2024-09-26T14:53:40.226608Z", - "shell.execute_reply": "2024-09-26T14:53:40.226078Z" + "iopub.execute_input": "2024-09-26T16:49:09.351108Z", + "iopub.status.busy": "2024-09-26T16:49:09.350912Z", + "iopub.status.idle": "2024-09-26T16:49:11.514574Z", + "shell.execute_reply": "2024-09-26T16:49:11.513950Z" } }, "outputs": [ @@ -907,10 +763,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:40.228769Z", - "iopub.status.busy": "2024-09-26T14:53:40.228334Z", - "iopub.status.idle": "2024-09-26T14:53:40.460757Z", - "shell.execute_reply": "2024-09-26T14:53:40.459979Z" + "iopub.execute_input": "2024-09-26T16:49:11.516779Z", + "iopub.status.busy": "2024-09-26T16:49:11.516299Z", + "iopub.status.idle": "2024-09-26T16:49:11.775844Z", + "shell.execute_reply": "2024-09-26T16:49:11.775225Z" } }, "outputs": [ @@ -946,10 +802,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:40.462963Z", - "iopub.status.busy": "2024-09-26T14:53:40.462510Z", - "iopub.status.idle": "2024-09-26T14:53:41.139530Z", - "shell.execute_reply": "2024-09-26T14:53:41.138920Z" + "iopub.execute_input": "2024-09-26T16:49:11.778182Z", + "iopub.status.busy": "2024-09-26T16:49:11.777875Z", + "iopub.status.idle": "2024-09-26T16:49:12.458180Z", + "shell.execute_reply": "2024-09-26T16:49:12.457592Z" } }, "outputs": [ @@ -999,10 +855,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:41.141576Z", - "iopub.status.busy": "2024-09-26T14:53:41.141387Z", - "iopub.status.idle": "2024-09-26T14:53:41.442674Z", - "shell.execute_reply": "2024-09-26T14:53:41.442054Z" + "iopub.execute_input": "2024-09-26T16:49:12.460934Z", + "iopub.status.busy": "2024-09-26T16:49:12.460410Z", + "iopub.status.idle": "2024-09-26T16:49:12.801673Z", + "shell.execute_reply": "2024-09-26T16:49:12.801157Z" } }, "outputs": [ @@ -1050,10 +906,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:41.444606Z", - "iopub.status.busy": "2024-09-26T14:53:41.444407Z", - "iopub.status.idle": "2024-09-26T14:53:41.692450Z", - "shell.execute_reply": "2024-09-26T14:53:41.691834Z" + "iopub.execute_input": "2024-09-26T16:49:12.803602Z", + "iopub.status.busy": "2024-09-26T16:49:12.803295Z", + "iopub.status.idle": "2024-09-26T16:49:13.047992Z", + "shell.execute_reply": "2024-09-26T16:49:13.047318Z" } }, "outputs": [ @@ -1109,10 +965,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:41.694792Z", - "iopub.status.busy": "2024-09-26T14:53:41.694309Z", - "iopub.status.idle": "2024-09-26T14:53:41.786453Z", - "shell.execute_reply": "2024-09-26T14:53:41.785871Z" + "iopub.execute_input": "2024-09-26T16:49:13.050430Z", + "iopub.status.busy": "2024-09-26T16:49:13.049967Z", + "iopub.status.idle": "2024-09-26T16:49:13.144064Z", + "shell.execute_reply": "2024-09-26T16:49:13.143541Z" } }, "outputs": [], @@ -1133,10 +989,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:41.788692Z", - "iopub.status.busy": "2024-09-26T14:53:41.788289Z", - "iopub.status.idle": "2024-09-26T14:53:52.391383Z", - "shell.execute_reply": "2024-09-26T14:53:52.390803Z" + "iopub.execute_input": "2024-09-26T16:49:13.146001Z", + "iopub.status.busy": "2024-09-26T16:49:13.145817Z", + "iopub.status.idle": "2024-09-26T16:49:24.322742Z", + "shell.execute_reply": "2024-09-26T16:49:24.322123Z" } }, "outputs": [ @@ -1173,10 +1029,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:52.393513Z", - "iopub.status.busy": "2024-09-26T14:53:52.393049Z", - "iopub.status.idle": "2024-09-26T14:53:54.671283Z", - "shell.execute_reply": "2024-09-26T14:53:54.670780Z" + "iopub.execute_input": "2024-09-26T16:49:24.324669Z", + "iopub.status.busy": "2024-09-26T16:49:24.324456Z", + "iopub.status.idle": "2024-09-26T16:49:26.555604Z", + "shell.execute_reply": "2024-09-26T16:49:26.555068Z" } }, "outputs": [ @@ -1207,10 +1063,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:54.673751Z", - "iopub.status.busy": "2024-09-26T14:53:54.673100Z", - "iopub.status.idle": "2024-09-26T14:53:54.874229Z", - "shell.execute_reply": "2024-09-26T14:53:54.873718Z" + "iopub.execute_input": "2024-09-26T16:49:26.557921Z", + "iopub.status.busy": "2024-09-26T16:49:26.557336Z", + "iopub.status.idle": "2024-09-26T16:49:26.765308Z", + "shell.execute_reply": "2024-09-26T16:49:26.764809Z" } }, "outputs": [], @@ -1224,10 +1080,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:54.876098Z", - "iopub.status.busy": "2024-09-26T14:53:54.875918Z", - "iopub.status.idle": "2024-09-26T14:53:54.879013Z", - "shell.execute_reply": "2024-09-26T14:53:54.878602Z" + "iopub.execute_input": "2024-09-26T16:49:26.767450Z", + "iopub.status.busy": "2024-09-26T16:49:26.766988Z", + "iopub.status.idle": "2024-09-26T16:49:26.770215Z", + "shell.execute_reply": "2024-09-26T16:49:26.769781Z" } }, "outputs": [], @@ -1265,10 +1121,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:54.880796Z", - "iopub.status.busy": "2024-09-26T14:53:54.880464Z", - "iopub.status.idle": "2024-09-26T14:53:54.888465Z", - "shell.execute_reply": "2024-09-26T14:53:54.888011Z" + "iopub.execute_input": "2024-09-26T16:49:26.771953Z", + "iopub.status.busy": "2024-09-26T16:49:26.771643Z", + "iopub.status.idle": "2024-09-26T16:49:26.780528Z", + "shell.execute_reply": "2024-09-26T16:49:26.779968Z" }, "nbsphinx": "hidden" }, @@ -1313,7 +1169,30 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "19f8d7cfcb2441f39ec909950206b100": { + "0a0ccca290d849a9a789e4b592e9595d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7a2ddabd7aa24965b01b202aab4c0f6c", + "placeholder": "​", + "style": "IPY_MODEL_a9ed174ff34f43a78a93918b71313fcc", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } + }, + "0c22791d5b4549aab88fbdb1117551c9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1366,30 +1245,47 @@ "width": null } }, - "3dbde950338e4819980320793264b8f6": { + "1568567d61794ef3be7306ec89cc19ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_444a8341757540238acd548381d3cf78", - "placeholder": "​", - "style": "IPY_MODEL_c63ffa48637c4cf790d73142dcbf1bca", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0a0ccca290d849a9a789e4b592e9595d", + "IPY_MODEL_f565c63018c44bf3a256937bc1041a5e", + "IPY_MODEL_9bc2b17042da4bac975d46c5271d017a" + ], + "layout": "IPY_MODEL_da8c44c809db4ca9b0751f81c514dde6", "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "tooltip": null } }, - "444a8341757540238acd548381d3cf78": { + "2f3b10ce827a4e0da6fcbda13879c1dd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "31ff6041b83f4b43856cf5ea87682fc6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1442,91 +1338,7 @@ "width": null } }, - "4b5509bd08094575af9bfd6e1b39af74": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "502208beacbc4eb2877f50728ccb04c0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_3dbde950338e4819980320793264b8f6", - "IPY_MODEL_55906b2e5cc7451a90a629cf8eaf9dfa", - "IPY_MODEL_e08a4a5cd5a34519a67999d955a20b6a" - ], - "layout": "IPY_MODEL_f0529d443cdc4ef783433718b133c35d", - "tabbable": null, - "tooltip": null - } - }, - "55906b2e5cc7451a90a629cf8eaf9dfa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a6577ec2ef7f4efc9edc61cb0c210c81", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6428955d0d764798b409d0eed1cd24c0", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "6428955d0d764798b409d0eed1cd24c0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "a6577ec2ef7f4efc9edc61cb0c210c81": { + "7a2ddabd7aa24965b01b202aab4c0f6c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1579,7 +1391,30 @@ "width": null } }, - "c63ffa48637c4cf790d73142dcbf1bca": { + "9bc2b17042da4bac975d46c5271d017a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0c22791d5b4549aab88fbdb1117551c9", + "placeholder": "​", + "style": "IPY_MODEL_b6a18501680c472d9c0f8df4018f9f05", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 251MB/s]" + } + }, + "a9ed174ff34f43a78a93918b71313fcc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1597,30 +1432,25 @@ "text_color": null } }, - "e08a4a5cd5a34519a67999d955a20b6a": { + "b6a18501680c472d9c0f8df4018f9f05": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_19f8d7cfcb2441f39ec909950206b100", - "placeholder": "​", - "style": "IPY_MODEL_4b5509bd08094575af9bfd6e1b39af74", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 297MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f0529d443cdc4ef783433718b133c35d": { + "da8c44c809db4ca9b0751f81c514dde6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1672,6 +1502,32 @@ "visibility": null, "width": null } + }, + "f565c63018c44bf3a256937bc1041a5e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_31ff6041b83f4b43856cf5ea87682fc6", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2f3b10ce827a4e0da6fcbda13879c1dd", + "tabbable": null, + "tooltip": null, + "value": 102469840.0 + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index 5670e5e42..a578e0757 100644 --- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:59.188556Z", - "iopub.status.busy": "2024-09-26T14:53:59.188370Z", - "iopub.status.idle": "2024-09-26T14:54:00.464944Z", - "shell.execute_reply": "2024-09-26T14:54:00.464378Z" + "iopub.execute_input": "2024-09-26T16:49:30.934149Z", + "iopub.status.busy": "2024-09-26T16:49:30.933967Z", + "iopub.status.idle": "2024-09-26T16:49:32.164050Z", + "shell.execute_reply": "2024-09-26T16:49:32.163421Z" }, "nbsphinx": "hidden" }, @@ -116,7 +116,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -142,10 +142,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:00.467202Z", - "iopub.status.busy": "2024-09-26T14:54:00.466665Z", - "iopub.status.idle": "2024-09-26T14:54:00.486020Z", - "shell.execute_reply": "2024-09-26T14:54:00.485402Z" + "iopub.execute_input": "2024-09-26T16:49:32.166155Z", + "iopub.status.busy": "2024-09-26T16:49:32.165898Z", + "iopub.status.idle": "2024-09-26T16:49:32.184052Z", + "shell.execute_reply": "2024-09-26T16:49:32.183625Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:00.488158Z", - "iopub.status.busy": "2024-09-26T14:54:00.487625Z", - "iopub.status.idle": "2024-09-26T14:54:00.490770Z", - "shell.execute_reply": "2024-09-26T14:54:00.490324Z" + "iopub.execute_input": "2024-09-26T16:49:32.185766Z", + "iopub.status.busy": "2024-09-26T16:49:32.185371Z", + "iopub.status.idle": "2024-09-26T16:49:32.188249Z", + "shell.execute_reply": "2024-09-26T16:49:32.187760Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:00.492476Z", - "iopub.status.busy": "2024-09-26T14:54:00.492170Z", - "iopub.status.idle": "2024-09-26T14:54:00.593026Z", - "shell.execute_reply": "2024-09-26T14:54:00.592503Z" + "iopub.execute_input": "2024-09-26T16:49:32.190088Z", + "iopub.status.busy": "2024-09-26T16:49:32.189648Z", + "iopub.status.idle": "2024-09-26T16:49:32.293466Z", + "shell.execute_reply": "2024-09-26T16:49:32.292891Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:00.595033Z", - "iopub.status.busy": "2024-09-26T14:54:00.594676Z", - "iopub.status.idle": "2024-09-26T14:54:00.781165Z", - "shell.execute_reply": "2024-09-26T14:54:00.780607Z" + "iopub.execute_input": "2024-09-26T16:49:32.295419Z", + "iopub.status.busy": "2024-09-26T16:49:32.295087Z", + "iopub.status.idle": "2024-09-26T16:49:32.475211Z", + "shell.execute_reply": "2024-09-26T16:49:32.474596Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:00.783347Z", - "iopub.status.busy": "2024-09-26T14:54:00.782969Z", - "iopub.status.idle": "2024-09-26T14:54:01.032458Z", - "shell.execute_reply": "2024-09-26T14:54:01.031929Z" + "iopub.execute_input": "2024-09-26T16:49:32.477258Z", + "iopub.status.busy": "2024-09-26T16:49:32.477063Z", + "iopub.status.idle": "2024-09-26T16:49:32.697411Z", + "shell.execute_reply": "2024-09-26T16:49:32.696786Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:01.034452Z", - "iopub.status.busy": "2024-09-26T14:54:01.034056Z", - "iopub.status.idle": "2024-09-26T14:54:01.038763Z", - "shell.execute_reply": "2024-09-26T14:54:01.038275Z" + "iopub.execute_input": "2024-09-26T16:49:32.699513Z", + "iopub.status.busy": "2024-09-26T16:49:32.699117Z", + "iopub.status.idle": "2024-09-26T16:49:32.703469Z", + "shell.execute_reply": "2024-09-26T16:49:32.703008Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:01.040507Z", - "iopub.status.busy": "2024-09-26T14:54:01.040163Z", - "iopub.status.idle": "2024-09-26T14:54:01.046197Z", - "shell.execute_reply": "2024-09-26T14:54:01.045737Z" + "iopub.execute_input": "2024-09-26T16:49:32.705261Z", + "iopub.status.busy": "2024-09-26T16:49:32.704952Z", + "iopub.status.idle": "2024-09-26T16:49:32.711173Z", + "shell.execute_reply": "2024-09-26T16:49:32.710630Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:01.048092Z", - "iopub.status.busy": "2024-09-26T14:54:01.047754Z", - "iopub.status.idle": "2024-09-26T14:54:01.050568Z", - "shell.execute_reply": "2024-09-26T14:54:01.050000Z" + "iopub.execute_input": "2024-09-26T16:49:32.712999Z", + "iopub.status.busy": "2024-09-26T16:49:32.712672Z", + "iopub.status.idle": "2024-09-26T16:49:32.715518Z", + "shell.execute_reply": "2024-09-26T16:49:32.715075Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:01.052488Z", - "iopub.status.busy": "2024-09-26T14:54:01.052092Z", - "iopub.status.idle": "2024-09-26T14:54:10.157589Z", - "shell.execute_reply": "2024-09-26T14:54:10.157001Z" + "iopub.execute_input": "2024-09-26T16:49:32.717162Z", + "iopub.status.busy": "2024-09-26T16:49:32.716830Z", + "iopub.status.idle": "2024-09-26T16:49:41.734764Z", + "shell.execute_reply": "2024-09-26T16:49:41.734156Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.160258Z", - "iopub.status.busy": "2024-09-26T14:54:10.159589Z", - "iopub.status.idle": "2024-09-26T14:54:10.167515Z", - "shell.execute_reply": "2024-09-26T14:54:10.167054Z" + "iopub.execute_input": "2024-09-26T16:49:41.737298Z", + "iopub.status.busy": "2024-09-26T16:49:41.736633Z", + "iopub.status.idle": "2024-09-26T16:49:41.744294Z", + "shell.execute_reply": "2024-09-26T16:49:41.743827Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.169285Z", - "iopub.status.busy": "2024-09-26T14:54:10.168935Z", - "iopub.status.idle": "2024-09-26T14:54:10.172611Z", - "shell.execute_reply": "2024-09-26T14:54:10.172168Z" + "iopub.execute_input": "2024-09-26T16:49:41.746155Z", + "iopub.status.busy": "2024-09-26T16:49:41.745811Z", + "iopub.status.idle": "2024-09-26T16:49:41.749437Z", + "shell.execute_reply": "2024-09-26T16:49:41.748991Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.174288Z", - "iopub.status.busy": "2024-09-26T14:54:10.173947Z", - "iopub.status.idle": "2024-09-26T14:54:10.177369Z", - "shell.execute_reply": "2024-09-26T14:54:10.176897Z" + "iopub.execute_input": "2024-09-26T16:49:41.751039Z", + "iopub.status.busy": "2024-09-26T16:49:41.750857Z", + "iopub.status.idle": "2024-09-26T16:49:41.753838Z", + "shell.execute_reply": "2024-09-26T16:49:41.753402Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.179183Z", - "iopub.status.busy": "2024-09-26T14:54:10.178849Z", - "iopub.status.idle": "2024-09-26T14:54:10.182081Z", - "shell.execute_reply": "2024-09-26T14:54:10.181652Z" + "iopub.execute_input": "2024-09-26T16:49:41.755387Z", + "iopub.status.busy": "2024-09-26T16:49:41.755213Z", + "iopub.status.idle": "2024-09-26T16:49:41.758144Z", + "shell.execute_reply": "2024-09-26T16:49:41.757690Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.183707Z", - "iopub.status.busy": "2024-09-26T14:54:10.183367Z", - "iopub.status.idle": "2024-09-26T14:54:10.191340Z", - "shell.execute_reply": "2024-09-26T14:54:10.190898Z" + "iopub.execute_input": "2024-09-26T16:49:41.759733Z", + "iopub.status.busy": "2024-09-26T16:49:41.759558Z", + "iopub.status.idle": "2024-09-26T16:49:41.767740Z", + "shell.execute_reply": "2024-09-26T16:49:41.767203Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.193003Z", - "iopub.status.busy": "2024-09-26T14:54:10.192665Z", - "iopub.status.idle": "2024-09-26T14:54:10.195213Z", - "shell.execute_reply": "2024-09-26T14:54:10.194766Z" + "iopub.execute_input": "2024-09-26T16:49:41.769406Z", + "iopub.status.busy": "2024-09-26T16:49:41.769226Z", + "iopub.status.idle": "2024-09-26T16:49:41.771789Z", + "shell.execute_reply": "2024-09-26T16:49:41.771346Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.196853Z", - "iopub.status.busy": "2024-09-26T14:54:10.196518Z", - "iopub.status.idle": "2024-09-26T14:54:10.322626Z", - "shell.execute_reply": "2024-09-26T14:54:10.322078Z" + "iopub.execute_input": "2024-09-26T16:49:41.773381Z", + "iopub.status.busy": "2024-09-26T16:49:41.773208Z", + "iopub.status.idle": "2024-09-26T16:49:41.900172Z", + "shell.execute_reply": "2024-09-26T16:49:41.899567Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.324771Z", - "iopub.status.busy": "2024-09-26T14:54:10.324359Z", - "iopub.status.idle": "2024-09-26T14:54:10.435194Z", - "shell.execute_reply": "2024-09-26T14:54:10.434642Z" + "iopub.execute_input": "2024-09-26T16:49:41.901970Z", + "iopub.status.busy": "2024-09-26T16:49:41.901791Z", + "iopub.status.idle": "2024-09-26T16:49:42.011526Z", + "shell.execute_reply": "2024-09-26T16:49:42.010936Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.437396Z", - "iopub.status.busy": "2024-09-26T14:54:10.436936Z", - "iopub.status.idle": "2024-09-26T14:54:10.943293Z", - "shell.execute_reply": "2024-09-26T14:54:10.942658Z" + "iopub.execute_input": "2024-09-26T16:49:42.013507Z", + "iopub.status.busy": "2024-09-26T16:49:42.013151Z", + "iopub.status.idle": "2024-09-26T16:49:42.521399Z", + "shell.execute_reply": "2024-09-26T16:49:42.520858Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.945562Z", - "iopub.status.busy": "2024-09-26T14:54:10.945188Z", - "iopub.status.idle": "2024-09-26T14:54:11.045547Z", - "shell.execute_reply": "2024-09-26T14:54:11.044913Z" + "iopub.execute_input": "2024-09-26T16:49:42.523595Z", + "iopub.status.busy": "2024-09-26T16:49:42.523202Z", + "iopub.status.idle": "2024-09-26T16:49:42.628290Z", + "shell.execute_reply": "2024-09-26T16:49:42.627757Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:11.047649Z", - "iopub.status.busy": "2024-09-26T14:54:11.047228Z", - "iopub.status.idle": "2024-09-26T14:54:11.055699Z", - "shell.execute_reply": "2024-09-26T14:54:11.055230Z" + "iopub.execute_input": "2024-09-26T16:49:42.630336Z", + "iopub.status.busy": "2024-09-26T16:49:42.629907Z", + "iopub.status.idle": "2024-09-26T16:49:42.638614Z", + "shell.execute_reply": "2024-09-26T16:49:42.638150Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:11.057456Z", - "iopub.status.busy": "2024-09-26T14:54:11.057092Z", - "iopub.status.idle": "2024-09-26T14:54:11.059706Z", - "shell.execute_reply": "2024-09-26T14:54:11.059257Z" + "iopub.execute_input": "2024-09-26T16:49:42.640252Z", + "iopub.status.busy": "2024-09-26T16:49:42.640077Z", + "iopub.status.idle": "2024-09-26T16:49:42.642912Z", + "shell.execute_reply": "2024-09-26T16:49:42.642418Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:11.061497Z", - "iopub.status.busy": "2024-09-26T14:54:11.061113Z", - "iopub.status.idle": "2024-09-26T14:54:16.702766Z", - "shell.execute_reply": "2024-09-26T14:54:16.702139Z" + "iopub.execute_input": "2024-09-26T16:49:42.644520Z", + "iopub.status.busy": "2024-09-26T16:49:42.644347Z", + "iopub.status.idle": "2024-09-26T16:49:48.426835Z", + "shell.execute_reply": "2024-09-26T16:49:48.426209Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:16.704653Z", - "iopub.status.busy": "2024-09-26T14:54:16.704460Z", - "iopub.status.idle": "2024-09-26T14:54:16.712980Z", - "shell.execute_reply": "2024-09-26T14:54:16.712530Z" + "iopub.execute_input": "2024-09-26T16:49:48.428878Z", + "iopub.status.busy": "2024-09-26T16:49:48.428500Z", + "iopub.status.idle": "2024-09-26T16:49:48.437064Z", + "shell.execute_reply": "2024-09-26T16:49:48.436506Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:16.714897Z", - "iopub.status.busy": "2024-09-26T14:54:16.714556Z", - "iopub.status.idle": "2024-09-26T14:54:16.786785Z", - "shell.execute_reply": "2024-09-26T14:54:16.786234Z" + "iopub.execute_input": "2024-09-26T16:49:48.438914Z", + "iopub.status.busy": "2024-09-26T16:49:48.438608Z", + "iopub.status.idle": "2024-09-26T16:49:48.502713Z", + "shell.execute_reply": "2024-09-26T16:49:48.502072Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 6779478cb..b69ce6189 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": "2024-09-26T14:54:20.095591Z", - "iopub.status.busy": "2024-09-26T14:54:20.095416Z", - "iopub.status.idle": "2024-09-26T14:54:23.030061Z", - "shell.execute_reply": "2024-09-26T14:54:23.029312Z" + "iopub.execute_input": "2024-09-26T16:49:51.750531Z", + "iopub.status.busy": "2024-09-26T16:49:51.750346Z", + "iopub.status.idle": "2024-09-26T16:49:53.816346Z", + "shell.execute_reply": "2024-09-26T16:49:53.815636Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:23.032402Z", - "iopub.status.busy": "2024-09-26T14:54:23.032024Z", - "iopub.status.idle": "2024-09-26T14:55:28.921952Z", - "shell.execute_reply": "2024-09-26T14:55:28.921155Z" + "iopub.execute_input": "2024-09-26T16:49:53.818373Z", + "iopub.status.busy": "2024-09-26T16:49:53.818182Z", + "iopub.status.idle": "2024-09-26T16:50:58.968730Z", + "shell.execute_reply": "2024-09-26T16:50:58.968046Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:55:28.924172Z", - "iopub.status.busy": "2024-09-26T14:55:28.923971Z", - "iopub.status.idle": "2024-09-26T14:55:30.137143Z", - "shell.execute_reply": "2024-09-26T14:55:30.136538Z" + "iopub.execute_input": "2024-09-26T16:50:58.970954Z", + "iopub.status.busy": "2024-09-26T16:50:58.970572Z", + "iopub.status.idle": "2024-09-26T16:51:00.191978Z", + "shell.execute_reply": "2024-09-26T16:51:00.191433Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:55:30.139396Z", - "iopub.status.busy": "2024-09-26T14:55:30.139106Z", - "iopub.status.idle": "2024-09-26T14:55:30.142481Z", - "shell.execute_reply": "2024-09-26T14:55:30.141914Z" + "iopub.execute_input": "2024-09-26T16:51:00.194104Z", + "iopub.status.busy": "2024-09-26T16:51:00.193692Z", + "iopub.status.idle": "2024-09-26T16:51:00.197076Z", + "shell.execute_reply": "2024-09-26T16:51:00.196619Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:55:30.144228Z", - "iopub.status.busy": "2024-09-26T14:55:30.144050Z", - "iopub.status.idle": "2024-09-26T14:55:30.147926Z", - "shell.execute_reply": "2024-09-26T14:55:30.147419Z" + "iopub.execute_input": "2024-09-26T16:51:00.198776Z", + "iopub.status.busy": "2024-09-26T16:51:00.198500Z", + "iopub.status.idle": "2024-09-26T16:51:00.202390Z", + "shell.execute_reply": "2024-09-26T16:51:00.201918Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:55:30.149873Z", - "iopub.status.busy": "2024-09-26T14:55:30.149499Z", - "iopub.status.idle": "2024-09-26T14:55:30.153440Z", - "shell.execute_reply": "2024-09-26T14:55:30.152904Z" + "iopub.execute_input": "2024-09-26T16:51:00.204130Z", + "iopub.status.busy": "2024-09-26T16:51:00.203796Z", + "iopub.status.idle": "2024-09-26T16:51:00.207281Z", + "shell.execute_reply": "2024-09-26T16:51:00.206836Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:55:30.155269Z", - "iopub.status.busy": "2024-09-26T14:55:30.154919Z", - "iopub.status.idle": "2024-09-26T14:55:30.158026Z", - "shell.execute_reply": "2024-09-26T14:55:30.157441Z" + "iopub.execute_input": "2024-09-26T16:51:00.208813Z", + "iopub.status.busy": "2024-09-26T16:51:00.208544Z", + "iopub.status.idle": "2024-09-26T16:51:00.211286Z", + "shell.execute_reply": "2024-09-26T16:51:00.210833Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:55:30.159991Z", - "iopub.status.busy": "2024-09-26T14:55:30.159527Z", - "iopub.status.idle": "2024-09-26T14:56:07.853263Z", - "shell.execute_reply": "2024-09-26T14:56:07.852683Z" + "iopub.execute_input": "2024-09-26T16:51:00.212837Z", + "iopub.status.busy": "2024-09-26T16:51:00.212655Z", + "iopub.status.idle": "2024-09-26T16:51:38.202000Z", + "shell.execute_reply": "2024-09-26T16:51:38.201357Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d3c194b71ae41699ecaf593bb466ee6", + "model_id": "502b9ff8baad4af5b5dddfdbca9a8ab9", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f246aefc67174f658fc6990471fd838b", + "model_id": "bb7371c71aa847f2b4d79a007cc0db13", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:56:07.855727Z", - "iopub.status.busy": "2024-09-26T14:56:07.855280Z", - "iopub.status.idle": "2024-09-26T14:56:08.539218Z", - "shell.execute_reply": "2024-09-26T14:56:08.538732Z" + "iopub.execute_input": "2024-09-26T16:51:38.204539Z", + "iopub.status.busy": "2024-09-26T16:51:38.204042Z", + "iopub.status.idle": "2024-09-26T16:51:38.876296Z", + "shell.execute_reply": "2024-09-26T16:51:38.875807Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:56:08.541245Z", - "iopub.status.busy": "2024-09-26T14:56:08.540794Z", - "iopub.status.idle": "2024-09-26T14:56:11.382690Z", - "shell.execute_reply": "2024-09-26T14:56:11.382214Z" + "iopub.execute_input": "2024-09-26T16:51:38.878353Z", + "iopub.status.busy": "2024-09-26T16:51:38.877791Z", + "iopub.status.idle": "2024-09-26T16:51:41.686939Z", + "shell.execute_reply": "2024-09-26T16:51:41.686386Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:56:11.384692Z", - "iopub.status.busy": "2024-09-26T14:56:11.384341Z", - "iopub.status.idle": "2024-09-26T14:56:43.908330Z", - "shell.execute_reply": "2024-09-26T14:56:43.907746Z" + "iopub.execute_input": "2024-09-26T16:51:41.688815Z", + "iopub.status.busy": "2024-09-26T16:51:41.688478Z", + "iopub.status.idle": "2024-09-26T16:52:13.850567Z", + "shell.execute_reply": "2024-09-26T16:52:13.849973Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b6f8c999233c44e6b60c123e18607ca1", + "model_id": "a82830a9b3954f49ba5ec182f4498604", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:56:43.910328Z", - "iopub.status.busy": "2024-09-26T14:56:43.909998Z", - "iopub.status.idle": "2024-09-26T14:56:59.179852Z", - "shell.execute_reply": "2024-09-26T14:56:59.179195Z" + "iopub.execute_input": "2024-09-26T16:52:13.852523Z", + "iopub.status.busy": "2024-09-26T16:52:13.852050Z", + "iopub.status.idle": "2024-09-26T16:52:29.573789Z", + "shell.execute_reply": "2024-09-26T16:52:29.573253Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:56:59.182094Z", - "iopub.status.busy": "2024-09-26T14:56:59.181790Z", - "iopub.status.idle": "2024-09-26T14:57:03.058054Z", - "shell.execute_reply": "2024-09-26T14:57:03.057557Z" + "iopub.execute_input": "2024-09-26T16:52:29.575856Z", + "iopub.status.busy": "2024-09-26T16:52:29.575654Z", + "iopub.status.idle": "2024-09-26T16:52:33.399699Z", + "shell.execute_reply": "2024-09-26T16:52:33.399171Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:03.059722Z", - "iopub.status.busy": "2024-09-26T14:57:03.059541Z", - "iopub.status.idle": "2024-09-26T14:57:04.551144Z", - "shell.execute_reply": "2024-09-26T14:57:04.550565Z" + "iopub.execute_input": "2024-09-26T16:52:33.401431Z", + "iopub.status.busy": "2024-09-26T16:52:33.401250Z", + "iopub.status.idle": "2024-09-26T16:52:34.923698Z", + "shell.execute_reply": "2024-09-26T16:52:34.923154Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5444a2dc1c4c403ab396248114105df7", + "model_id": "bdb101c173114412ac307c8cf1b57cbf", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:04.553436Z", - "iopub.status.busy": "2024-09-26T14:57:04.552948Z", - "iopub.status.idle": "2024-09-26T14:57:04.584746Z", - "shell.execute_reply": "2024-09-26T14:57:04.584077Z" + "iopub.execute_input": "2024-09-26T16:52:34.925981Z", + "iopub.status.busy": "2024-09-26T16:52:34.925610Z", + "iopub.status.idle": "2024-09-26T16:52:34.959645Z", + "shell.execute_reply": "2024-09-26T16:52:34.958978Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:04.587085Z", - "iopub.status.busy": "2024-09-26T14:57:04.586688Z", - "iopub.status.idle": "2024-09-26T14:57:10.717190Z", - "shell.execute_reply": "2024-09-26T14:57:10.716706Z" + "iopub.execute_input": "2024-09-26T16:52:34.961505Z", + "iopub.status.busy": "2024-09-26T16:52:34.961304Z", + "iopub.status.idle": "2024-09-26T16:52:41.078411Z", + "shell.execute_reply": "2024-09-26T16:52:41.077883Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:10.719179Z", - "iopub.status.busy": "2024-09-26T14:57:10.718830Z", - "iopub.status.idle": "2024-09-26T14:57:10.774462Z", - "shell.execute_reply": "2024-09-26T14:57:10.773892Z" + "iopub.execute_input": "2024-09-26T16:52:41.080563Z", + "iopub.status.busy": "2024-09-26T16:52:41.080164Z", + "iopub.status.idle": "2024-09-26T16:52:41.137242Z", + "shell.execute_reply": "2024-09-26T16:52:41.136739Z" }, "nbsphinx": "hidden" }, @@ -1038,25 +1038,60 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "008823ad1c554e5fa5b1815e6e7eee3a": { - "model_module": "@jupyter-widgets/controls", + "0121c98358854d4f8fe3347d2c02f405": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "038939be2791404a8d8b3535498c5720": { + "013d5146b8714b33a9b49c5875a584bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1109,57 +1144,7 @@ "width": null } }, - "03edd2e8077d415a86a42428227957c1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d5f572bcf9e34ff5b3f799cfc3b2c03c", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_fc418d04bfd44dc999d29a7cfbaf1bf5", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "0d3c194b71ae41699ecaf593bb466ee6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_5d4af35c70b14d9b95542e9fbacf5ee2", - "IPY_MODEL_03edd2e8077d415a86a42428227957c1", - "IPY_MODEL_d408f59a6c4642dbacacf8536dd5bb86" - ], - "layout": "IPY_MODEL_b25023ee46574f0987d4401430bdbe95", - "tabbable": null, - "tooltip": null - } - }, - "11d7b29b91e94ee382b5c3abbb5da356": { + "0b9d95e573da454fa4d21aa6a8707152": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1212,53 +1197,60 @@ "width": null } }, - "15a01925ca5e45e5bb086a7b185ac53c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ffca702bd3444f1690f1f5f85493ca09", - "placeholder": "​", - "style": "IPY_MODEL_008823ad1c554e5fa5b1815e6e7eee3a", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:25<00:00,  1.22it/s]" - } - }, - "1b8ddda746534779bbbb5fd4b8f8df0b": { - "model_module": "@jupyter-widgets/controls", + "0c78a349af244bf3bc6ee925f47cf139": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_11d7b29b91e94ee382b5c3abbb5da356", - "placeholder": "​", - "style": "IPY_MODEL_2cada3550c7444318e24a19d4c5bac92", - "tabbable": null, - "tooltip": null, - "value": " 4997683/4997683 [00:32<00:00, 153968.10it/s]" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "21f930a5e16b44e6896cab16aadf76b0": { + "190e7a9469bc4e24a369b43d468f6485": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1273,33 +1265,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_cbe2b73a42764a2aacaeaee0b9c612b7", + "layout": "IPY_MODEL_b6f143853a084fb0b6ccc250fe93236e", "placeholder": "​", - "style": "IPY_MODEL_d26696e0cc9f4eef935b52e6f5301e41", + "style": "IPY_MODEL_fe0fc4bc25ce44089ff347ab28732dab", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: 100%" + "value": "number of examples processed for estimating thresholds: 100%" } }, - "2cada3550c7444318e24a19d4c5bac92": { + "1970eb3b8ea140959defd0687c894d97": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "2e397451daaa420aac06b58d115ddb89": { + "1c4b81bd340f4a0295988a552f7a58bb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1314,15 +1304,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6c7533cc89b74b90bdcf06dda9d4297f", + "layout": "IPY_MODEL_b1b74b4c0287481381f3cd409688ae72", "placeholder": "​", - "style": "IPY_MODEL_aedea8fe506b40c1933ac0b06c3dc5c7", + "style": "IPY_MODEL_e9fd87514e6b4549b129af0d6e8a05f0", "tabbable": null, "tooltip": null, "value": "100%" } }, - "3539b50ce0e843448d49322ce25b2b2e": { + "2067dcea68e84063ad1f6a5054c3e789": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1375,7 +1365,48 @@ "width": null } }, - "38f90d531db8409db73b7389ee4986c2": { + "20ab2ca6c5f64a7ea18f346ddf743a8b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_47581e85de3940b587f67825e19622c5", + "placeholder": "​", + "style": "IPY_MODEL_97feb3768a2e4dbda7a36b18153e5c0f", + "tabbable": null, + "tooltip": null, + "value": " 4997683/4997683 [00:31<00:00, 158200.14it/s]" + } + }, + "245b1a0dff754ba583337f4d70981efe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "25ab34454075497b970ae157d65bc0f2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1393,7 +1424,7 @@ "text_color": null } }, - "414724ec89444e8ebc1105e3c21216d3": { + "32926b1dbb4b452d965d85b9ab3280bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1446,7 +1477,25 @@ "width": null } }, - "477c45c2e60a4cc7bc955c274f038c75": { + "42ac1b98a0284f3892253b28e716c7ad": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "44eb89ac642f451e9d987ca3647bdbe2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1462,17 +1511,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_496448b9dfc748d7b07ed9a700cc1ab7", + "layout": "IPY_MODEL_0c78a349af244bf3bc6ee925f47cf139", "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_c19fb70e50ef4b3aa215c397be2fa0ed", + "style": "IPY_MODEL_a95f2478462247808e7fbfeaf7f4269c", "tabbable": null, "tooltip": null, "value": 30.0 } }, - "496448b9dfc748d7b07ed9a700cc1ab7": { + "47581e85de3940b587f67825e19622c5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1525,30 +1574,7 @@ "width": null } }, - "4db5293fd3e94b6eb261d17cfdd19337": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_aeabc766c99c4e2b8edffb93d948620a", - "placeholder": "​", - "style": "IPY_MODEL_cbf3bc2871f144bda8f96df51315cc6a", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:01<00:00, 20.31it/s]" - } - }, - "5444a2dc1c4c403ab396248114105df7": { + "502b9ff8baad4af5b5dddfdbca9a8ab9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1563,55 +1589,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e15e2d1b74894e47b98ed243861d83d8", - "IPY_MODEL_db3abba05009401583103fd3bfc35643", - "IPY_MODEL_4db5293fd3e94b6eb261d17cfdd19337" + "IPY_MODEL_190e7a9469bc4e24a369b43d468f6485", + "IPY_MODEL_af36ee45554e45998cbe073cbd448f30", + "IPY_MODEL_fefa0f5d00b74ed48732a140b6e290ba" ], - "layout": "IPY_MODEL_c41365fd01984997be6e7450cfa7d4d5", + "layout": "IPY_MODEL_013d5146b8714b33a9b49c5875a584bf", "tabbable": null, "tooltip": null } }, - "5ab8498d427444c6b4d07bf8d5bc6157": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "5d4af35c70b14d9b95542e9fbacf5ee2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a6ce96bd4a1f4a83b1164ac6cbe3d02f", - "placeholder": "​", - "style": "IPY_MODEL_38f90d531db8409db73b7389ee4986c2", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: 100%" - } - }, - "60825090590c4420817f531a12ba0cb9": { + "53b638ae41f3478f99d13a3ebb42169f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1664,7 +1651,7 @@ "width": null } }, - "6c7533cc89b74b90bdcf06dda9d4297f": { + "60f7f25133834746944894473307e0e7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1717,7 +1704,7 @@ "width": null } }, - "6fc3fa5fef38489287ed8d9f7c6e1c3e": { + "6149d5ce9db84ce78a209d8113fbcdeb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1770,25 +1757,7 @@ "width": null } }, - "7211d82a11904799ba5182ef4f7e1762": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a6ce96bd4a1f4a83b1164ac6cbe3d02f": { + "69d0fc942d0d4e6a97445f38e4d57ea2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1841,113 +1810,67 @@ "width": null } }, - "aad869b6d41d459097999efed9f5aabb": { - "model_module": "@jupyter-widgets/base", + "6de0625b60cd4c02b120f1de805e03c9": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "aeabc766c99c4e2b8edffb93d948620a": { - "model_module": "@jupyter-widgets/base", + "7a004d25557e444496cc520252ba637f": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a701b3a09a964c25b447425ed13b3e2c", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_861c793818bd43bca46ff4897a3a44fa", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "861c793818bd43bca46ff4897a3a44fa": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "aedea8fe506b40c1933ac0b06c3dc5c7": { + "97feb3768a2e4dbda7a36b18153e5c0f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1965,7 +1888,48 @@ "text_color": null } }, - "b25023ee46574f0987d4401430bdbe95": { + "9939290b74ec4ebe886ed5834f42faf6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a24f9eab3fb94dc2990c0f506f2f987a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0b9d95e573da454fa4d21aa6a8707152", + "placeholder": "​", + "style": "IPY_MODEL_9939290b74ec4ebe886ed5834f42faf6", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:01<00:00, 19.48it/s]" + } + }, + "a701b3a09a964c25b447425ed13b3e2c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2018,7 +1982,7 @@ "width": null } }, - "b6f8c999233c44e6b60c123e18607ca1": { + "a82830a9b3954f49ba5ec182f4498604": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2033,16 +1997,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2e397451daaa420aac06b58d115ddb89", - "IPY_MODEL_c126cab8ab9c4826849b4c390465afaf", - "IPY_MODEL_1b8ddda746534779bbbb5fd4b8f8df0b" + "IPY_MODEL_1c4b81bd340f4a0295988a552f7a58bb", + "IPY_MODEL_e3dce47f83364b91ab69a6c0d0ada3a5", + "IPY_MODEL_20ab2ca6c5f64a7ea18f346ddf743a8b" ], - "layout": "IPY_MODEL_60825090590c4420817f531a12ba0cb9", + "layout": "IPY_MODEL_32926b1dbb4b452d965d85b9ab3280bb", "tabbable": null, "tooltip": null } }, - "c0780c5558e44bdf9cd38943fbc6879f": { + "a95f2478462247808e7fbfeaf7f4269c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2058,7 +2022,30 @@ "description_width": "" } }, - "c126cab8ab9c4826849b4c390465afaf": { + "abb25c23e4fe4deb9022759c984b7e1a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e417d1d7ab4b479ba1a24db21005ee18", + "placeholder": "​", + "style": "IPY_MODEL_25ab34454075497b970ae157d65bc0f2", + "tabbable": null, + "tooltip": null, + "value": "images processed using softmin: 100%" + } + }, + "af36ee45554e45998cbe073cbd448f30": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2074,33 +2061,93 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3539b50ce0e843448d49322ce25b2b2e", - "max": 4997683.0, + "layout": "IPY_MODEL_2067dcea68e84063ad1f6a5054c3e789", + "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_5ab8498d427444c6b4d07bf8d5bc6157", + "style": "IPY_MODEL_fda6bf3d4a3349d082a2341f068a5e89", "tabbable": null, "tooltip": null, - "value": 4997683.0 + "value": 30.0 + } + }, + "b1b74b4c0287481381f3cd409688ae72": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "c19fb70e50ef4b3aa215c397be2fa0ed": { + "b1ecb6440eeb484cab4c6d3a1846d880": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6149d5ce9db84ce78a209d8113fbcdeb", + "placeholder": "​", + "style": "IPY_MODEL_6de0625b60cd4c02b120f1de805e03c9", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for checking labels: 100%" } }, - "c41365fd01984997be6e7450cfa7d4d5": { + "b6f143853a084fb0b6ccc250fe93236e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2153,7 +2200,7 @@ "width": null } }, - "cbe2b73a42764a2aacaeaee0b9c612b7": { + "b7f3da04e1de47889cbec5d9b381d330": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2206,66 +2253,81 @@ "width": null } }, - "cbf3bc2871f144bda8f96df51315cc6a": { + "bb7371c71aa847f2b4d79a007cc0db13": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b1ecb6440eeb484cab4c6d3a1846d880", + "IPY_MODEL_7a004d25557e444496cc520252ba637f", + "IPY_MODEL_f411da9220e6477ebc385b8c9da96582" + ], + "layout": "IPY_MODEL_b7f3da04e1de47889cbec5d9b381d330", + "tabbable": null, + "tooltip": null } }, - "d26696e0cc9f4eef935b52e6f5301e41": { + "bdb101c173114412ac307c8cf1b57cbf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_abb25c23e4fe4deb9022759c984b7e1a", + "IPY_MODEL_44eb89ac642f451e9d987ca3647bdbe2", + "IPY_MODEL_a24f9eab3fb94dc2990c0f506f2f987a" + ], + "layout": "IPY_MODEL_69d0fc942d0d4e6a97445f38e4d57ea2", + "tabbable": null, + "tooltip": null } }, - "d408f59a6c4642dbacacf8536dd5bb86": { + "e3dce47f83364b91ab69a6c0d0ada3a5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_aad869b6d41d459097999efed9f5aabb", - "placeholder": "​", - "style": "IPY_MODEL_7211d82a11904799ba5182ef4f7e1762", + "layout": "IPY_MODEL_60f7f25133834746944894473307e0e7", + "max": 4997683.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1970eb3b8ea140959defd0687c894d97", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:00<00:00, 761.55it/s]" + "value": 4997683.0 } }, - "d5f572bcf9e34ff5b3f799cfc3b2c03c": { + "e417d1d7ab4b479ba1a24db21005ee18": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2318,33 +2380,25 @@ "width": null } }, - "db3abba05009401583103fd3bfc35643": { + "e9fd87514e6b4549b129af0d6e8a05f0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_414724ec89444e8ebc1105e3c21216d3", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c0780c5558e44bdf9cd38943fbc6879f", - "tabbable": null, - "tooltip": null, - "value": 30.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "e15e2d1b74894e47b98ed243861d83d8": { + "f411da9220e6477ebc385b8c9da96582": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2359,123 +2413,69 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_038939be2791404a8d8b3535498c5720", + "layout": "IPY_MODEL_0121c98358854d4f8fe3347d2c02f405", "placeholder": "​", - "style": "IPY_MODEL_e7222a4d37404d41a68f2bf782915ef2", + "style": "IPY_MODEL_245b1a0dff754ba583337f4d70981efe", "tabbable": null, "tooltip": null, - "value": "images processed using softmin: 100%" + "value": " 30/30 [00:25<00:00,  1.17it/s]" } }, - "e7222a4d37404d41a68f2bf782915ef2": { + "fda6bf3d4a3349d082a2341f068a5e89": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f246aefc67174f658fc6990471fd838b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_21f930a5e16b44e6896cab16aadf76b0", - "IPY_MODEL_477c45c2e60a4cc7bc955c274f038c75", - "IPY_MODEL_15a01925ca5e45e5bb086a7b185ac53c" - ], - "layout": "IPY_MODEL_6fc3fa5fef38489287ed8d9f7c6e1c3e", - "tabbable": null, - "tooltip": null + "bar_color": null, + "description_width": "" } }, - "fc418d04bfd44dc999d29a7cfbaf1bf5": { + "fe0fc4bc25ce44089ff347ab28732dab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ffca702bd3444f1690f1f5f85493ca09": { - "model_module": "@jupyter-widgets/base", + "fefa0f5d00b74ed48732a140b6e290ba": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_53b638ae41f3478f99d13a3ebb42169f", + "placeholder": "​", + "style": "IPY_MODEL_42ac1b98a0284f3892253b28e716c7ad", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 822.88it/s]" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 9d0e0764f..9eee41a96 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": "2024-09-26T14:57:13.331707Z", - "iopub.status.busy": "2024-09-26T14:57:13.331541Z", - "iopub.status.idle": "2024-09-26T14:57:15.936866Z", - "shell.execute_reply": "2024-09-26T14:57:15.936192Z" + "iopub.execute_input": "2024-09-26T16:52:43.644067Z", + "iopub.status.busy": "2024-09-26T16:52:43.643568Z", + "iopub.status.idle": "2024-09-26T16:52:44.942651Z", + "shell.execute_reply": "2024-09-26T16:52:44.941999Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-26 14:57:13-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-09-26 16:52:43-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.243, 2400:52e0:1a00::940:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.243|:443... connected.\r\n", + "185.93.1.251, 2400:52e0:1a00::1207:2\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.251|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -116,9 +123,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.41MB/s in 0.2s \r\n", "\r\n", - "2024-09-26 14:57:13 (7.67 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-09-26 16:52:44 (5.41 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -127,13 +134,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Archive: conll2003.zip\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Archive: conll2003.zip\r\n", " inflating: data/metadata \r\n", " inflating: data/test.txt \r\n", " inflating: data/train.txt \r\n", @@ -144,16 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-26 14:57:14-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.27.119, 52.217.207.97, 52.217.171.81, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.27.119|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n", + "--2024-09-26 16:52:44-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.81.60, 3.5.29.169, 16.182.105.153, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.81.60|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,57 +168,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 2%[ ] 482.32K 2.17MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 7%[> ] 1.23M 2.84MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 14%[=> ] 2.42M 3.72MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 26%[====> ] 4.26M 4.90MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 43%[=======> ] 7.12M 6.54MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 71%[=============> ] 11.56M 8.85MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 100%[===================>] 16.26M 11.2MB/s in 1.5s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 82.5MB/s in 0.2s \r\n", "\r\n", - "2024-09-26 14:57:15 (11.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-09-26 16:52:44 (82.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -241,10 +187,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:15.939149Z", - "iopub.status.busy": "2024-09-26T14:57:15.938782Z", - "iopub.status.idle": "2024-09-26T14:57:17.187528Z", - "shell.execute_reply": "2024-09-26T14:57:17.186884Z" + "iopub.execute_input": "2024-09-26T16:52:44.944544Z", + "iopub.status.busy": "2024-09-26T16:52:44.944353Z", + "iopub.status.idle": "2024-09-26T16:52:46.315902Z", + "shell.execute_reply": "2024-09-26T16:52:46.315340Z" }, "nbsphinx": "hidden" }, @@ -255,7 +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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -281,10 +227,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:17.190094Z", - "iopub.status.busy": "2024-09-26T14:57:17.189576Z", - "iopub.status.idle": "2024-09-26T14:57:17.193093Z", - "shell.execute_reply": "2024-09-26T14:57:17.192623Z" + "iopub.execute_input": "2024-09-26T16:52:46.318115Z", + "iopub.status.busy": "2024-09-26T16:52:46.317709Z", + "iopub.status.idle": "2024-09-26T16:52:46.321236Z", + "shell.execute_reply": "2024-09-26T16:52:46.320645Z" } }, "outputs": [], @@ -334,10 +280,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:17.194944Z", - "iopub.status.busy": "2024-09-26T14:57:17.194599Z", - "iopub.status.idle": "2024-09-26T14:57:17.197554Z", - "shell.execute_reply": "2024-09-26T14:57:17.197086Z" + "iopub.execute_input": "2024-09-26T16:52:46.323141Z", + "iopub.status.busy": "2024-09-26T16:52:46.322796Z", + "iopub.status.idle": "2024-09-26T16:52:46.325840Z", + "shell.execute_reply": "2024-09-26T16:52:46.325375Z" }, "nbsphinx": "hidden" }, @@ -355,10 +301,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:17.199051Z", - "iopub.status.busy": "2024-09-26T14:57:17.198872Z", - "iopub.status.idle": "2024-09-26T14:57:26.446906Z", - "shell.execute_reply": "2024-09-26T14:57:26.446343Z" + "iopub.execute_input": "2024-09-26T16:52:46.327592Z", + "iopub.status.busy": "2024-09-26T16:52:46.327316Z", + "iopub.status.idle": "2024-09-26T16:52:55.436229Z", + "shell.execute_reply": "2024-09-26T16:52:55.435663Z" } }, "outputs": [], @@ -432,10 +378,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:26.449170Z", - "iopub.status.busy": "2024-09-26T14:57:26.448693Z", - "iopub.status.idle": "2024-09-26T14:57:26.454297Z", - "shell.execute_reply": "2024-09-26T14:57:26.453763Z" + "iopub.execute_input": "2024-09-26T16:52:55.438417Z", + "iopub.status.busy": "2024-09-26T16:52:55.438126Z", + "iopub.status.idle": "2024-09-26T16:52:55.443647Z", + "shell.execute_reply": "2024-09-26T16:52:55.443211Z" }, "nbsphinx": "hidden" }, @@ -475,10 +421,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:26.456078Z", - "iopub.status.busy": "2024-09-26T14:57:26.455769Z", - "iopub.status.idle": "2024-09-26T14:57:26.817319Z", - "shell.execute_reply": "2024-09-26T14:57:26.816634Z" + "iopub.execute_input": "2024-09-26T16:52:55.445403Z", + "iopub.status.busy": "2024-09-26T16:52:55.445074Z", + "iopub.status.idle": "2024-09-26T16:52:55.783623Z", + "shell.execute_reply": "2024-09-26T16:52:55.783066Z" } }, "outputs": [], @@ -515,10 +461,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:26.819374Z", - "iopub.status.busy": "2024-09-26T14:57:26.819176Z", - "iopub.status.idle": "2024-09-26T14:57:26.823791Z", - "shell.execute_reply": "2024-09-26T14:57:26.823316Z" + "iopub.execute_input": "2024-09-26T16:52:55.785701Z", + "iopub.status.busy": "2024-09-26T16:52:55.785338Z", + "iopub.status.idle": "2024-09-26T16:52:55.789881Z", + "shell.execute_reply": "2024-09-26T16:52:55.789413Z" } }, "outputs": [ @@ -590,10 +536,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:26.825588Z", - "iopub.status.busy": "2024-09-26T14:57:26.825150Z", - "iopub.status.idle": "2024-09-26T14:57:29.558927Z", - "shell.execute_reply": "2024-09-26T14:57:29.558069Z" + "iopub.execute_input": "2024-09-26T16:52:55.791534Z", + "iopub.status.busy": "2024-09-26T16:52:55.791208Z", + "iopub.status.idle": "2024-09-26T16:52:58.424549Z", + "shell.execute_reply": "2024-09-26T16:52:58.423726Z" } }, "outputs": [], @@ -615,10 +561,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:29.561613Z", - "iopub.status.busy": "2024-09-26T14:57:29.560961Z", - "iopub.status.idle": "2024-09-26T14:57:29.565280Z", - "shell.execute_reply": "2024-09-26T14:57:29.564687Z" + "iopub.execute_input": "2024-09-26T16:52:58.427547Z", + "iopub.status.busy": "2024-09-26T16:52:58.426730Z", + "iopub.status.idle": "2024-09-26T16:52:58.430710Z", + "shell.execute_reply": "2024-09-26T16:52:58.430197Z" } }, "outputs": [ @@ -654,10 +600,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:29.567105Z", - "iopub.status.busy": "2024-09-26T14:57:29.566772Z", - "iopub.status.idle": "2024-09-26T14:57:29.572163Z", - "shell.execute_reply": "2024-09-26T14:57:29.571688Z" + "iopub.execute_input": "2024-09-26T16:52:58.432406Z", + "iopub.status.busy": "2024-09-26T16:52:58.432073Z", + "iopub.status.idle": "2024-09-26T16:52:58.437486Z", + "shell.execute_reply": "2024-09-26T16:52:58.437029Z" } }, "outputs": [ @@ -835,10 +781,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:29.573957Z", - "iopub.status.busy": "2024-09-26T14:57:29.573552Z", - "iopub.status.idle": "2024-09-26T14:57:29.601023Z", - "shell.execute_reply": "2024-09-26T14:57:29.600416Z" + "iopub.execute_input": "2024-09-26T16:52:58.439340Z", + "iopub.status.busy": "2024-09-26T16:52:58.439008Z", + "iopub.status.idle": "2024-09-26T16:52:58.465679Z", + "shell.execute_reply": "2024-09-26T16:52:58.465240Z" } }, "outputs": [ @@ -940,10 +886,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:29.602952Z", - "iopub.status.busy": "2024-09-26T14:57:29.602606Z", - "iopub.status.idle": "2024-09-26T14:57:29.607644Z", - "shell.execute_reply": "2024-09-26T14:57:29.607163Z" + "iopub.execute_input": "2024-09-26T16:52:58.467381Z", + "iopub.status.busy": "2024-09-26T16:52:58.467051Z", + "iopub.status.idle": "2024-09-26T16:52:58.470976Z", + "shell.execute_reply": "2024-09-26T16:52:58.470534Z" } }, "outputs": [ @@ -1017,10 +963,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:29.609321Z", - "iopub.status.busy": "2024-09-26T14:57:29.608970Z", - "iopub.status.idle": "2024-09-26T14:57:31.052597Z", - "shell.execute_reply": "2024-09-26T14:57:31.052050Z" + "iopub.execute_input": "2024-09-26T16:52:58.472587Z", + "iopub.status.busy": "2024-09-26T16:52:58.472255Z", + "iopub.status.idle": "2024-09-26T16:52:59.920706Z", + "shell.execute_reply": "2024-09-26T16:52:59.920179Z" } }, "outputs": [ @@ -1192,10 +1138,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:31.054589Z", - "iopub.status.busy": "2024-09-26T14:57:31.054180Z", - "iopub.status.idle": "2024-09-26T14:57:31.058507Z", - "shell.execute_reply": "2024-09-26T14:57:31.057947Z" + "iopub.execute_input": "2024-09-26T16:52:59.922589Z", + "iopub.status.busy": "2024-09-26T16:52:59.922209Z", + "iopub.status.idle": "2024-09-26T16:52:59.926355Z", + "shell.execute_reply": "2024-09-26T16:52:59.925893Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree index 72bcf4a6b5247dc437edb0e2fbe314e187c9d905..ed76c89ebb817905b651a4e34d290019b2649b29 100644 GIT binary patch delta 62 zcmX>tep-A(E~BAgc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= RQj%$MVsfIv=6Q^|TmW)Z68!)G delta 62 zcmX>tep-A(E~81<&1cwV47l)l9p_lY?5S}Vq#>JmXu~_VQy)fWME)vVquwPxViMr hL?$Ids%jJ_Pkgg<^4a&jgbl3v@Q|Ibs_GxP%m79INe=)3 delta 221 zcmaFyo%zLg<_%{!4GYYYixbmL diff --git a/master/.doctrees/tutorials/clean_learning/text.doctree b/master/.doctrees/tutorials/clean_learning/text.doctree index 6371237a08a00b08c4b9ecdd05ad90793e8035f4..dfc8692f6c224322ba6a0cc105b44cdf9387069e 100644 GIT binary patch delta 13455 zcmeHOTdZAG6?LCeDU?EOXnFUZP0K?}AM5>y7$VVxcOgbaY_01FNT9S3jkKlD_+hbx z9B{`N8<3b9LKJM^m>7Xd^urHD`7uO-Xp9(t+AkAh#Ieslg?mBIO61GrH0izV?Ye8u zwbmSSjydM-V^5rU_=z*0{>5pOekG=q4cU+-)!r+Uya%~RxG`A4Ywa%TaP~t zFFJ6}aK*0c{)g{+!^iesG~B!Us^OV?&Rua^y6?U_?!Il) zmV57U+wt$gZI8o`?KyY&((YBmPvi4zy9d2ZyM_cEuXTygJ>70P(4h+~8VOE~A*s^Re$_mvlsS5_Xq{OJ>mYwl=F zRub(rw$t8)9JA0QC$3ziWV~H*QM=uVAHTl1PKc!>v{W&0!KpVYYc7}wR*6JO=Us78 zyWNpv*e*mCgK;+cM1+^z%fyqG*=mxsb*VdSX;Hh~=3g#9wdUU16a--e!5$@d!6s>q zP(e@@4eJhDSag)Uvh>ti*~FZpo@q(({n8L8sPl>olQj|DR>eiFXJ`F>&8cDAe?9k(tsEW&)KYq8`QGKuZ!V}dsH8X^An-pbSh+wF(j5(_<4Lzni?9$oQd^|PvIzL|Tfy0K`zx%tZ8hUO4Y=U2PyE7Av+*-^q~{IV^yuBV#E^T5rHCk*?kr?{KyD*VXB@M;N*k zEDbh-Z+MrIi9E>`5>6^Q$rILcse(rgW-21bV&oB(31dX%$joTl#{LV{9P+p&nIj^k z@F*PA(2%7z1d$Qb=$SRW=c}U0F|g{A#r6!Osef0U52PC0uL15^5=KY^8WlaoUtG?L z^T`>Mpg~LTNTOU&JQi}kr3Ph9Y0a3OfQvfAG)FO(SqbKorGm!nIEob0#&+N;+H4O& z2L+2Qyi{}f8`Wj&xaRXQX0mG8g%sKgt%Whjb0KL%Bc+TrxX2aHJy(5x(V##Ln&roO zD@H$jsXAEgp8hy;A$V|Y(*n~kTwS=c7HW0#=#t*bnoK{LoQE}(b&Duaj#~sNu}($? zkT~uTqN07TW&N#-<|NB0s1#AHg^c9TgiduU&m3z!gL45*<(ZV?Gx3K0S_+18X%-v*b$p_a6-ai!J_k{ zy^oGlCs37=vIbrX9jF1;GB5<1#axiNi_==P@Ju9dqzwTIiTk0NsF;)@frgW1tY9;B zXbT0K9oi24qq=YqQFandNPTdR3h8sSk=Ovn&~`E>D;dhP3Cy9^Srl2~slw_gl178W z;sk6pwN^x`5beaHUQ>s+1j`Tzd6%?~lUiJ`$+|<3g3S(Y-|F`+UPtFwH03B55P^dc zPJk^%#vNFB1PkKGAd~!2qRo?%R(h9($SK66Sh%`$U2REQw7qxEpm3sjD06s>96fiI zXyH>dp4yB@iwLg}?8%B2Z5xXqFQ^TIWl$7kQH{nmXKqBZd|z)|(OQ(L0+z-f>5?paiOk(mJz zPn^U5sNg65lx1jpRj)yh$HY+(DN_arEqF;b!fvP0X; z=kzaI&$ztd9RjH$8W6#w0c8SvjqQ2bW>BCcXOhP6gSEEW}VD zKHpW@3O6QO#I0Nh5?MOc+mJhDE>L7!w0{vC(xVY!h)<5}ZP5Y;Gq1R(wuzx$C-7>p zG~=A34qQ`5hp0_maUa*VT2dN`*2JL&5Wy>Jqf7?;h6U>ai}O3QtNSligXvDl=5Mlh zb6s$Nw1?Wz98|qC27ne?G>s^P14tdfQ9>dB3r6=T<#TPV9$04}9iX0Dj@+P>;m{N~ zu;fTY5f~q+c?WX?Xe}OFf{~&@BU;&vgo3JxqdG6y7^5Sse-d5Ybo+UNyXg*$543UM z654iDRcI7IS|X6(G^kue{s0!)q{X4&n2>6O%t(yXqndk-vVk@&8qzXFVD5!#pg=hc ziz-T_8YEX9rOnU;7&N4$Po|B6N#D6>CA7p{skBu9xf8Jj^k$6lu9ZZOur8PzJyw zBRULFOQqO^<_`)2O@l7=BmASyf<~_l7%BYrfk5`a zpE5_w4~pVW_6X8ys`12728hU9Sc6dIXle+_1{|}~Lk2mZW(3P76#P9`IpGCp0dCVRMOQ%pfTQq=1m-We@^M zvenl-8V3<5N`3p05=dpOtT2Ai1%N}9hmwhY2iJtSb&LYS26_a6+!?!!(*dsFgzO@= z-~elmx!z}Lya?`%vp@{gRoJy!wwh{24+-6J@;Y-oGtB@vtI^v9stmph-M6$69XuJm zwYL8v5&<3y2_@33l0Y+RtfM-4^iNp>cy>~!OC~$_r(0B^1KcTh_AXgZ$$B-fg=0wa z5?R4RO+*m}&xKZ*!IqOVc%%^;vu%r3O%*X0z-{`&kc9v$#OJ$Kmu~DGxJ^IPID1Zn z5@Z$1GzCH^lq?aPg#wL;bs5j>Sm;RD9OcA051rIP^~mVgd}#GW0@K2pn3IvRJiF5DA%Fx{vEwDYAsQvTnL(g zG}0IyL*7TO2aANpD&3?_&@MwqNv#48mV>eY?=#%Xm#d$w;&91Xf$b3IDYX2yKUD-1wyZP>;8l~e6-4~` zm@7TeLd=x{bm3|;{m%H?m@6Htecg+j2fx&8wLF5NyKavFdGHDcTO`K1(aG5;VA_w_%tis-z-=}Pm^O&$I-bHD zVm1nxjRG*|FDUVyFgF_o%tiqxrw_AHz`_Yfr<34^xK5dk0%oIt*(hK#s+x@gFpQXu g0%oIt_x{lT|K})Rgi*k67Z?At!03TP{nh<{12}`$;Q#;t delta 13327 zcmeHNYp7gT8TFirP2ZX}w~sWvbDF-eq&NHh5Jg+6w9+Rc)S4Lg)2h%$i=s{HP0}E! zG*YJJP>XG1!DrEws3VF^gW!)MsoEb^QH+A(y9p>tsp2{_lX!2ev(-QTnS}hw>^a}r z`}@|n*0=UN^4OVs9y@d2cTS;ORJhB%Q*PeCqR%9xz_sBvc@kxCS#Uyqrp~e?66M|t zw*N1u{QA9v9ow40x!bNEd}G_igTHNCTd!{hKi+!5AZ(i-{CVrT!QO4>4}OY&=il?j z|8(g?A3ikr!{VC3VDakqyRJHauy4nT!7ba@p1HocJlyW?cxT$UdCR-*7<_+m?ch(_ zFC47ge%9bCJI<>&-ZQxW*J}qK-+tTw`t?=6=?_lddGBETuJZ=Nov#_ZW!D9Rbnkg* z91lE-`~1!|gQxCYGq`5gs`lV@)#|~vd)0sWmHm(I+J8l(eU{34nu1`0WbbvRLTb%e zPC^AcIP%oa@*?u=-2PYXJGyc{29Z3CL5b+_7-ys5o_H5U7C8>Ke0f)Y`Vu?1|KK_M zkAD4UD;uF>WJ)TM3BF8)OEfCRB}%|(6vOD%ii^5e`|LqHPASVAD#%#L1kKu5;kb7p z8RD>eH+o!gQTMn_Pp>+;VU%;0cum{iXWF+vR$bRmGF!tGO`HSw%2>m3!Z;$~c}To{Vr_5r*S=Q0ZAIS(qO%NC zdY1$ei(^$b3KUCDF}Rk#P_J&E|73m6@WY?0-e2`CrOM;Dq@+ZsBn=8p6T?Nue|z4p z+F7q29)7s~Vbv5~BDM36S2s3=7f|hy=c*0uHy^J~?Nc4K=7IrhSr~={F>1BRih3G? z8~*&-`py-7!iDB3$7Hc8dhL=IKBIC?3|_f*$NTVx@3^M^V%216WH6b^B!iGvI%7hJ z+(*KQB$N+t*Af^k7Y2C}3W zZ=;dXqP0kpoYW%O49F1?m9$2n$7er0dUx;1s_%uu+cKHZkvcHAAO&SS=$MmrZn*dM z`am^5J!1Rla}_NHY+t{xzHNam*!Nt$;jT^V?zv}uvyLh!ER~$_#6(KgTF#WWGIR7E zruA^ggY_LN`h||!;w9&X&9ADDRf~m;tV7W2Zhq_L8*aMh4cBij6r)?dx^Pr>-KO*jGIgXyr#|7FL7a6C~ITD2M z(L0}c;2|hS&;z3%(!W-y<3h}K58zV_+2!R;^V%~j13-huE3{LdC_$lqJmW6q_M+F; zbK~BJN)0|5hlYW?Kph!LxaCf&z=Br8BX{=>RE=gltE`Ol$qAOJRp3J-W#9&F1~ooz z64RqHG1@`D5VF1MzS^``{JgsCN}T=@paEJc+GN4RLO23J7d`dhVk8TPI;x-H;TP+T zE9R%l2|GkXf23M0Wo0>X3e|W5&PoRcoV5>}TA$MtHZ`ir3CuK(H$r<*2qX}iG?dU3 zW6nXNum~I1}*vl7z~%%dSg0co}^f-u%Yi-m0%shIHUwWJ`ntX`w)YUofUi2u@ZS_4vfa^;lqBn7Jf^0_&(*C{J%wMn<68 z5{PTK<%#NG)knWk$?_bmg_^LCtATmL0tK-{I(5}OH{3qg`#iXKdSE$*+0>wfw6EJ& z>-M2ueaV$U2rU(jU`CuCXwt<5awMpI5?xdQn%ac%lP99F@Di?l_u2KE`=u8+cvF38yYKPp zwD$DX^(*=*bRt!nC>3x{yOt3p^j<*C+B6Z4jfBSPbQG}}iPzAxio|R!0rnWQZ3npn zP+SmJSyL#UD6n00V1QH*=9sb>x@<%TrH~WJRfsqS+>>Ya)-@Eu4?`34v10(43`8U0 zCQyd+_cjmRk0E~Nvq27M#Nl$m;RJNZ%#aa^B=nl$-a}A>ec~C3XsAqZBdB6dG3l@s z9!a)d4*}jMfwV%zG19{Hi0!IZ_jLQrxxLG;v~oe-utYi^hrz1RqAVyAGZ(ji%4 zH61QKSZ`U;FU*!*NzM)TeY^T-wMYtiIRQV@EAj+JD;$_ShPFPPx~bGY|6FxxryEa# z5QyLb=yGX<7tsW(c&D|J&&=78#zHzZCQ_?>^1vEC_sVjGS>6ZCGc1p~>=NDm(7ieynqBTp7xsHmYz*F*Us$!wIXW zA67bsv4$45bez#n>RV%+N9q_!0s{aBGfa$>N2$HyakBY&^i>SjKplsFXVLH}d>Xi1 zIk~`DyZwFj+!B!W1*ZW$79Lm-CJ_s6fCZIU76g(j4p&{<`+YS(J#_1Ct#53m2Zj_K z3CF+RTA$JocrwvP4L8ceI*YJ!f`tGRQb8>p-%1J?MI)9%`UxqSSCMz_0f`YN<#5lT z`YE^`MhbicI|yzU6tcGpkWD6c(+~#CW3VcO9M$Cf^oZ@z!xd|fxcaibCRD4$6j@uSPb0gX`<~HM4&aty*;LnT&`q;p6rh}J38`ao?Bsl-T zv;t8IjY;5`{4K;z6VX_RI}#PmV(+Z>z{cLC3wA-7B@>#0`X&(;5{6=k4H!F}XQQ}M zPEoE$6WUlk6>Q1j9@oQ!R?5-|=qXg?^3|ndn_%tS{`5U98PNIy0s{tYS9ma}7%m;a zR1V`o%3h1`&;H+tPTmKJznEPeMW3Y*^)!2EN*H`>xc3#9P@700MJdD>htViJh8hj` zi>Qn-0|+%8&VE-lg}AQ$(cK`jQp_>ZKD4zyqe%oEJP0r0ox-m1oefMeBZkh-g-tk_ z0cZfi;nF(*4oK79a@!FzOo0pzh?Dt@c}X#?b|ltWq@f#{0Aoz^mSA5KwO9-~D$u4B zv)ct}@7z_d!I%`73g*4b+U4MdL2@)AO*%#?=y(^Un4Qn4+7I4WU%J5gf>}alXlEiD zOq`N~Pch(P?vQ9BFzllsaR_G@{%^sj??lrv%~;CLa@3S6a{21gnH}e|!mdUva7p{U z2Ws=mPrUfD(VyE_ZhGMMRkzN}{+Zc7Gy5kuPbZ9QGqeBS-!{z5{+Zdo^a^GGc)^fUp6!Qr`<2i%>L=V_{{8ou)DjNZnn?N{?Tu-W@i8RieYB< zkM1vKX8+9WpPBt9TtO|L=VoUA2^S1AvwvpxzrtbjD~mt-A8y;- HTi5#sZY)Mc diff --git a/master/.doctrees/tutorials/datalab/audio.doctree b/master/.doctrees/tutorials/datalab/audio.doctree index f3ee38213764cf2c1fe232d93dda74f34c6beafd..46c6956325a26334c18e2811b746802f676226b0 100644 GIT binary patch delta 8955 zcmeHMU5H&*74~}1P{u}@;7pW&b5F;{JeZ&Tv)29*L@QP(Sf`-~N)2KE5)o}htPfJE zR49s|V(NL2ih)8=p|ql2Fj(z_4`M6LL%}{M`cyQ6>4W$p#_!xS&CH~D&y#OAFyS(D z=B&N;THnuFbLHuc7oXmE<`?TyeleF)oj6lj+8B*1ky4Sq)M(jqGOiMPvNA?aAqDa1 z)%C}&u21mw=K0n2=~K5)rt|N-XS(&+$2C8GpAUBlpIob>4(k6&;GpjK%Pj_wC9OxNf4>gI__yF7NKA(^q@eKMxo`2F<6DHi(EA3XEdHjfAgQy zuTLIXUMsVWz<1yKiSzgDeCV!^oFC~;d+_Oz`^($sPPX0Vv1YR!NsaqHy`%fblg--9 zCGE8{xrUqw9U62rl7!SGB68^fkB!AGY+1vUWk6+7Mqod<3V6n9OBUI7SAIJ^fIFK_ z4k~0>Or`(?Y9ilXFFV8DcF!J0*Is+E`NO0gIfL!~`p4;gv*q!*yYtZ6@$SoinjV{R zHAzK{PNPg-3xrlN#2y?qN3#gM$O#}+6h!pSii*>zn5f68j73{gwF6WK5Vl5|j5lQ} zE>Wn`8)qBvB`1NC<~5MRq&EU zu^M6sJ@7ZFr$K2N@kBrskAdE&DQy+YK*KHhY-oCXwslGc;^lxUa zGMIoeA$M?JBr+&bkczc;X;&PI0_g@wNELxp3Qh^*vSSpgIF^eJz9DiCO}0cvjg^dq z*OeSm+sZjunb6CH7gV;3wUPMs@y|f#M#Af!KhAWwPMTY1$%i0)NUo|x5v}AD&Mc#p zAW2Sp{4tZN2q2Svfs|w+`zEN)Td2H%%I{%qmJ$k|wMlpnv>ke4l9oOKXRZX(J^!zR zLNRg%yAX=e_%~p8eZ4s`D=vfoTd37Zg?O!VVR*pvc#@%t5gB(CXm0fj6NJ80hK+nN)noi_V4 zk5f*$3MJq-D8gwK<*RY%tsH`?aj;lzs6{BNZSfWjC1J&i0*eM>mOWS85Ilfyq*ym; zSX$nxd=Pvi_hxp%cXT$AS9kW6=J=p@46=7z(Z@jCyZN}`Y)+K>P6*L}2^B||MF!B5Z6 z%H~&-7F9A?YA+Z(L|TOqy;dSR36Tsibc5K9L_K_Ms%FdM>A`A#^<>kw%7orO)&wmg z_yn&lS05NQJ>xFi(3|fB0|;ACK|>bI0WFTjz`qF8Gj^B$Jv};Lc8>1(KCm`YmxJgT=}3Rm)64Hm^;moS$>wM~ zlGH*RcFiS7B?L#fbb~{z zK4S^3)1q*7Xi1p~jO!JCGZlg9G3w0waW)d3x=-EJoNbqf7iAE;^$$01oq2#HTS^iB zk}9Ykg*5>qTY&hn>(En!S#B{GV7LK>62pA<6GN$jtPQZE(vcj5p;aDOR9;rJJ;n*+ zpqmvfpQM)6+1LZ^my^-BU-?`kM?(%!yZ+H;yUp1-gaUqDCWdaQIY4b@ zi;^G~N)RgQIio~dg{>xYC{fR!{|U%qhWXz!hWy1Gr%Uuk#0r^VKn)Z)zjR9vw6T!G zFgFr0^f}n>wzai8PP@~}?SqA x%l8_qy~<>3wO7F^XSG*3xGY-jRr){0uJ$Ucy~<4+zyJGQ#m=8NwD!}t{1@RYA+7)b delta 8847 zcmeHMO^9Sy6}GH5Xve{skXA8^u9`M63y1FebMC$8y3vRcB8hRB%>)^m`vGyg3Y*MJCg^X1OuL>2ZVs38brvrsj#?SK2`^uA<KD{9mXtMytF1U0RY$CA5Jbl0 zs7lEF3-@>1!{t}HzfR_j;M6~Tse7Uw{M|oHzc?f5Yjj2gFCA5%V<{}E#K}`grVQc2 zA`wB}t7If@ERf;CEV94$T&du=kSIayg{3gnQ3XAW7}5VWmErd)g0?u|=;$A-oKcoJCVp zVdqNpF1f0P%kOo+m|SgCCGR5Oqu+e;;PEHE`p9DkjXu)O3|w!iP~>8Izd zjCyqA1O4rPO;5~=t4XnBZGjq6A^=~8RmdjDD#Ws$JKDOc6)vFUh-Pyfs&I}A1+9Y| ze);d|%cv98jJN9oLsSgM01!qF)_ZL@&=B6A-kLN+_Ba1NJwIO`QR+nw`~CCpOi#@n zfLtxu0d)hvP%LOka?)8QrLwtGK%W8wa+H-vrrJxqbrHl+ZXcG;M%z++?b`nc{8T>aG-y9WUq;dIFex(MrPl&&P@O{t+O z>5<6*hL|aIrifkyB$zb;`c)!h<8kxC#5)8LrJ)ooiYv%O6;sGHndm`&TxAco*&c3u zulvoU8Fx&?HsUT4xwvQSo_YL#=rT@#jWclMHRXU)Yp|LiR|Llz`ZvCgQ+#lH`Yn(~ z2}Y?(3;+^Z2QRR~HJFO7vYdwRzSsSHa8R`M9a zqIE?@os=qyG;#}8$>J2!97*ZFFMn9 zP^YYiJ|!0{B^B6YErZL2lLgs_u|ww$6cK|d7D%HSY>+zl;5o-+QJz?9kat8ZQMFlC*cewSNf~uC?43?V!HJn(Y z;MBG{zic%xgaq;BEMYxd($SGVbQvTw`y!S&YgK0zIgOrlt4}xXOdm;EE2U#V19RfW zv$QEz@TUk_D`D9HJ<3I7>Eaq|gy)t?2#D-R}In8GKZ=&vmC}S*ptLOF}`;(TS1a_9CMZle3x!cvP50 zo3fHg0E8}6w3hLh(*`VCAk}iI1v@sB%5a_VFHDv+jCc%iUPcO;sDJB|-Dmrk|1{ki zZafBxoi}3ZCE^;fm%{e9Za$B!Wi>`=I3{u+b@(J@G{G7yyzi0(XO58uL$bBC6uk-H z%aXi>d5o6K2&F@bo+$|)4aG>X2c@bxQxOEquanhT3}^lVliZBENJP%oN7aiwT>s;L zcAwvKW{;1Fn_f6o2em2{po1LiBfce5#$<4xnq#CEO7s>6d&Loxp)C0#IRLM zz{~=lbO^awf?))2n&QHul_OLjqGXj*DJ9ksM&o2gn8Tc^8BPIqA!>LRh=QbQw2?X; zM_em-|I{Phxn}H#54s2YAG|X?*?;_N-N~8L0wb9!FjT0jgX0B3%3MoKFlC}Y_tG7^ zGOGm%xgcQA7M=kGcQ|DYMSyVZaX&fppj@BX#02qlu*}uTg%wQ#@8{*bIZ8z106apk z5qs$=|MQJ*cSn>8Uyh+BD<+4j*h2cy3=v){is4IFy35@RH7UVKI5VW_vy4eZ2w2v+ zkcM8vrM-?}5~aq31V;(MhO0p}b99&l%fgVlGV1kgeZ<B1??F%ceuG!G4&Cj~pDnfR%?0!5j+4w`k3AUTak5 zt_iHsop&LwbzMh@Yh^Sdt{!xJ`q#rp4u39v?Ba`;?!B1v)PbrHYA%3$!0^c*^qqbUS_i)Sy}sRHYDR0I-3p2W3StseP!q7pC0J=Nv%9l9vyCWdA^4F}5efSV_JUSgr19eiTx|Re zX~L~+EG{9PpcAh!su-mIs+pAM6>!+lAmU1o(3<Z z!Ez25vvj!xGGwCVg2o}q5KQ#Z!*VmxHxtc+Z?oDu)FAH}u(g;i*j?>R zckPcj-LH&B@0yk&>C9Y8s_Iy6;K&M-19IdGmcCy2(6oAJhTq=>w8O8D#nTpKF$6`* zViWpQg2{Em$g zhqAq!t_%J8)aZ5da>%Q>`F8I1;=`r8_ix_1vosJ}e^@=Vx4Fmj-O5ifeFV0#xYSMj c60e4fa9qgaLLL`#{~!79g{1z;nbDiczepK9$N&HU delta 1971 zcmeH|J8M-z6ool+17Z?XP!h249xhfI%u=87W1= zU}N)mvs&&FwI|2b(wCYNrtC5#5(YJZ2ZuU)PC~mRB{dhv?09=@huxX#D=oQ6!KC6z z@#wOM7@Tnyh{&Z~>DdqLWmQuYfTRyTWp?uVl#c?ln6FypCg(qf}_MOks+;<(D;Yz_Eep`(IscjW#kxCwv#xx zAYa7RMwn>loAW=}k#?omdsUqZfS5S=kYWjq(5aFk1SyhB+_e|R>}^%cE(bq07~jY2 z5qnfe1*#0;&Ss4&b8nnBQDRoMEKBUhIoVR7!9p20If@Vv%OMnhV982peFpV6O`TC` zqpd4f3Q7_dmGJ~Y#T11WzAgA~s(y!u=KBVNeGDHl-n`yoXAS`vIA=t&w#DXar@4^a zWg`S&NIZZk`!F$ZVXO;#06Sj4p8%d{*JpdrtEd0o6xqQ-J-D)TYk2L(rOQ``U9Wbp ux+}NA-Gyf5E1TN~+=!iSroXXg-8DF<<3Sw{>bU!f{O>xN_Wse{i!Hrd~T2MTQ4);z<2crL~cc# delta 234 zcmZ4Tg=5JVjty5h4GYYYixbmLF)=DA7@3--B>`0#C8njB7^N9mSSDH|rkExrnx$A8 sCRvzmSJPr@Vp1ZctVUtFwHDLT>G`@$y@bt_(PR3}PFR__8Pi*40KT+CtpET3 diff --git a/master/.doctrees/tutorials/datalab/image.doctree b/master/.doctrees/tutorials/datalab/image.doctree index 2b46e857785c783ac56a39f121c49bb76bfbe058..a4ed3468beae40cd035b3b642a5d8fe41483feb5 100644 GIT binary patch delta 27238 zcmeI4YpiWmb;sRj?{j$g;wl&ADrfI|tEgN!%;%bOf~j7kZK$GvtxwR-wdPv!RndsG zjRK;H_MuJe)h<6&5fiHwYoJtj)lemAYNBcDl}2kd6>OtTF!f?-)2e8HbM1XD=Nw_J zZ?qpa5#rrvojulkj4}S>{~zOi`p(Dw<(-ea>9eOu`KDCYjTh%PR=Tn*d|jHVmZ5G+ zXPvDI-xwQ=lVxeESVeKmU8mf5-c*#XtdWIsMdg*y(WKCX=!|u$C@NVO{I~nM!H4qKl}FZ2o;mL;>pDq@vlU76bX#X5%`%Im!Bo2NZ& zW{W7jOxBx585dpfrD~!rRUu2)n9zOQ;6wTA!oNM|q;<0utUvnNd+fLnddJosIQrKA z@VkF{%XP;eJv#Bxt}V>JyL$^?aQ83vJ^H;9KkN)|@8V@Y|MB5lj@|QtwW%zkcR^QW zSsM{$tW+s&3_`mmmMO3L?Ss$cML+Yu&wSX_T9vr{czz*`Nzym9Yf@cSgbx+f(d)kV z_SwKQ%~8H>Zq`SOJ=DH^<-So~i`}`2?Ya9#-7fC_xoSW8@X?)lt&7hOx1WE+=%L*& z@7%>}AK-~+wfjyRefYi~c4oJ?@WB1qoO4URxHgkkRj$@C)hwPJ@H!-EgiA#k>r(3O z?hHPZ7yh*UBkrQ9Y!%8_dQ~+h5rL)iu8Q7^LKUu%-S-VWG~ahogbaqe(hd9d-IRBAG`2PYX{nMe#&Q; z{dCu7*W}Osdld*@GB>Z?u} zA9&oURtqa;yZ;eeqHv*=H2to``q>M$?eKr#!y87+M_#waXV0?|sEdSG@NX`(AX>=pheTUZw~ACFY+zb^M}7Pj39q z=pjd6f7yAh+Z;dcETe3!J@`+WIySL3z&K$FPu|t8eb2)ur_b-WW_iU^eDF;i7#-6Ro%w%U~QI}dOC)o2|NFR+3 zRZ!lkCXjjn%Z08NtC`wVu>c(vKQy&1gZG6<##afXzx?9oJ@Z-5 zdHKNQOuL^R*sJQ$73a>{|18J*rvnRW-_uN<*xvD_@jCnOeWSfnw&dCsxc~q-6v0+? zioR%%yldR~+)F3ZlG4gkGgPUmC}KcurYTjbZDXCcp}p=B9{tMt`nyJ(x^#t)z*xmI zO&v>sfKc3t(#eF+-Ma9(e;myQ?2GnU7mc4k8!)XquWP?|(fEXA#>f?q#6xl?05Wbkr)!Fvo$eoJ=jTqD+kq zNl|;5x+HXRZ&f9QsHNNCX37R27%nL(mx&l&)l&OJO<4$6%0(1>`n_X3vfr}r!p3aC z^3vDtdFlAc=c%n-uBLra5O*m6fQPk+F-S!nRELttEZTz?5#yhJ+JqS2UytWYw7)%m z*Z5)cAADqd_2}Axv)bc%y8nt+yFG7U`x2hl-cydxnBhu&%Tw*akBy(Q8ABnfWJ^tS z0V7AeCF!zoQsyFhe%oDyo6X8O+eD=;sf~CjxPs1>g|fEMs-EBW=E;XREF%;@(E_;6 z`PZeW3D2=eQM0vXkuu3{-xy7MZC0xN(yPap&H9b%X5nbBe$Dt1Q|*0;_Yxr^m#-{J zZK%XFc{YLf?IH)0t%)4g2Op)Yn??#8r}SiO2lrrIL%z53>%Kbq{Afy5o$4Hi9O>V> zIuM3}5RFJpp;FNve1DEF@5ouB-?Uxc(SEC@z59jhFW4+p9Yiptf|N0eh-N}T8Yl@H z6#9#^m==PHQj~$Lt4$+daS|XxvDt<^*L03H|IlBre`2&r3QKk6LaZv53QG{9a1^J$ z;?$aazaQJa^(Es|=Cfx{KEMI?ny=csKTc*FFyDFG65VJ9A?@-V|JZ$-fOJM|_KhDwrV5EvV~KFOt>*Q*vP{ z*u0F+gi%}vU632O8W0+(42vL!*X8`yZ{M{S|H1es6)w$i1iKHD|NhN`ymatAq zk-m~Q7%iOUfNM$qT0%-b;N6P4;5h>u=^+e>3uF}q(+O7VAmt1P6Wn@!Ut9b)Q!)hR z=)BU6Evhho&x^;m=7sbdUBJl!o2`7~ShnA2$4{AgATfEg6hr`1OOw4P4_ebYHogd_uDPDQi2~1hVx@g$VMnxQ;DIld(<+JOh0+cc5 zeZV1!qZ5|29f;ow3Xl8|d77XC%bUXd=HJesa^Q$|d(gn`RJ)%Z*f-gM%CkSXv41wO zBDrw50Me+5I2CZq2#}$KM6ZZ`;6&fJ1xj2b_#jP;I#Qm&-r3U2BKsvbfvaK41@!D3 zZk7@uxv6CXf-@03Sr=pw?~10PmMiP|rN_p1jb;P3b$i=Q<4b11sXn`+l{b$s+!Qs? z(HAwyke5ldufb!uoP>c%WVbI&_RqiY3+v#N08l9ktrcm+1J7)ZvSlb6sZ%3!@O?&~YHZmO=(3C* z(`2aXgcr39oVXS!6a>qNWI~Fk4kxkSm+d}C9oK8yw>Q6gLp{FFax30ARtvAYq_8WD(;4WZ05rl|^jf<_?m4G1P(c4&Wa z*ZAQ}jM;yHdQU1Fy0yChvX#B6VeupG>K1Adjg*u2MleH5x12U70%Sh2dO=LIKl!Tku z@UGy_5?Z9ODJntmO)li52iDB|5}Je2&?qE{5S&V+9hU7H($O;K4cHaSoY!x%y%RUh z4k)I)O@TaF5zJ~s{e=i3pz;%DseVj*5uD$GHj*$#*l?IXWCK4$~+YO8Py zm&4x`z#^5N8Xzl`XtF9)sVsCQ%(Cd74(N$LSJBylQ`+r60~d4;mol_zC%kQ7X*;MC zDr)3G)hP60Ly<cvFW8f*LYH(l7ePuI*`Q}5<26(~TseZlf)+Xc2+E;pjlLX%6@nU3 zQR`&w&&B8jia$tyc{c~l7TM9{=77}>kp14z#%E9K5I6~m=0s-zE*&64H1QR!ydWJc za@-VsixiC0%6nLGVx=PKOE9xhEPuI*0kggx zT?7R*VD&y&*(JEYIC=8u4WzxTpgLEZDFK8BHfhiSsn=yb4k8Pd4KB_(7}yJ;hBo^} z`JZz&GjLA3U9I;SBsY=YXmpVpxXEOSqJ4ce-oMCjGk%@$B_X04@Mx`wsSeamas&7%zYQ`6 zY@7C57fdcbtJf^=x1rkEnUhn{x@|*efuc4KCgUiAK`=!L431Hf+D@c|8Fh~!AT+ef z;8TQp6;xA|RC2gpqEuec->VA z%k(xy?LbMcAYyY^qRE2@YfNl3MPRB^-l6^T)3(Ncn%C0%0=iqvY`}I$CKv5P4^IxA zueY4vQPb&Ckc7EUN|$+yc@=PT1*B3)8-l{S^S%BakPcMs1eK=$#P)#7p3cAII+E5jp2D`IDWeTu~U+jz?|aTn)?%K@%CQ zrgrCs?3C%~Gq5#8J6VyiR>HSYG}2lFaL0L@IXcAmN3#Ljy7Rr+fLYt#aK$8$O#ph0 zBrM=>B0H3@38FD;gD?v_gb$}`@|+GH--E8@%2v=wkP-+a$$^RT4gA92ZG0Sk&gzOh z!Q;?QlE=u5WIb@WW7TTj$yxNI^x9tF%GrR~roCaZaY1%E3DgV8q5<%3U`BAdWv|Pk zZqy=xbcN~^E(B-~)#T|oqO_A_wJHUxXdfwvA00H_6tW?oQ_j*-f=WQ&8W{(vQ9(I{ zv>?nDJj#ISW$0j^`phoiamS-{E+@9M+Tn6RCW@8!_f|Nbi}c6%CqNPYX}@U&U`n=jtjPtDc0r1p-3le0T*ZWBo8sal(4B+P<@ zOomwoI?-(e)!2UJ-CVmxI7J6ir~(w?RC^9izETin;6$o?W2-qL_w}P`r4oc;su1hQ ze)--5XMjBsC+7d{EaCc1tJuEl+R1aL{Z>4zr)?KX4&nOG!32o2v+2kNs3V_LzY zE_ptxGb(m;JIzvIdnkHO^kyzG<$&E~N*gdqWtpY#&s{k=be`K1 zZl`}`fd$!@{sB5@3%c<*s!VNDENeMG_L+^>O=i_V*WQw)>_45j+g}DQ_RCZ5HljGp!n6Lxd*lAk&a+Xf=@} zwpxf~?cA}((+sz$GE)JBr{^;`q8(u(X#xQf!cx`HIA^ILmC^JJHG#J^Jw#|>4N+3c z?UHT4?m|hNHvKjis>XH-d&0RP>~Jtk*z>s(jTO~}rG|`<%B9jm&_E0x&s`HsnY6#1 zbq>xz3m7=1J#IE|HT`a<9NM+*QVspTcU5{RzL8mSzmlP#F%&wqV`QP2BY+4;TLouwFK$2(igXSe} zpC%0Cmin%c+XjhQ^RH-=N9lp2EvOx+7$8$ofY5`)9L0d1ryD^llbGirTc3=JfC z-Gp4a85}$$3|^u+g#N+X{wvF=@L~v#kYM1bGLr|W!69V|siv@9^aLz4Zo2Qte$;Js zjWhsP&!9q)wOPh;;L&&Hv?1GzoYrqTF8qxV6p|U~DG*>RK$TJkd8z~jqW~B4PMw-o z&|elA02&P9w4%EZMXT{1ILMP~o4NbM=q#|IhyQCZ4kHaQVW>iB_<%(v*IsfQ1ijRR z`wcJJo(2s=-dzZADB*LFNG$SNPF3(V^ueVNRi0wn8Sx$7!+4XTE+I?hMoQFb0w6*} zL5AL0SyDdNVSBKnXc%YLg23|ymMJu4%psiK>E#gT|Ns2WXo+Cu_K#tB|t$e zD_UwucqQqCuc{)#FuBMt=61nL{yB5p{WArf9)E%bxn^{2?@5(=^3=fP-1GFn)^&Z8 z?|b4pBuMY#XilSfYbx**`W>10AzE{;TuvnJWcEAjtB_v& zuc9X4E9RqDj8R3wBuEF8>#z@~+c4CWWw`@GfFymjGq(*GUntzu0lPVziuTuUMp@G+ zw#xWA5E*nW?9UdmNu@)dMTtZGC2j94{ziYzSj239+ zxW)9dA=ROP04KQ_gd2#5_Vr?8f9~M=v(e-GthRSv1=~Mhbo7>uv)j|e#>3hJzdL#8 zCM+mDpQJUsgG>}7Iosxe6VV_|EWr8#Wzl9xHIWE=ytZ@MvDiTfa;%+K}b0fos4fEKRP@GU5MB6V86kRTm0zfe`0j z{zgS-w3Nzh6+07NrBMq@BvIf#;Jo(4XBtg%{~xbQl3oP+{z;q+C$Aq}+k0Z(?M_+) zH+4^n>)%7Zdr66$LBDX{dOG6&eb=o2@@iUO+1fSGwZGlIyqXrczafRyv_PJ$SWOG8 zrUh2h0uMCVv6>d>n^34$(*mn$fd~4VEpo%_E*51qEwGvv*gK-I?40bMP*~7@t7(DN zv_R&Q`n&d5(*mn$fuEaGXjjt$t7(DNw7_awAkTE{W-?dP0uL~zu$mTFO$+S#EBsc| z0;_3(#a~yjnikkQsj!+BxSv6Y)wIBBT3|ITuxo&2Pp{Z&T43*t!fIMzH7&4XmSayF z^J-e)eg+j*(*mn$fd~A{uUFFot7(B9g9@u@f$hKSwZET!H7&537FbOSEG89J(*pN1 fXtA0WSWOG8rUmYIzT^Mbw1Asm^)DOGc=-PU2poVs delta 27665 zcmeI4d#q(ueaAg_pBWfLV9M|i1UsP&Yg3w zaMoWf|C}KqGyCq@>-qcrzTfZf_nW_b?DPj7JN;7+?vaN~eOvXW;)V-U@QtWrotHLu z(Th;jRikqueIqNUqK-26UaR~6ZO_MFK9Z%kd2ObTTX1vvUn$F`(wlc)`)lJO z_0pNwopk>#c69Vu7Nr-y2+^rphmbpKq!c0wp=~XEIsIJnrS5b7(zT<

~Zns?39z zS_fwpe>7fN6HKgZn0_w#Qun!wewojeWz@BcwU*xMLeOQyc zwO1THu7j(CjI6OKwN~025qw@q;dO46>1UUE>HagXyX~}b5v9~oOOXesP3fEzjgn4P zO;PH`&5j}UQa7`k-*MpDHB)T9^Q&fHHXr)_6`L1-^`gZG=WYJQGkZ24dgNdFhX-DC z-3_-_H^*Bq9P5qJY#x5%*Ehr8UHV)<{JBl>%{`kh{mP##escfj$xoiXbDaJ6|NU40 zbZu;F$1aJyR!VRqj0^lGN+)G(v?%>_&yz28E4${OPB?a4*R=?$ba^f~LT`Pp8zZWs zjHM}c-XCG|rS5a?yRSNGSFs_C+PwA2B6C7+-u3vo?OR?lIO_KvIc@XZKeXF_djDOI zpE%fhbl|v!ZR`H-;UD~L`{-}2?QV}hesJmL>z?@eWv~A0)(@uxxA*NG?B6`)$!mW4 z@qzmfKlz7iBO#*{RnzUHuDQbkdk#GCRb@gYrkhx&Ug}nT*?+zG*s&4Ph$5HDVNb3x zPO7Sin0rynI_PTpx#UZHZlZS$`dt*Ry=m{D$gB{H+t{AJcQD<@?LSxTCtfsotXpmW z{6PDK;|4FB{^iAeyz@EUctyMSguzFT{BSz#d=pQ5wlOE{@R!!ck*im$#_PgF?Y;B- zR(BpM7J10EoGv{1Qn&DnPa1F=ahRnnWFte_=t{dPk4lxc4)}rEh~8{dFHQFDAKX!n zwe^)Wy3o=UI!NJ^F|jC=6GB?Ae0%WB!QG?8OPkE?H{%-zllaE9!++HN^V4hl+j|FV zN3{<=y|&&yuGco&jUTU_)PCxnYp1lY{PEiH?O#1QIPJqfTHDt@`pE3ji`%dKc>e6) zyJr`@W9_u|yr1;XPHsQ8-wH8Zw`3&#O`g+&Nzl=mmIV0*(Woe9a`J@Y*+jKW3plUXKUH` z`Jcs}VfOL%(8X)R_V6$}rM+@@_TpXtFU!CB@Y)&esk^iC`51k3@V!y{nWHlQydS{c z58imx^&h-(@9Qrgyx;}n?pJ%)p6s;e^JY#yaZL956NexB*5C!3Z@uQ_ZMmMEenucD z+e%bM0cOgosbXz{s7sOKklMkG*}lo41HQMy(Zh+zFB0ajM==}H%wY-g0}FMnMfB(0mLua(FwbL>u_Z9CI}O~wlZqb zG%k0Qt{RRY)?lvo()+S~?YP7_iKnyUCimVv{K#Ovv3yBa(buImp>RbARnRhmvKkX6 zA9zEz`qTub%Sw)a=AUP0wU6GL{Y-nvW#?=-BX0hM3vRo9|MavRLl>HRRRDWMBPuBh zO_=sV0jw+Et?}S+eK{RzuYUJ%-{j!k*?R_ercP`>SLE`m-f+W}uet0Mueu>m+}7=H z`{lb9_FsQ~`;^a49;a5-o%BoF*VNgrF((zkCelaK8A$|F)Q$6yf+l*a)a;OD)#RLy zhiXJ!ViYFnC! z$TmKDW*Q*^8cXyAj8*x%!cc2nOxLq;X)k#o`+a6>s-~%PuT1TABMM)s%3$QcCe2XT z_7%g=4@PX&b5BAw{jE(+;L>hU*y1_sF%}q#g@8bzgK!Z80ZW#(jjoQ9d%FudcG3Z- z0n|bRFw5xR(uFH+WrVOrS@iy71mc%9~o~rYf687ZdEN^OXsT z-{LC?6nAI8jrk>xXuCnBuIl!;6Pwp`=J$cKvy<1CFRPVr&(5A2-$>#At+BWoQ*(m~ zMT_CUo1>wv+dsI|`|CJTb+rxIt_KXU(7d#I#=Nc%l zno^n^%1{+j%W~275`&xe%Qt7&BnOwDCT84nOLp8y=O#CB6JK*Zje%YGvZ@Gdb?t3c zOo#&M(BXgpbrA}Qj5E8a&>T_c8Yj{brlNAPF%=hCcon0lT6SjN zq`#<%abO$GNcZp-w*s`e!TP2Itd9by{ ze{lbZjv;V*eF%4GGc`^#I9Qf76+lF>1$mSu=2`ez2u}^#uOc<(g1P<5y9jo9vEdf& z4H!|@g?7Pq9;&c~!LtY=u??=7-?xOk)e@XfnweYTn9OzqO5M)wZ>Kiyh`B9YRr~m< z!`|H1eavk{!2ox8&6PsS!3|myn*ud*a@D(shlXQZdTekEUYY=7$I)_GePM+%pfocJ zU(--G5={gWfzM*4bS<5?+=)DgN;k>{H^DN$HyAG;HrMT|e>ZzodfX0Zo}*?cQ{W~; z(G+#jFw1B?oE@dIGN5SX;@oo-gg@zX6h;DIREReLCsN`Z@C|W_TXsgdQQe@VIxdID zLv&UgoCs)Wc~OK~i$&8*%)<1Swpcz3-Cx?Twb{9&rnW!}9qWcDj1%*=kd-flC2E;a z_C91(7aWUibO}<{zN(!GKKQ6`8B$m|o#D=9jUyUvUeyh%YojgD4#_y?z-q;n&|(_A zfzpW?^xh;ngC|ZMiuT~c*-`Be?#;#*ScFb{)9qWR(-7POry-jt@%!k@oyS4N;fOHI zKJJZBcYx5&qKlg@ssq5IM^!g2in7Xkzu%+I#F_1|!^E`}+wCy5adSHyCss8*=N@&g zb60^N9 z2%0XP?*z>}x0UozJLqZfhJe!Hv_r#n)b$+S0^@O7;)ZgZLuqQ6TgTx)t3i&f&20e4 zVdKPqAS&Okl_+6`mRql85hO8z8DPU_EStgfkS=ZC^k6m~g9aTVm-cz%f`j}Cbn8lO?p77tpbJ}|z&h{OpT<^rVs4G2#H!k>KRP_CN1=e1 zLR2L&kV|P@g`$E_#p6^JWLWnMV?-p07#O4w03mGy^AQqv3Tk&inGO}q86b?B2nw8Y z!f?6;cN}Raf_VyC5-}H1Xkzf#UjImTRdVqAuE*a#{YZA~sCE*U?p=@_e5@PL4N;84 zjTIDU?pmQdHgexkHSqmp4VA?T0ceGFO=F&&{xNkhsbN8#VT;Cv0+4{HBs~PMtmjB> z`3!V_VN)}h^1s(Th!(TjKvbfrl(EaU zEBx$S0=;K2iXNa);=m*aOkAlVB9{eD1Vzw|6T})|X|;UZI1PlO#JKGj(G0i6L1qUC zjlx4jwLxVOXeuQ7iYgY)o8C`6{&0q>3rrShZY5&Hlm{c#sz`t#7=p@XRB%kFBWnu{ z0~G5bchwxYCB_Z?P0bTy_BZvOXR@^XYM00Z0Fsl0Q4p8XO148xI1@xqmaG9bxLw!5#~sZgTJ)P0ie`Sct@_% z)>FlgaJM}5-y^CS_)Hwob_+^ePP_f>#J+85F5}dyw(M=!7+I5hS$As(tCSwr9y^W1 zRGbESL+?vR4e+vigDRFq%oY zQ0~Qmmm0nO#)ZQxlY@7%%ei`vJ-uZ5!H_?wSP`(7iQr6XU}i&}8Q%1bPDqkfWHMw; zx-1G+68H#`@gB>ObmoKwjoXL1eEGPY3z6;3 zmko~@+s^5GDsNm7$v7xq`pTQUAUNeUB{Q>=0dK+=T)=lYw0pxV#6zVFaNC()9NNkM zMtO-01&siglb(S50d%@UGswBo&azECz+(>#_g^44M7cl! zi>?r*P?aaifa@zsvUfHqONq;_<7}bHQkHg6`CNW%J4s#Q#%{O5#NHjT!qmEsSm6lM zKzS*!s%hfKa4HBIPQo-I#J^WC#t1f2MM-fONEFF~Y?|pf5FwCyhkRyPy%kQy07L;W z5`vcJgQhcyfKuSeN#%=Hm>Au(zjNpC)$7X#@0_d~F>w|`x+8}b*+$?2s6B#XDQiJu zGs%LB%FJ}c*&@6ft|>xIq^L|-;-c@O;;DUptWPzN7onVu(k}9XT#%Y|yV8I=sjx$| zm}AYv;4`%%HTV=gzxlG^3r9_0${Yeg8D1Wg1BTRrlw@EQLCvrT5*e}0QMlJ7dAcl} zRlyY0%H^bfay3&vi%R6OAi;nKr3BDB)KjTSz8V5zYB9shi7{uqH8o}*xTlGRsln+D zwP z&{Nqd>&w@4gy9kKa@8S1Y^TdqQEcg<>tu{1RK_~j`_(btmr6`>@DXI-9l{}Hb2_q3 z!PJoc-hg^zE6^=G^htJkc9afh95;u|VMCHonjIl;`S8<_mma;7T#m#%N<^viR21YP z6jM%RJw&r*)BOfV_rK3+}!p`u3FF-~W@|=jROKKr3;oJb52~wnD zXP9KHOhMK_vFc8$U~VPO67{#MchqVt&4&yvurw+P3OE%BdGbJps24Q~sCN%^Y0Bh~ zJz!3axL|jmJxHK)8&)g?x|#|zL}yW!_&xL>B6r;gww}xtl7gR(nTGKa_gqHO+;Z1s9W%MZMjBDplDVw^SV9r@_9yj zy`s((Q9FhlXbPpnZBjez5r#|R-UByg_YOwWl%5b2TyV!bzQMH&=%9sDu2x+6X})3k zn8EetwtT$2S2)kvf|xlKpao}U(Hkh%0Wk%#=7BOsgVwX4*p6cWv{CR#SpkNLmPGub zRsbm*`vGdtU?##EI-3s_JXKSGKoZd6ex}iaWbk4tdimH$w$t*K)VTdko#IR~p#^ZA zoH=lw1fHjwk~kaid6sX_Zyq{iG~ZyO}k)q=edUXAObuve6acZIe&hzds$Yb%P-v+t?0 zo~+4T1eb}!YPOqE;+pP&qN(khODdz(+JG@LMKcx@h@rqxlLXBp+o>B{@Mrj~Qnr{W znq77rut-G}^)3iJOCmX`g?VMp7>PpKypsXR z4#cXiVDab<*s4XTjD8NrNTNfm0ybNNL=g_TkDUg9%2qb`j%g*?C^2aJ#2uMT4!K}y z=v64Pz+nzf>ALR^rDt@hY4o<2n8nm3igt=Ir?F0NaPba9R|!Dv#E*Cks<1qcUU;d~?MEbyra%_6BAQ9e&~q#|4z z>K07bf8kBjD6~)C;fJdaXK0zJfqSc4K5l!}M~4?;>0DL~>#?Xli0!xE!zrQV<0p0uY*WS*YM7wbb*#KAoPJLg>@!$ zBM7-9t9MaX+L>%YS`auBXSk!rC3T0ln`UYw=fMyLwscu7y8Kxmx^vertr{IxjSj0u zhgGA)iIYA|t{NSltJ7lD=l~B`H9BtHr9(;io!8=61MhbXYYytQsAZBYIa@7OjSj0u2l_X* z(u1o;hgGA)s?lMq3uo2nuxfNzH9GY5pQ}cPRineI(P5^1CYmdDRK#4cYIImNI&}X$ zL88^5D`wqUKeB3cnD$o8>@Lw-v0!(rMu%DV#um5RwQ6)&H9D*s9d`8NtQs9wjSj0u zhv)jY?^lfut44?I%@wOghpqngRii@_R*ep;Mu$oN&tatM^H+@y&)KZ=f7s|Sx%k_= IfAvNG3xu>YEC2ui diff --git a/master/.doctrees/tutorials/datalab/index.doctree b/master/.doctrees/tutorials/datalab/index.doctree index 4e29de852c98aac3fa0d21b9167cbdfdfbd54956..5853cc9ec00448f96a3cc00ba83322516c138c68 100644 GIT binary patch delta 62 zcmZ23wOndLBBP;Uc}i+(nuURWnyI0AQj(cPYLZEEs-b0ST9Ub`xoNVQMVg7JrHQ#= RQj%$MVsfIv=1GiGxdC8_6AS2@f9-wQo}!0 diff --git a/master/.doctrees/tutorials/datalab/text.doctree b/master/.doctrees/tutorials/datalab/text.doctree index 4d8dd8d199c54a095fffe56444b400efad8c81b9..bb1ef32805e6c63ae715e06ff9069affba882d1a 100644 GIT binary patch delta 730 zcmchTy-NaN9LKp!h>M^h(MnOv$e|#w?#&>oC5pmmEDnz6p69vqtxL|AXlU{e2>A^M z?rsf9fkx0;Q|02`mj6JDOP)fj1kw8A^ZS1Je0w#nSL1G59M4}6N)08xYKXk7>ykos z0aHFf4P6#x5lf08hzUXFbzQ^=qxfBytDnF<@xmdAK?{fA3ok+kM*w9*aDyjdj`*NT zqE3Tj{9uwOypY}BJ?t5;UW_bEbE;OfkyS9L9b1XS;N#N|@1|#_oGu_$JGWKs3>Avh z#>gfv;KWNz0FKPCv(;ker`;1yAVGnr!!+7Veql0glDiff8MrMQmCdP0&UtRPXKJl}lSWUK6GEVIoD S%xLUshkYI&jn((r&e%7z%KfbX delta 752 zcmex8i}Uv^&J8;_4GYYYixbmLubxyha>Jd;l*%T4A^QJDNZS!Hr)ir8eu9P!C>lXWI< zOc9@)lA^_*oS2uKniF4Al$ckXmRdCVaEdqinw`@wPq*K}s5sd%U1oAky7Y8|QbuWE zbsYuu|4V6cuxhZQV&JY)kExx0 zQH@DvvQ5NXz8=S@IY`t(|Q~2Wy)Ba<=>B zFnV89NHI+_G%++YN;NexG%-&yN-{7?vM@4ANik2dG&4<0-M-V7X(AJ0WmyhPCX9rY R?Q>*0&Q4g_QctF9%m7=nmVx2*(34G?-vaom~NggdaMZ@XoRW<7RA59V9%Yk6$x7- z*R@Jt@~C(H9qGx;StW{?-P3$|OwX>3L&Wr+k?Sf3uC;ePj$Y5T_G5g{G7*y*u_0fA zDeS%3GrIGaxfvy%@;eucN?tj+Y5&PhlK5K`Ke=g;{)=vrCKuJJyaXqt7gD z&6JTGGC7A#$swaTWNNysJJv;V0Q<5G#aNe|L(i9G=_xt%d|8$rP3R3v^O$d6jjAHP z^ZfK`)RYw)J)L(*o8^#Y>G>41^n6*Co=-7DPsGL-W-0o-GZbT8zAQ^mP576k=gYG6 zv>bZAtUF7Q|!Yq@-FPv;mUzn|#D=#D? zG3{YzkcU$v(w{3eBv)<%VK#hrc6i%Ms39o6o@Q$_Klc)<3u{qoMRUX9f>ty?9G+-J zP2uo|x{%PMdW(a~LeQNKS{4qcR|BgG7w|7t zqv)rd?Md1m`zE2s*TMU&OYW$jKrmd|BI$#)q~aXlPoRTW4Pr zZV8=az>YFt(fMs)QK|!rHXTh%xlH*3HvwV@kX0MevmwYm9nDAsAia8&rR(1mXs0?L3l}@U#uJ_C2~mTD2>oP2PIf|5t*HeP zZ`Fc<8?`{<$|f{B<*CNct{0y6O-0XzUR;_AvqsgW2Sp2l(ou&dhpN4GkffbkfWhT0 zz+m`Ru+_X37@Xb;b=o!^%?ypzZG&X&-G*kQD6#+-&k*7HY#StFXMLuU-^9MsjxIDM zGjj;6xC{@{T=X!oPQ;Xh^1_8_mkl63r88 z9qxvBMdzVe;e;=`(ZX=};XG6u4nLoV>NCuU&?Le30K2~V!0zt+l;?XP9=l&a^{KoO ze&0n;^S-@;^3Hug=7}aC^S6CK=A9-WbFV4WN1#S}c`d z#Tc;!y8Pn<0DN=_0AE=G7S6l|z(WoK@P(xS+;ArBe2phX_ zxxl<|xj0j}T)+XNaJfi)1;Y!Mi^AoiaJleXp>Vkfz90X8T`uC{a&e((sNu?vq1z7O z%ill^X&-<52D&s1p5i)g{=BG^zuAM9W=KcQ|9Vz@=WVp%>8PnG zmaRB4b4`O%Th}c@nQ3~K=IXMCEj}1U_T|-zzl|bePesKWNnO=3Q8X%EK2l;jGBzxt zh&oZIZE5ib=OPFE(y8m{hO5b{tk^DPs=;)R3$-WX*M1*4B$Zp9jZMdP zEy6TPG}ACl%Cy8>jbU5oCoe`u$EOWMu1a~)`NtEA7YKuo1m}2CG3Fafi%X)4%m{HT z$5Kq0GF>sSsjH^u7^>l5X5he^a;k~vj3u7wlu*M|RNcdlNfoA;9_IJYMMm?E-$c;+ zpF~bc87crSd%Z{L^QPaWoIS%E_eSny9YX zB48@%a zLf37n-n?ewu3eSUiQ*MEv#5%(t*TUUnPuCe?o?OxL=oCliFe+L#87#@QuoVlAuT>6 zb?3>_L;>2ksHL@O;jD%iTJzMXlc}9Aaue57MtRYDkulMHZgDcY`1z>js)-5|iSukq zJk78fRSEG7#Z^2a(;&5?Om$aP6fCQv!ChS!HNafY(M(J{+aqZ`7j=XI5kkfcim9oI z#>JvHEzeSg#7OD}O_vWnXgdAT=v=xQ5j9FI&0>P2D+rjXVOW-{8PpW3l54p{3HmK+ zxPma&3`h1l%VXhc zBRVcosd*>_m714<-*SfPSSnT(S!N2=RnhCF!wl6muq;z*nxQ)?rHM7JnEg-_3d=vtA@-j#@ zo~&1CN?PNI-B?-Te5IPHoiBEx^|W1_||5LDUg)!AI2wvb$%`IlE~R5+FcCAmNRu$=UOeSGh?5gJU3| zwW4BuT&FwEsDw_Z)rvJVQ}5J{sGU0fN2pH!sM8q%XZr8~6sFTZ>U6Yw&pG+-!>iZ& zPy5GZ24>H9e*67?-}mizc9;9n!7D#Hc;)DCCbjfg?sn%+v!{`IGQh6y8a6kCFR^F3 zPQVOIWI!B2edZHGN=6&Kw7b1=dwS=9>u35W%o>|nm+Vw;jG=mkKbUY~X2r^Xz|%x^ z^o_)%u^p>^l*rkb8r?Es;#l{Zx#45W*s-F6v~<# zcX+LJsqjy;9vYT9G=Q$j^bUP9v)H><^}kYc+1Rj`PN?HA*GwF1b6%_Y>a%*#MU+Lp z^}mK?WKM6KS;NWL#hZE(U-0VS=F^GGif7dsdUmG!_|(kqZ5Qb2nTt2g96Paf@T=f5 zC$>JW_O8|$nwP%d3plmEr!rIee^p*l?yWlgR!!Y~hXx)vG?37LYqEz1Ch%VjQ|?i> zXCOU@d))L^+>p9vRZp>vl(Eq=HlvJizh8OCNW!q(JCAd&x2`;pi;6jN-IGvl&SON@n7Xj0Q zp{)dG=He^Cg|-r$RR$N@hD(5U8DMBD!SOP<&{l#IWpJS_#tn}zO%krnl29UJiAxeL zoKj?HEJc>93n?nh*0?js=S!o@U#>KyTx}v_DSc^mRPPX^+IX1$Hd+`DyBxG49$wmj2IJu)4FK3C zQA=zc%@1#z4c2G#!&7=#m;YL*_Lb;zH9Q9;)w?d5UifBoFnUv8Xe4YLzTQJKV$vZG zt&WFnji73#5B$d(QCr>%QIB_`X=)Be*Tl0<`{RjJBN)w#t-Awo@LGVHV(X^?ni~%f z6X1RNPT=jPg%MJfSeKJ*=mu-Emd1=H>`@z3^nVvrOy{7Ym!rA)keNE~>yW#ICb@MRx-KR;%h9|7 z60#;oA`p#VPfr8iZD|NJ?n@~&M)eqNcPba^l*`DizD!kPPJ}&7mEQlEu zMB~Q`a;P7g>h@+3@pLmdIMoay-X1^;^O0KW$YvesNDI0lj^bDg%(}F-Fez4)l)hGU zQw%-W3Pl>)0Sb=q00kHB1Yg}dLBR_`|d*X@)GTc7thn_`QR=n z#!!1PC~V>oboU^d9Xowt5H%Hg3R^b>)$M{r&)Egi_U{5|E%VX*{8S#b(8zq?@4Xvh zyJ!Kd+p~8=N)9f7l$>7xDZw3uIpyjI$)$h3j_syg$Y0;B$tUdr@)sAwcl)tDsADpGEC|jeVVdgOt0(+> z4@$*iW)7nTMJU}oG3vlD36LOF_)prNv%G$+(x(_kotj zmVuV{mVuU6zXxU7x*TYh+z-8<=mP7h`+@w~E+9YIh1%n!{QCQ_8}92yow4=z-N5$J z0}y%d3YgZsA1x}#r4fBGE?Nm){?2|Nesm=ezr7MX9DWdpFL(%uUta~pBM$*__iC6n z`{5#D77>588i*U#0P)^6K>WrU@bJt5ApX|@AU?7dh#!9h^4qo!rd|DmBI5WPd3GJT zG0xf5>w&m$JrEyR4;~(Q6s=I5x1#Gy4$uWhZMbk$FBj3ptMqtS=4f2*6kF~PTXcqv zkFcLV!B#I9gB>!)yu`<>g56s7}d)~?!RDE^>R_YTvRU?VJlQG7t#NZ z|F13=S$(;9y=Icbs4yvZOtt0-)KT#G<0sIu$?z7pD(B}l)74WMw5rH@>ECMV)wCzk zaKY4rPa>Ls%>L;~bYSsB)?|_<6KD5*i1sJcKTn{U)3MlIS4S~rv$2>>trUqdsT&Bk zRqy+#UF|rD>c7_1kH=>zO}ILH)n8DMs7%lxHDsm_hV81p!%`axK(trwqXXU`o?rcu2q+(75dcPZsMg_c$(9V`Qb8q%Xo z9eOJ{H9PW3@@I*ZppL5p2&74DW=Y{t+ZNiRDJ@faJdURC%^bD@IacpqSBp~>$feTJ zG^GsU=sbG1@QA^D*B6*z3;Tg51(U=wqr%i1*bFS+Fnr(TjA}{3Fo^9tZr}@PNu}eR zqO>~n@_WgjCQ`N~0te&3v>eWeEd}+cqcdQ;jEKOZQEj4VDk+GfsUQI7qQUBw-z9It zhB*h@n|3sAzkSy3-3_T()OVQg8`|!>j9Ge7>9r(0y|%dJa_;9s)Wxc_+{2}}oYg** z7_L;TS(>%8Ykkks8Z3(i_w)*H|zEWmH1`y@>Hd;6q!Ii=t}tBJj_RATZObsQ#U=FvCJb8!RCm5Xg-tnjNWA=_#xny<0^wSwm0Q)? zli>(LIn@pbCN7VP8#P`f`mpipE3m7?yUC^$wX_mj5+(wV>HS4Y)3E%2dVzEt+ojp= z3u|>5g=LWdOWWZdW46m(W_y9J4Lq=jAB5tES@OBW3@b2nr|HCCpAio`h9eCw z1IP2QYm%@<)wyeHzmeVjX7ZUtMHS&{t#`g$g?j&!Bv!rG*G|^`C%I|*nCNc^Gi{+i zj|TI6?3+aD^jc7#K!0Ui4c7hVNapzZvm&@ox#8=lsG$lPkU9F(gLU6YYBSDF$1<^P zSphbsZBtRwfL|^lYCzkrpkTZKZzPtm=3uj+10~0%UQeE+dMoj{DcyiO4#CvX@py(C zm(Jx5v0SM;6f;5euE3>>v7~|iG|@o+LX!LVM;&N>dD&!fPgPp6#pp`8gAS||mg+vA xJX~?_i7Q;)`l?%B>E>JA`if2s(0J9Yue$YBx4!Zxmj9ovZ$tJ&t2S}ve*otPp+x`y diff --git a/master/.doctrees/tutorials/dataset_health.doctree b/master/.doctrees/tutorials/dataset_health.doctree index c81e7bbb3179d1e08e94edd5252ba58f042c0967..548e9e4215d74b20a4df87876c3de6124bb688fc 100644 GIT binary patch delta 236 zcmdnlFS4^=WWyd#L&Nfv)YLQ!1N}5pL-V90GmF$DljKxG%ha?ab5nEEWHXC26H`kQ zbHk)0)8xeDM1$tHob7Kp8G)Dyh?%#)h^CxSSB(NR<`^%iwPrPWxxNh>}Mye>=p~_eI@|#(@Ww2 delta 236 zcmdnlFS4^=WWyd#!veG9;>2{*czvUkq%?!%)MR6G3uDXFB!d*wDaF7z(bU8=F~uU;#MH#d rG$qB{(lR+U(PI0zA1o7@2rFCuo5h5Yu(IEOSoX6MR(6Yp^*$2-OXWJ=E%vcYe&7nS+Im7J~6dDH_802{sZjD2gFqEJ7j@ z2;r*mD#L3eVu-k;@QL^z#6rt}AU44&kjCI-?<()1?5%}mi!FBce)G+I-#7Qh<-<2F zAO3phkTLI;S{v)F<_)EUTodGKLT!mW`HbAa2OyJ@&oM`R>F$H;*Y6&B=d-S$3Cv!j zwE-x%X2RovOz)0Cu2PslCXidzuxJ0FfJb%<^k-l=| z?CqV)U(P9UvE^q4baM)Gw3q4(`3Dj&*(ve0^JS z=Llno-e;2in9!0IdSEHk>>*@7;NH3;Y}QHNRfapS~&Jo$lu4W@0#$VOLv#-vn zFDD(#Uqh{~KyFYpMfoCaa|%9C9A@lgtVWJ_Axs=%t@2issx@@8eK2>gL08>qj$R*|Az%`x1Ot!c?A9bjP$W%tq z5fck3A5%lwODOIBc>X4`9G6T#Co%>+5ZS${HXVC5;V7CnPHnw?eB<=#&2Gcl>ZH4d zu>!Q}LT=PD3cW(DxxMRpe5s52i?`H~+3wHk{N&8owl)O#`IlckaqOjMpL^j%9#QK4 zYH`cb0d5@M?5};V9$8z?te9czDB2D0h?*}%leH972VnUP-BNtc61=132$zfY) zRs_wpQYDTF(6*GV)@;P7QLGMeuV&pC{9LnR!2g)<>HdQqwcbfK2#&PJM&{R~Ir$VF z5F;697h|7?%G5WZFL^KV2{v;QI?+j}f)k4czu@HMA?Re8wPnbWqjo`8xeTaNj&+|8 zN73(dnwI6jeYdT2|K=UFX{~k+k2>CC!z>w?d2ac_5zo$l&iu(v{{`YUyx$*n6x`3+-`0vZRx`6-r3bDF? Us|&cgfd8ipNV9`~s%sDY1q^Xo;{X5v delta 3887 zcmeH~&ud&&6vug&ELun_VxnDTUV^R+&HZuiJ@+EwMi+vyx|CYN{VA?gDuU1wG%jKb zVkqTlAuG!)grcS5qYKlD|3I2eU3Mki6jz0AES@(LV$;REwJ?iW40GO`bH3+%&UfbK z<=LB;XMY|Z6(5^;)_-bwVDE5LOxMfoBzLl;ESr7$F5| z=&LpceKGv%{KkW8?SpI8l|Slei?Kyzg=j%jbHP}8qd;dbrFYG{>+HF<{))XnGeEDo zMlTE?C}(|f#zn1lrkB@g9Qyb>cCo4vQw*d-h(3XlJcOJ~^nei}P^shCzkg$YR{bSB z{+zAWu`m1LHnTMkPWY&N$lQa<5CCZ^DtKiTSQH2CoSqqosI%iGSevONkd`BdgxMP> zIF{yRTt@Ti z9+P!X`q)rIE6v3X76w`y=dIy9L<6bhP&6W>6h$x^R9ER-H>1-tHBHlDf=(<4K+EAE z)3cs?u5DDJrFrgDgG{?3zV7MGcC&u*rI8FJ6ERv^ zM~e*Jmmuk)5M#!sj3NF9Bn_KBvQl7D+-Eum(Pj-u>*GHi4i)}k1$a*7=>;OG|WOrmOOxGaf)8w)=oa==BDK^O-lGe~Cw z^+#Tj3?`#uY5Xob(d^!5$J^VR>`FD@2s%i@Q4!)H*ic9oOX891&{gif`i1t}3v5{R zmocKUuYhNVOf(-oWG^dYq%kK2e}YN`GAX8%tdFqi delta 84 zcmca{N9@KOu?-hE4GYYYixbmLSQ%sFb43bQfERsx(lFST~ vfDFsTM1$>GLCj4|N`#cvC``8xVqQAEIFz}Uu$c;B%%9l_D>IH`e#Zj delta 72 zcmeyif#cf-jtzS_4GYYYixbmL4GWYHDU;V3=fRusQQr6O$4lRW%Be>wYbryywYbryy3LI3~& delta 72 zcmbPwoMY;7jtvJm4GYYYixbmLk diff --git a/master/.doctrees/tutorials/outliers.doctree b/master/.doctrees/tutorials/outliers.doctree index 97ff7c611d063d879b49c0c6cb44a6490a745169..e904db46290a563ed556c58316b02dd94219ac5f 100644 GIT binary patch delta 2406 zcmeHI&x?&w7zOQ z>5D{Y7Nj(d>F$*U7M8M6{s9{c**SA(6m|8j?B?5^=Y7xfyzldT=llDWKi{wHxVBCFc8LY=?%3Wln+KQ!7P`7GDQU#!>qO%&wlMZauki!g)aBN_R+Cj z81KZmm;mO2q=MmIE>Dh?s+Bc;rL*UKe`91-r7^Ou)~eX_uZsF|a|u1jX11VWeb5eU zgub?I>tCjmKVTc$!s-w0Wi(vht^2pOJ#Vr5O{;}#-)Hund)Ai#8!91-OcTpecXrMCMkr#F$E*jKn+c!S{s>^DS)lql&tl#VUG(ayevO7trB+3X_M>Kq548P8%n*CRA}|Fc^Y~*3sBF zaJ#5S$K8RTA*3|WR12w9sz4e6DWt*L1&$}KqK@pq71Y&yd;{G_3D($JtBJx+Ql(X_ zQ=S^8h*wOp#VN$97burVwqpX}B$r1NRmz@EpsfkEga`$W8Oq}2+DI9UGXNkM1FII5 z8LmMx3|wMM1#v3MmM+k^j-Uc&?UgBGi2;Erahe)!m0*tY=sW=qgV3$2=7O?3A`8lL zd9;+hzKMEz7!2)+zl_58crhtBJ#QN;v2t-`S{W=D1PPV_bT1Ba~#5i+irU_Pwz6v6y zYBw%yvG`IhE)+iygcd&ta+iYOrY>|NT}uB1apgjAcrRI~(Ytb&yS)5n=J#=aGrwQi z``eYhy*IDFIQ4b!^0Zj1dFW5-8Fmt z$J@vHh+wt&Q}_x zL>rRlz?6ct#$=5~TIPVnIC}5nvz?$#`sC`;?717#3u`NWxH{YzZCrf%v+Ks+H+RE_ zih_mKIJ>7C_SPdn&Ft7?+gX&o_|nvAo7&_zrs7S6Ib&=lBh?XxezJ5Xuuunsb5W<$En z4EkC+&Nlh%V)kQf^T!Zo+qt9g9dvSNW}bj2yD9woJlxZK`8M3tw6DOviio^bB#bti zij&rJ)MS+vQE@hQOBvTHXHT|PhN=8_MWPZUBbRv(JYm~jcn&VXT`Go{oJOT=A}gG; zs3aPvWKF^5W?zE+O@a2Bg=b*8T{{jRKqrp|&j41PG`7{<+_1lT2EdVm%J*uIbR#+Tr9DB ziJ{)g?eFrsh)m>l^4OMV?|0_#L*?4&udffPenu@_BvPdsWjxE3L`oT9gHVc9^Z4s< zd;8NVIMM5@-(>2}pNCu8Z&zU*-slLk?w8{qJ9>Qi@R7xb4=r~M^?kvbw*t;d&E$L*;cT2kC%kw{$^hA5)UHD)rCPtgH8MpfZ0I91KXaE2J diff --git a/master/.doctrees/tutorials/regression.doctree b/master/.doctrees/tutorials/regression.doctree index 5359952a464446b35bd07bd109e686327670e42c..7f483ee29ad3bb0fb496aee13c4c62c1b2164611 100644 GIT binary patch delta 224 zcmajVyA8rX3;NP!gWzy_Ry?~`%tJ6mi(4?|ERM27?gQbeCIDAHsPMz~kMX>O;v zeSbQ{Q&F|#_U18)grq`QaZr=!qt783`GF#e_{^EzW$DJHgUxlWFXa9-8h>kb6<);!?5uD9P4j?EwL3rEEO delta 224 zcmX@|fbGZwwhdc24GYYYixbmL#UR-%Db2zl*~HA)A~7W~#mvM!Ez!~-)gslv iay#!r#wI4h%GNJtG+`vHjCm>JPIkh|?yO>fNl%>g`_`K_lZdv{_v73z1Br-P1d=hyA|gqcx#xb6fS3#> zhAaj@vJl-x#MhO$Q9%&1vP2NaKOkfyvzfgu3I<$@PjyudRh=;R2Uv^K)l}bp&pjW{ zd7e{sbmOg~8*jb)htpENnM^ z6WB*!ABF9}J_h?Z>^9gb*!;{VV7J5WfZYkZ3wAf`ldyYWpMu>BI|I89whKE8yC3#x z*g4n(v!l~{k9~PCLUE|obBvi{HEeRuHFJs~`p98?_NRN_Z=ZVS`MvvQ|9xujy;B3T z(@rKQWAx;zV#+Q=AzVr#v()n^zCCRmn&E-JczlRnc`agK9Sl_p98F2mt4b8S6|}fr z^VIR}PK|pH{$yZWLC7SXmXb)Sm^5zYQ(Kmxzdz{y_28g{bvc!Y`g#&p z$1JOjMi@%EI3H$zd9C~A&QNo-Ts2axHH9p^ib|8L!g_>+W0RkCKc5V-YFi2!V+cP( z@QI6XI&miQRf`c%yLUFc*G^{J^$K(%$GOJXIEKQ%cH+T7p)% z(khA~Y9hv8QXvu^jibp$R^DlqBaiR=usb_@_iO#HCsP?jaWRk$oTw-Sj^79wK;v8T z0tcNkGtKW z72LdIAA0CG577z9=-Cj1C3GbdIZ~#e(5#D^y??!Xb!YGv(!Jz%6U*~r#PKDekO zQH@LNIm{@^3fOaKZVVV;z$l@N;%K$2>)2~JP7Ca{951kEB?bZj6*7A;g!55}kiC?; za>|95eQI+N79m=ZG9^rHsYI2zcwvgrDXjM?%aYgvp#`LahelA3%{od?HhV9YecJFm z{`qC}Y18itc9*9@Dw%@U#(P7Vs;>pp&Siz`RuT)y4;Q-^b_PX;WfZwqX9!c>YmF3x zDMawH)Up=Mw33uL0+*s#a;|W~q+*`}-n7P#S1$JV&n|zr`_%-!bITi;#UyDuUqNqo z{QGWy?hDebVUHcgcobBSdyEwyFrt&mpeQf1)?!(%CJUa#UaMxCXlga<=IKTT)lKZR zf?LC$6&2)eq}Coa#+U~~!5mWHiU4Em^snxK1)M0X*_Zj83?}4KGe{4V8z`e`|B4B8 z`0Z|YcK2ucHzw1fL8ed*=m$$2WMmtng>Vot1Fkc$#y=-R!?zoM_i+DQ)3^L(C&p(U z>F=7d#5@N^Kmm@tL?x>R%0#dp1mfl$c4oF{+!AWWoRw58lYt_UD#R+XEnpB+#aiPI z5?tveXHB9K_yytPdB&*f(DBR*7!D2ZYSFY!$MdHB+`bgz{DLIN89>RKTyy|MfgP%K z9@9|mMx*n7g;QNG{7|?ihs4IjC#!9Y^bo8sB z8s3*kY&u>c@wV|xp+EP8eE1u$KPto*g*bTe4FydpjmOIcNoxqzDk$7OgXmErl`$kT z21bail^00+LXx#r<4JaGlyehvjZQwnrLC;qL})9xV}zbxsDuPAf!d5!5kvu)585&) zQ^`T@>ZLTD#$a&3fg05OML_vft|bEAq-+>Lq8kt;D(pi{mN3+f1aAev84W`Y;<_xs zax76r;|2?wY+N8MXc9WkWtpKiQB{XcKt_dLRcLrqiuAK90t2Trs4@YV0=yC&WBkM- zjmy?>60_GX_Sc}YnW12nE>zL(g@aJanE>h{c112_+|crPjLWIIrAMd+h`sT*&-IT@ z1tV3a7zh5q4Qqtjz>;L6ck@PGpE4onFjtgNnGnxhytgHTB(Vdj8mnCgB+^hrLD-cs zQj-cn0J28dswyh8il*UxIc1uTSAaY_{^N!Ioz=Be%NMxlmE8c;dVRRZigS(=r`GHyB*$QGu>{7^N$c4``LCoY<=@^Qk}Mz{Y`{! Yw?lkbx7`l6+u{G+b~wX!_{ZD-2Y1uM1^@s6 delta 7624 zcmeHL&97Zm70=bDv;qRszSj1ga}Q68COrH0_lG3Z7#xs56$Uj1NW0hm5E3ILfQCVX z9|Hrk8slN4jx;eQGU6B#r2GR0CZvWSj~QUSyTIBvRt1dq;n^ zcXS5(1niTr4cMn(pN8EAI|3Wed<-wSu+PHog53?f2lhGGy|81jP=o+i5@E$e5f>|M)Mreo{Ym^}^Qi;a`t#y?>-D zLYa_DG?rUcjCT0wJyVvKG}N=DCfoM8#cVib_%{jUgrBgN=nkK_}>8_ImTHS;u5fNo2>YO^WN_ zOi)4zCxrFdM&7@Bxw&`fe%M@}brr|4pFh$*R(0IJf1z>nU}E-$k_x=2;3RQwREQ>0 zmWhe}!&!T5=*~A!Zp@`C9!)HHBBz*7WF`Kb&slkA;_&?W_MO>W<5flKXc=*pJbA5^ z$R6RwqI2$X>f~_gN6l|%Rp0MC)9|k1Rg7Wm_OIR9KG^SEY95$}keu;kkj135XC5{2 zx#Z-F4uOVuH`?bm=2mc?oCutc4eC7w&7H_a6`Q#=Q!HIl%mEX~kunN!4iP~(ElopK@8`{u*nc=Tee zM^{hRlDGZzPXEuJHz($m*v0~X@yvc2ls8H_@~K3tT(ZP0>KZI#G4eomkp}QuA6G)rq0<~V#72{OlzFPsyrO13*yh;q z-s$$W*(=LRfM9e~yAaRfZ9iRpt z4}lz|A{sC;OT~d9(a-wL*GH&9y2p-m0Zp!)F!%;3K$3ybLJ0#@=I^tv=CS|d{p}O; zio?G9Qu|PA0ANfG(Rs3pJZLf&2HFlD7xn$kS$lj^wJzk8gDW5lTpcrpmoh{~%tVzrFjYg@MVB5zNB5^T&G0+&Fjta;ihSSKbs%!-}d{`R&_lh?SXC1*qKQN zpjnm#G?ix$PLNIzh%FG2PC;t3Xi5>o_%CWVmPB%w1>_x(M-2({3!&!Z5)xuj0*P+( zcnfI^DdbSR!lu^UkEW~no$yw1`?0{?)_={nrydsffA7Ty8T%?@J5RkN7_%H5XWUuN zmT|}&^uaSIIp#pY9JgRV9>A1=MVct4J}rVQSA_5IN;ejAVR${%@_1nFONXSQb(miODYXui`vR(Nz3? ztbZr^zdhOB!vs4k`4aT7xJUy=fFgsh$MFe@t-vZJVEjx6LuY+sx&?c+imjrl)}yPZ zYY9BSURMilUkLl%*xLTb*PF-J%iem~TQ7UdKS-?NZoTZ?x@1mDS6jIzrR%CY*LvCe yZ%gKS*}HW~{z&82%iem~TQ7U~<>k<|eZB0hm%aboF4xQ6|J$-RVA*@?_J0HJvAe$j diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree index c1cf22ab62fe0ae27246f661629023256102d902..9fa20654c0d01bb98902cd11631874e1f6eeccfb 100644 GIT binary patch delta 1649 zcmZpE!Zq~?7fS={RK|@gM>q`)%TrQQ(<}`1(@YJ`lakCVQj<)QQw=Rs(~`_h%}tZd zEYeI&Eltb~lafr66O$7SHvi=OZ7OHV%D`Y#P?T!NrK@XXU}U0eV5w_lreJ7hWol$) zGC9CCMatArN5RO%zyK(cYG7rU2xM6q8X1^d8BOkYux2tfocz(*d-B|voXP84*vu_W zE%hu-ycHBo^-K(Xo%D+pfFLtZ!9dTbn2QV33KInlkjlvi9RIUHEtQL0q+x2LXJlxu zXJM#kW}u^BtY@lcWT|IpW~l>onx3JBk)EM}sh**!@#KEz2p(j`K*yZX1In8cXE#XY zWXEXp$q8PZsu}FDQ!)f2M#BUiCMAqL(;JwX^e4{@;Dbd^fNR*~Ja1tl(gnYNC@@`w zPnHN$0!ASD$-!;1oL>_0Ny0WH7Bxxm`(Gn9NvsIJkgBLD-Pl_>xK delta 1630 zcmbRGgsb@p7fS={RKATYM>q`&%#w=}(@o>`jZ%`*43blmjm<5LEmM;WQcRN*(@cyF z43p9f6D^HWO;Qbwlafq~Hvi=OZ7OHP%D`Y#P?T!NrK@XXU}U0eV5w_lreJ7dWom9^ zI9bj+Z1ROLK1mZ}9R(v30|P5lqf`Sc!$cs<%F+bLntajKn#sg?GN0QqJzZTrU2iA- zVg(?`%u_JXGc4xf!Zg%aLBm|n%zW}e$Ny|lYwr8HO`aDQqhYLPs%K=bXJ}}tqhM;J zXJlxuXJlZmXK4=S8k!sGSpaSIh~Ppf2HNgDxxup$s8P?z7vTa!JyWbMFjde1shk`T zZ9ZAmRbVs_(!5HTI$0+he9@cC<;@3+6*=!r!V$kABy@5^c=+TlA0>(szzX6cf4yHU za^!CaxkhTl%LIgtF#q~b&*Nm`oXiszCLssR@|0%w?UNVy@K6-r)X4BfldD2`NR9h| nX#3G1u-LpcA$=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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 43216f98a..ddf8864b8 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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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 672c4326e..1365d5ce5 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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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 38dc81ec0..5e0d20ce3 100644 --- a/master/searchindex.js +++ b/master/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/adapter/imagelab", "cleanlab/datalab/internal/adapter/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/multilabel/index", "cleanlab/datalab/internal/issue_manager/multilabel/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/model_outputs", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/experimental/span_classification", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/datalab/workflows", "tutorials/dataset_health", "tutorials/faq", "tutorials/improving_ml_performance", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/guide/table.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/adapter/imagelab.rst", "cleanlab/datalab/internal/adapter/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/multilabel/index.rst", "cleanlab/datalab/internal/issue_manager/multilabel/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/model_outputs.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/experimental/span_classification.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/neighbor/index.rst", "cleanlab/internal/neighbor/knn_graph.rst", "cleanlab/internal/neighbor/metric.rst", "cleanlab/internal/neighbor/search.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/clean_learning/index.rst", "tutorials/clean_learning/tabular.ipynb", "tutorials/clean_learning/text.ipynb", "tutorials/datalab/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/improving_ml_performance.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "imagelab", "adapter", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "Improving ML Performance via Data Curation with Train vs Test Splits", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 86, 91, 92, 101, 103, 104], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 91, 92, 101, 103, 104], "generate_noise_matrix_from_trac": [0, 1, 91, 92, 101, 103, 104], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 19, 43, 48, 50, 51, 52, 53, 57, 58, 59, 70, 93, 97, 98, 110], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 23, 24, 25, 26, 27, 29, 32, 33, 35, 37, 39, 40, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 85, 86, 91, 98, 107], "benchmark": [1, 40, 85, 86, 91, 92, 101, 103, 104], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 100, 102, 107], "": [1, 2, 3, 4, 10, 21, 35, 39, 40, 44, 48, 51, 54, 56, 57, 59, 63, 64, 68, 70, 71, 72, 73, 75, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "core": [1, 43, 46, 77, 79], "algorithm": [1, 2, 8, 10, 34, 41, 45, 56, 57, 59, 63, 72, 81, 83, 85, 88, 89, 92, 95, 96, 97, 98, 99, 101, 103, 104, 106, 108, 110], "These": [1, 2, 3, 4, 5, 8, 10, 24, 40, 42, 44, 45, 46, 47, 54, 61, 63, 64, 67, 71, 72, 76, 80, 81, 83, 84, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "introduc": [1, 10, 90, 97, 99, 100, 101], "synthet": [1, 103, 104, 109], "nois": [1, 2, 3, 39, 46, 49, 59, 64, 91, 92, 97, 98, 103, 108], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 17, 18, 19, 23, 24, 25, 27, 32, 34, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 91, 97, 100, 102, 106, 107], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 19, 35, 37, 39, 43, 45, 46, 49, 51, 52, 59, 63, 64, 65, 66, 67, 72, 73, 81, 82, 83, 84, 85, 86, 87, 90, 91, 92, 97, 100, 102, 103, 106, 107, 108, 109], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 28, 29, 30, 31, 33, 34, 42, 43, 44, 45, 46, 49, 51, 55, 59, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 91, 95, 100, 102, 103, 107], "specif": [1, 3, 5, 9, 13, 17, 18, 19, 30, 36, 37, 42, 54, 55, 56, 61, 65, 68, 71, 80, 84, 93, 95, 96, 97, 100, 101, 105, 110], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "modul": [1, 3, 10, 13, 14, 16, 17, 18, 19, 24, 27, 32, 35, 36, 37, 39, 40, 41, 42, 43, 44, 46, 51, 53, 54, 56, 57, 59, 61, 63, 68, 71, 72, 73, 85, 93, 99, 104], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 17, 19, 21, 26, 33, 37, 39, 40, 41, 43, 44, 46, 49, 53, 54, 56, 57, 59, 62, 63, 64, 65, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 103, 106, 107, 108, 109, 110], "gener": [1, 2, 3, 7, 10, 21, 26, 28, 36, 39, 51, 54, 56, 59, 60, 72, 73, 75, 80, 89, 90, 91, 92, 93, 96, 98, 99, 100, 101, 103, 104, 106, 107, 109, 110], "valid": [1, 2, 3, 5, 10, 15, 35, 37, 39, 46, 47, 49, 50, 51, 54, 56, 57, 59, 63, 65, 68, 71, 73, 75, 76, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "matric": [1, 3, 49, 99], "which": [1, 2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 21, 25, 29, 35, 36, 37, 39, 40, 44, 45, 46, 49, 51, 55, 56, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "learn": [1, 2, 3, 4, 5, 9, 10, 17, 19, 25, 33, 36, 41, 42, 43, 44, 46, 48, 50, 55, 56, 59, 61, 63, 65, 72, 74, 76, 79, 83, 85, 88, 89, 90, 91, 93, 95, 96, 97, 98, 100, 103, 104, 108], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 103, 104, 105, 106, 108, 109, 110], "possibl": [1, 2, 3, 7, 10, 39, 40, 44, 46, 48, 49, 51, 65, 66, 67, 68, 70, 71, 72, 73, 75, 81, 83, 84, 92, 97, 99, 100, 101, 103, 104, 105, 108, 109, 110], "noisi": [1, 2, 3, 10, 34, 39, 41, 44, 46, 49, 59, 64, 65, 67, 73, 75, 76, 77, 79, 80, 86, 91, 92, 95, 96, 97, 99, 102, 103], "given": [1, 2, 3, 5, 10, 17, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "matrix": [1, 2, 3, 5, 10, 13, 19, 21, 34, 39, 46, 48, 49, 52, 54, 59, 60, 65, 68, 70, 71, 72, 73, 95, 97, 105, 106], "trace": [1, 91, 92, 101, 103, 104], "valu": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 21, 25, 29, 30, 35, 37, 39, 40, 41, 43, 44, 46, 48, 49, 51, 54, 55, 56, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 84, 89, 90, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "more": [1, 2, 3, 4, 5, 7, 9, 10, 13, 16, 17, 19, 21, 29, 39, 40, 43, 44, 45, 48, 51, 54, 55, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 109, 110], "function": [1, 2, 3, 4, 5, 7, 10, 13, 16, 17, 19, 26, 29, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 97, 98, 99, 100, 101, 103, 104, 105, 109, 110], "noise_matrix": [1, 2, 3, 10, 49, 59, 91, 92, 101, 103, 104], "py": [1, 3, 36, 40, 41, 46, 49, 51, 91, 92, 101, 103, 104], "verbos": [1, 2, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 43, 46, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 91, 97, 101, 103], "fals": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 50, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 105, 106, 108, 109], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "prior": [1, 2, 3, 39, 46, 49, 51], "repres": [1, 2, 3, 7, 10, 13, 15, 19, 21, 29, 35, 37, 39, 43, 46, 49, 52, 54, 55, 57, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 110], "p": [1, 2, 3, 5, 10, 39, 46, 48, 49, 57, 59, 63, 71, 72, 73, 77, 95, 96, 97, 100, 101, 103, 110], "true_label": [1, 2, 3, 39, 49, 59, 101, 103], "k": [1, 2, 3, 4, 5, 8, 10, 13, 15, 19, 21, 22, 26, 29, 31, 34, 39, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 66, 67, 68, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 88, 90, 91, 92, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "check": [1, 2, 5, 6, 9, 10, 13, 15, 19, 30, 37, 40, 43, 44, 50, 60, 62, 68, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 104, 108], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 15, 16, 25, 29, 41, 44, 49, 51, 57, 70, 75, 89, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108], "achiev": [1, 2, 40, 41, 44, 75, 99, 100, 103, 110], "better": [1, 5, 10, 46, 55, 63, 65, 73, 75, 76, 85, 89, 90, 92, 95, 96, 97, 99, 101, 104, 105, 106, 107, 110], "than": [1, 2, 3, 4, 7, 9, 10, 29, 31, 34, 39, 46, 55, 59, 62, 63, 68, 70, 72, 73, 75, 79, 83, 88, 90, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "random": [1, 2, 3, 7, 10, 21, 34, 43, 51, 54, 63, 73, 75, 88, 90, 91, 92, 93, 95, 97, 99, 100, 101, 103, 104, 106], "perform": [1, 2, 4, 7, 10, 29, 31, 34, 40, 44, 51, 53, 54, 55, 71, 75, 85, 88, 89, 91, 99, 101, 102, 103, 104, 107, 108], "averag": [1, 3, 5, 10, 25, 31, 39, 40, 44, 51, 57, 63, 64, 71, 72, 73, 99, 103, 106], "amount": [1, 3, 93], "paramet": [1, 2, 3, 4, 5, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 93, 96, 97, 100], "np": [1, 2, 3, 4, 5, 7, 13, 19, 21, 34, 39, 41, 43, 45, 46, 48, 49, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "ndarrai": [1, 2, 3, 4, 5, 13, 19, 26, 28, 29, 33, 34, 35, 39, 41, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 97, 110], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 56, 57, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 85, 88, 89, 91, 92, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 15, 19, 21, 29, 35, 39, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "shape": [1, 2, 3, 4, 5, 13, 19, 21, 39, 41, 43, 45, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 90, 97, 98, 99, 101, 104, 105, 106, 109, 110], "condit": [1, 2, 3, 10, 49, 55, 58, 59, 73, 93, 101, 110], "probabl": [1, 2, 3, 5, 8, 10, 13, 19, 26, 28, 31, 34, 35, 39, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 86, 98, 99, 101, 102, 104, 105, 106, 109, 110], "k_": [1, 2, 3, 49, 59], "k_y": [1, 2, 3, 49, 59], "contain": [1, 2, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 46, 48, 49, 53, 54, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109], "fraction": [1, 2, 3, 10, 23, 41, 49, 59, 63, 75, 95, 99, 100], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 100, 103, 104, 105, 107, 108, 109, 110], "everi": [1, 2, 3, 4, 5, 10, 13, 19, 40, 44, 46, 49, 58, 59, 65, 73, 75, 76, 88, 90, 91, 92, 93, 95, 96, 99, 103, 105, 107, 109, 110], "class": [1, 2, 3, 4, 5, 7, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 56, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 103, 104, 105, 106, 107, 108, 110], "other": [1, 2, 3, 5, 10, 13, 19, 25, 30, 39, 40, 42, 43, 44, 46, 49, 52, 54, 59, 60, 61, 63, 64, 67, 71, 72, 73, 75, 80, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 106, 109, 110], "assum": [1, 2, 3, 15, 46, 49, 54, 58, 59, 73, 77, 80, 97, 99, 100, 104, 106, 108, 109, 110], "column": [1, 2, 3, 5, 10, 11, 13, 15, 16, 33, 39, 43, 46, 49, 51, 52, 55, 58, 59, 63, 64, 65, 67, 68, 71, 72, 73, 75, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "sum": [1, 2, 3, 29, 34, 35, 39, 49, 51, 59, 64, 65, 67, 70, 75, 91, 92, 93, 99, 101, 103, 104, 109, 110], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 98, 99, 107], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 17, 19, 23, 25, 26, 28, 29, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "true": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 51, 54, 58, 59, 60, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "return": [1, 2, 3, 4, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 104, 105, 108, 109, 110], "bool": [1, 2, 3, 5, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 51, 54, 58, 59, 63, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 40, 43, 44, 46, 54, 59, 63, 64, 65, 67, 68, 84, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 108, 110], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 17, 19, 21, 25, 26, 30, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 51, 52, 54, 55, 57, 58, 59, 63, 65, 67, 70, 71, 72, 73, 75, 76, 81, 83, 84, 85, 90, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 109, 110], "perfect": [1, 2, 39, 75, 101, 105], "exactli": [1, 3, 10, 39, 40, 44, 46, 66, 72, 91, 92, 93, 95, 96, 100, 101], "yield": [1, 40, 44, 100], "between": [1, 5, 9, 13, 14, 18, 19, 24, 25, 27, 29, 32, 35, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 54, 55, 56, 57, 61, 63, 64, 67, 70, 72, 73, 75, 76, 79, 83, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "below": [1, 3, 4, 5, 10, 39, 40, 43, 44, 46, 48, 51, 57, 63, 64, 65, 70, 71, 79, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "we": [1, 2, 3, 5, 7, 10, 13, 16, 25, 40, 43, 44, 46, 51, 59, 60, 62, 63, 70, 71, 73, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "loop": [1, 3, 49, 59, 93, 105], "implement": [1, 2, 3, 4, 9, 17, 25, 40, 41, 43, 44, 49, 53, 55, 56, 59, 72, 75, 85, 88, 90, 91, 95, 100, 106, 107], "what": [1, 5, 9, 10, 13, 19, 36, 39, 41, 43, 46, 63, 64, 68, 70, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "doe": [1, 2, 3, 7, 10, 43, 44, 46, 51, 54, 57, 60, 70, 71, 75, 77, 79, 83, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 104, 108, 109], "do": [1, 2, 5, 9, 10, 39, 43, 44, 59, 60, 72, 73, 77, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "fast": 1, "explain": [1, 10, 97], "python": [1, 2, 44, 62, 75, 91, 92, 98, 106], "pseudocod": [1, 107], "happen": [1, 10, 46, 65, 96, 103, 109], "n": [1, 2, 3, 5, 7, 39, 40, 43, 44, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 88, 89, 90, 93, 96, 97, 98, 99, 103, 104, 105, 108, 109, 110], "without": [1, 2, 5, 9, 10, 15, 17, 23, 40, 44, 56, 67, 75, 85, 89, 90, 96, 97, 99, 100, 101, 105, 106], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 48, 50, 57, 58, 59, 62, 63, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109], "distinct": [1, 10, 21, 59, 110], "natur": [1, 10, 103, 106], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 84, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 109, 110], "0": [1, 2, 3, 4, 5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "count_joint": 1, "len": [1, 2, 3, 7, 39, 43, 49, 58, 59, 60, 72, 73, 75, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "y": [1, 2, 3, 5, 8, 21, 33, 34, 44, 49, 51, 59, 60, 62, 71, 75, 76, 89, 90, 91, 92, 95, 97, 99, 101, 103, 104, 106, 108], "round": [1, 43, 46, 59, 75, 97, 99, 100, 108], "astyp": [1, 100, 103], "int": [1, 2, 3, 4, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 41, 43, 44, 46, 51, 52, 54, 55, 56, 57, 58, 59, 60, 64, 65, 67, 71, 72, 73, 75, 77, 79, 80, 81, 84, 90, 91, 93, 97, 100, 105, 106], "rang": [1, 3, 5, 7, 10, 15, 49, 51, 57, 59, 71, 75, 76, 93, 97, 98, 99, 101, 103, 104, 105, 106, 108, 109, 110], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 15, 16, 19, 25, 39, 43, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "pragma": 1, "cover": [1, 3, 86, 97, 98, 99], "choic": [1, 8, 46, 55, 57, 93, 99, 104, 106], "replac": [1, 58, 62, 73, 88, 89, 91, 92, 93, 96, 97, 98, 99, 103, 106], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 54, 73, 90, 91, 92], "05": [1, 10, 29, 33, 58, 71, 75, 81, 83, 95, 98, 99, 100, 101, 105], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 91, 92, 101, 103, 104], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 91, 92, 93, 97, 99, 100, 101, 103, 104, 109], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 29, 42, 44, 51, 75, 88, 90, 91, 92, 95, 97, 98, 100, 101, 103, 104], "max_it": [1, 89, 90, 96, 106], "10000": [1, 43, 98, 99], "x": [1, 2, 3, 5, 10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 40, 41, 44, 46, 48, 49, 51, 54, 56, 58, 59, 60, 62, 63, 65, 71, 72, 73, 75, 77, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 106, 108], "diagon": [1, 3, 5, 46, 49, 59], "equal": [1, 3, 10, 15, 54, 65, 70, 80, 107], "creat": [1, 2, 9, 13, 19, 21, 40, 43, 44, 46, 59, 75, 85, 89, 90, 93, 95, 96, 97, 99, 100, 109, 110], "impli": [1, 10, 39, 64, 71], "float": [1, 2, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 42, 43, 44, 46, 48, 50, 51, 57, 58, 59, 63, 64, 65, 67, 70, 71, 75, 79, 83, 90, 91, 92, 100, 101, 103, 104], "entri": [1, 3, 5, 10, 39, 40, 44, 46, 48, 52, 54, 57, 59, 63, 64, 65, 68, 88, 89, 95, 96, 101, 104, 105, 108], "maximum": [1, 10, 13, 72, 80, 84, 97, 109], "minimum": [1, 8, 10, 13, 23, 46, 48, 65, 70, 83, 97], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 13, 19, 29, 40, 44, 46, 54, 70, 75, 91, 99, 100, 101, 103, 105, 106], "default": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 31, 33, 36, 39, 40, 41, 43, 44, 46, 48, 49, 51, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 91, 93, 97, 99, 108, 109], "If": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 29, 31, 37, 39, 40, 43, 44, 46, 48, 49, 51, 54, 55, 58, 59, 62, 63, 64, 65, 68, 70, 71, 72, 75, 76, 77, 79, 80, 83, 84, 85, 86, 88, 89, 90, 91, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "have": [1, 2, 3, 4, 5, 7, 9, 10, 13, 19, 24, 27, 29, 32, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [1, 2, 3, 5, 7, 8, 9, 10, 13, 16, 17, 19, 25, 36, 39, 40, 43, 44, 45, 46, 49, 51, 52, 54, 58, 59, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "necessari": [1, 2, 3, 4, 7, 10, 15, 58, 91, 97], "In": [1, 2, 3, 5, 10, 39, 40, 43, 44, 54, 62, 63, 64, 66, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 107, 108, 109, 110], "particular": [1, 5, 6, 10, 13, 16, 17, 19, 22, 23, 25, 29, 30, 31, 34, 40, 44, 59, 63, 67, 71, 75, 80, 84, 85, 88, 89, 90, 92, 96, 99, 103, 104, 106, 108], "satisfi": [1, 3, 39], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 15, 33, 38, 40, 41, 42, 43, 44, 46, 49, 54, 56, 59, 61, 62, 65, 72, 73, 75, 77, 85, 86, 90, 97, 98, 99, 100, 101, 107], "argument": [1, 2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 34, 35, 40, 43, 44, 45, 46, 51, 54, 56, 60, 62, 63, 64, 65, 67, 70, 71, 72, 73, 75, 79, 80, 81, 83, 89, 92, 93, 96, 97, 98, 99, 104, 105, 108, 110], "when": [1, 2, 3, 4, 5, 10, 15, 17, 26, 29, 40, 44, 46, 49, 51, 54, 56, 57, 59, 62, 65, 67, 68, 70, 72, 73, 75, 76, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 103, 107, 108, 109, 110], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110], "rate": [1, 2, 3, 10, 41, 59, 90, 110], "set": [1, 2, 3, 5, 9, 10, 13, 15, 16, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 43, 44, 46, 50, 51, 53, 54, 55, 57, 59, 62, 63, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 88, 89, 91, 92, 95, 96, 97, 99, 100, 103, 104, 106, 107, 108, 109, 110], "note": [1, 2, 3, 7, 8, 10, 11, 15, 30, 34, 37, 40, 43, 44, 45, 46, 51, 54, 59, 62, 63, 68, 70, 71, 72, 73, 75, 76, 80, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "you": [1, 2, 3, 5, 7, 9, 10, 13, 17, 19, 39, 40, 42, 43, 44, 46, 51, 56, 61, 62, 63, 65, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "high": [1, 2, 10, 19, 43, 46, 54, 55, 59, 70, 73, 75, 88, 89, 91, 92, 93, 97, 98, 100, 101, 105, 108, 109, 110], "mai": [1, 2, 3, 4, 5, 10, 13, 16, 24, 25, 27, 32, 35, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 63, 64, 68, 70, 71, 72, 73, 75, 77, 80, 84, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "imposs": [1, 10, 101], "also": [1, 2, 3, 5, 7, 9, 10, 25, 37, 39, 40, 43, 44, 46, 51, 58, 62, 63, 72, 75, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "low": [1, 10, 13, 59, 63, 85, 91, 92, 96, 97, 101, 105, 109], "zero": [1, 3, 5, 40, 44, 48, 54, 59, 60, 91, 93, 104, 105, 106], "forc": [1, 2, 3, 5, 44, 91, 110], "instead": [1, 2, 3, 10, 13, 16, 19, 36, 39, 40, 43, 44, 46, 49, 59, 62, 63, 65, 67, 71, 72, 73, 75, 76, 79, 81, 83, 86, 88, 89, 90, 93, 95, 97, 99, 100, 101, 104, 105, 106, 108, 109, 110], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 13, 19, 26, 29, 33, 39, 40, 43, 44, 45, 46, 48, 49, 54, 55, 57, 58, 59, 60, 62, 63, 72, 73, 75, 77, 79, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 100, 103, 104, 105, 106, 107, 108, 109, 110], "guarante": [1, 3, 5, 14, 18, 24, 27, 32, 40, 42, 44, 47, 49, 61, 86], "produc": [1, 2, 5, 9, 10, 13, 19, 51, 63, 73, 75, 77, 79, 85, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "higher": [1, 5, 10, 39, 46, 48, 49, 51, 57, 62, 63, 64, 75, 92, 96, 97, 99, 105], "opposit": [1, 110], "occur": [1, 3, 10, 39, 58, 70, 91, 92, 93, 99, 100, 106], "small": [1, 3, 10, 39, 43, 51, 54, 57, 59, 64, 71, 89, 93, 96, 98, 100, 104, 106], "numpi": [1, 3, 4, 5, 7, 10, 15, 21, 34, 35, 43, 44, 45, 51, 54, 57, 58, 60, 62, 67, 70, 75, 76, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "max": [1, 46, 72, 73, 92, 93, 97, 100, 106], "tri": [1, 40, 44, 107], "befor": [1, 2, 3, 10, 40, 44, 57, 59, 72, 75, 80, 88, 89, 96, 97, 99, 100, 101, 103, 106, 108], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 19, 26, 31, 33, 39, 40, 43, 44, 46, 49, 51, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 90, 91, 92, 93, 95, 99, 101, 104, 108, 109], "left": [1, 2, 46, 48, 57, 59, 65, 68, 71, 91, 92, 104, 105, 106, 109], "stochast": 1, "exceed": 1, "m": [1, 5, 40, 44, 50, 51, 54, 55, 63, 68, 70, 71, 72, 91, 92, 98, 103, 104, 105, 110], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 40, 44, 62, 99, 101, 109], "length": [1, 5, 15, 29, 30, 39, 41, 46, 59, 65, 68, 72, 73, 75, 77, 80, 84, 88, 90, 97, 100, 104, 106, 109, 110], "must": [1, 2, 3, 4, 5, 7, 13, 19, 39, 40, 41, 42, 44, 46, 49, 51, 52, 57, 59, 61, 62, 63, 64, 65, 72, 73, 75, 77, 79, 80, 81, 83, 84, 90, 97, 100, 103, 107, 109, 110], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 15, 39, 43, 46, 52, 59, 60, 63, 65, 71, 77, 79, 80, 81, 83, 84, 88, 89, 90, 99, 100, 103, 104, 105, 109, 110], "ball": [1, 98], "bin": [1, 3, 65, 91, 92, 106], "ensur": [1, 2, 10, 40, 44, 54, 56, 57, 59, 60, 62, 70, 73, 75, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 106, 107, 108], "most": [1, 3, 5, 7, 10, 13, 19, 39, 43, 46, 51, 62, 63, 64, 65, 68, 70, 71, 72, 73, 76, 79, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109], "least": [1, 4, 10, 21, 34, 39, 43, 63, 64, 70, 73, 83, 93, 99, 100, 103, 106, 109], "int_arrai": [1, 59], "can": [2, 3, 4, 5, 7, 8, 9, 13, 16, 17, 19, 36, 37, 39, 40, 41, 42, 43, 44, 46, 50, 51, 52, 54, 55, 56, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 93, 95, 96, 97, 100, 104, 105, 106, 107, 108, 109, 110], "model": [2, 3, 4, 5, 9, 10, 11, 13, 19, 21, 33, 35, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 56, 58, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 102, 107, 109, 110], "For": [2, 3, 5, 7, 9, 10, 12, 13, 19, 25, 38, 39, 40, 43, 44, 46, 49, 51, 54, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 81, 83, 84, 85, 88, 89, 90, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "regular": [2, 3, 43, 62], "multi": [2, 3, 4, 10, 35, 39, 40, 43, 44, 46, 50, 51, 52, 59, 60, 64, 65, 66, 67, 72, 73, 85, 97, 99, 100, 101, 102], "task": [2, 5, 7, 10, 11, 12, 13, 15, 17, 18, 19, 28, 33, 36, 39, 43, 49, 51, 52, 57, 59, 63, 65, 73, 75, 85, 89, 90, 96, 97, 98, 99, 100, 101, 104, 106, 108, 109, 110], "cleanlearn": [2, 3, 10, 26, 33, 40, 59, 62, 74, 75, 76, 85, 86, 88, 89, 100, 108], "wrap": [2, 40, 44, 53, 62, 72, 75, 85, 88, 89, 91, 92, 95, 96, 101, 108], "instanc": [2, 3, 5, 6, 7, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 62, 71, 72, 75, 80, 88, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "sklearn": [2, 3, 4, 5, 8, 10, 21, 34, 39, 44, 51, 55, 56, 59, 62, 72, 75, 76, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 107, 108], "classifi": [2, 3, 44, 51, 59, 63, 66, 72, 73, 85, 86, 88, 89, 90, 95, 96, 99, 103, 104, 106, 107, 109, 110], "adher": [2, 44, 75], "estim": [2, 3, 4, 5, 9, 13, 16, 25, 39, 43, 44, 46, 49, 59, 63, 64, 65, 70, 72, 75, 77, 79, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 102, 105, 106, 107, 108, 109, 110], "api": [2, 3, 17, 62, 68, 71, 72, 75, 86, 97, 99, 108], "defin": [2, 3, 5, 7, 10, 17, 25, 39, 40, 41, 43, 44, 46, 73, 75, 77, 85, 91, 92, 95, 98, 99, 100, 103, 106, 110], "four": [2, 10, 98, 101, 110], "clf": [2, 3, 5, 51, 75, 85, 88, 95, 97, 99, 100, 101, 104], "fit": [2, 3, 5, 8, 10, 21, 42, 44, 54, 56, 61, 62, 72, 74, 75, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 107, 108, 110], "sample_weight": [2, 44, 75, 101], "predict_proba": [2, 5, 39, 42, 44, 51, 61, 62, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 106], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 13, 19, 25, 26, 28, 31, 33, 34, 35, 37, 39, 42, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 89, 98, 99, 101, 102, 106, 108, 109, 110], "score": [2, 3, 4, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 45, 46, 48, 51, 57, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 79, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 106, 108], "data": [2, 3, 4, 5, 7, 8, 9, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 41, 42, 43, 44, 45, 46, 51, 52, 54, 55, 56, 59, 61, 62, 63, 64, 65, 66, 70, 72, 73, 74, 75, 80, 81, 82, 83, 84, 86, 93, 94, 102], "e": [2, 3, 5, 10, 15, 25, 35, 39, 40, 43, 44, 46, 49, 51, 52, 54, 59, 60, 63, 64, 65, 66, 68, 71, 72, 73, 75, 77, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "featur": [2, 3, 4, 5, 8, 10, 11, 13, 19, 21, 22, 26, 29, 30, 31, 33, 34, 51, 54, 55, 56, 59, 72, 75, 85, 88, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 108], "element": [2, 3, 5, 39, 45, 46, 48, 59, 63, 65, 73, 80, 81, 83, 89, 90, 96, 97, 99, 110], "first": [2, 5, 10, 20, 29, 30, 39, 43, 51, 54, 59, 63, 64, 68, 71, 73, 75, 85, 88, 89, 90, 91, 93, 95, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "index": [2, 10, 29, 39, 46, 53, 54, 56, 58, 59, 60, 64, 73, 75, 80, 83, 84, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "should": [2, 3, 5, 7, 10, 17, 25, 29, 34, 35, 39, 40, 43, 44, 46, 48, 49, 51, 54, 56, 57, 58, 59, 62, 63, 64, 67, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "correspond": [2, 3, 5, 10, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 39, 40, 43, 44, 45, 46, 48, 49, 51, 54, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "differ": [2, 5, 7, 10, 13, 14, 16, 18, 24, 27, 29, 30, 32, 39, 40, 42, 43, 44, 46, 47, 51, 54, 57, 59, 60, 61, 63, 68, 70, 72, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 106, 107, 108], "sampl": [2, 3, 5, 8, 10, 13, 19, 23, 34, 46, 48, 51, 54, 55, 56, 65, 68, 71, 73, 75, 76, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 108, 109, 110], "size": [2, 10, 34, 40, 43, 44, 46, 51, 54, 55, 65, 70, 71, 75, 77, 79, 89, 93, 95, 99, 101, 103, 104, 105, 107, 109], "here": [2, 5, 7, 10, 17, 43, 46, 49, 62, 63, 64, 65, 67, 68, 71, 72, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "re": [2, 5, 40, 44, 56, 58, 63, 75, 85, 88, 89, 90, 91, 95, 96, 97, 99, 100, 108, 109, 110], "weight": [2, 10, 40, 41, 44, 51, 54, 63, 70, 73, 75, 89, 90, 91, 92, 96], "loss": [2, 41, 62, 73, 75, 93, 100], "while": [2, 3, 10, 40, 43, 44, 50, 51, 59, 75, 85, 93, 97, 99, 100, 101, 103, 104, 108], "train": [2, 3, 4, 5, 9, 10, 13, 19, 21, 35, 40, 41, 42, 44, 51, 59, 62, 63, 68, 71, 72, 75, 76, 86, 91, 92, 93, 95, 96, 98, 101, 102, 103, 104, 105, 107, 109, 110], "support": [2, 3, 4, 5, 13, 15, 17, 36, 37, 43, 45, 51, 59, 60, 62, 72, 73, 83, 85, 86, 90, 91, 92, 93, 97, 99], "your": [2, 3, 5, 9, 10, 13, 19, 39, 40, 42, 43, 44, 46, 51, 56, 59, 61, 62, 63, 64, 65, 67, 72, 73, 75, 76, 77, 79, 80, 86, 88, 89, 90, 93, 95, 98, 100, 103, 104, 105, 106, 107, 108, 109, 110], "recommend": [2, 5, 7, 10, 13, 16, 19, 43, 46, 63, 91, 92, 93, 97, 99, 100, 107, 108], "furthermor": 2, "correctli": [2, 3, 10, 39, 40, 44, 46, 49, 54, 60, 64, 65, 70, 71, 75, 77, 89, 96, 97, 99, 104, 105, 108, 109], "clonabl": [2, 75], "via": [2, 5, 7, 10, 11, 13, 16, 19, 21, 25, 39, 41, 43, 44, 51, 55, 59, 63, 68, 71, 72, 73, 75, 76, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 102, 104, 105, 106, 107, 108, 109, 110], "base": [2, 3, 4, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 45, 46, 49, 50, 51, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 67, 70, 72, 73, 75, 76, 79, 81, 83, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "clone": [2, 75, 104], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 43, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 67, 71, 75, 81, 86, 91, 97, 99, 101, 103, 104, 105, 106, 108, 110], "multipl": [2, 3, 5, 10, 13, 15, 16, 37, 39, 46, 57, 58, 63, 64, 65, 67, 70, 71, 75, 85, 91, 92, 93, 95, 99, 102, 104, 105, 108], "g": [2, 3, 5, 10, 15, 25, 35, 39, 40, 44, 46, 52, 54, 59, 65, 66, 68, 71, 72, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "manual": [2, 75, 85, 88, 89, 90, 97, 99, 106, 107, 108, 110], "pytorch": [2, 40, 41, 44, 75, 85, 90, 93, 99, 102, 104, 109], "call": [2, 3, 5, 6, 10, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 51, 59, 62, 72, 75, 89, 90, 91, 92, 96, 99, 101, 104, 106, 107, 108, 109, 110], "__init__": [2, 41, 75, 93], "independ": [2, 3, 10, 64, 75, 96, 97, 100, 107, 108, 110], "compat": [2, 40, 43, 44, 56, 62, 75, 76, 79, 83, 85, 88, 89, 97, 99, 107, 108], "neural": [2, 41, 62, 72, 75, 90, 93, 99, 104, 106, 108], "network": [2, 40, 41, 44, 62, 72, 75, 89, 90, 93, 96, 99, 104, 106, 108], "typic": [2, 10, 40, 44, 56, 72, 75, 88, 89, 90, 92, 93, 95, 96, 100, 106, 107], "initi": [2, 3, 10, 16, 21, 40, 44, 54, 63, 75, 88, 96, 99, 100], "insid": [2, 44, 75, 99, 101], "There": [2, 3, 7, 54, 85, 101, 103], "two": [2, 3, 10, 21, 29, 39, 40, 43, 44, 52, 54, 55, 56, 59, 68, 70, 71, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "new": [2, 7, 9, 10, 17, 25, 40, 43, 44, 50, 54, 58, 59, 63, 75, 89, 90, 91, 96, 98, 99, 100, 106, 107, 110], "notion": 2, "confid": [2, 3, 10, 25, 39, 43, 46, 49, 51, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 79, 83, 85, 88, 93, 100, 101, 103, 104, 105, 107, 109, 110], "packag": [2, 5, 7, 9, 10, 12, 13, 14, 18, 38, 42, 46, 47, 59, 61, 62, 68, 71, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "prune": [2, 3, 46, 65, 75, 86, 100, 105], "everyth": [2, 71, 101], "els": [2, 71, 91, 93, 97, 98, 99, 100, 103, 104, 105], "mathemat": [2, 3, 10, 49, 104], "keep": [2, 16, 17, 59, 85, 91, 97, 98, 99, 100, 109], "belong": [2, 3, 10, 39, 46, 48, 49, 54, 64, 65, 66, 67, 72, 73, 77, 81, 83, 84, 92, 93, 100, 101, 104, 106, 109, 110], "2": [2, 3, 4, 5, 7, 10, 11, 13, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 76, 80, 81, 83, 84, 98, 99, 107], "error": [2, 3, 5, 10, 40, 44, 45, 46, 48, 49, 59, 64, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 83, 86, 88, 90, 91, 92, 95, 96, 97, 98, 100, 102], "erron": [2, 3, 39, 46, 49, 59, 64, 65, 73, 75, 76, 77, 106, 108], "import": [2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 43, 45, 51, 54, 57, 58, 63, 67, 70, 75, 76, 81, 83, 84, 85, 88, 89, 95, 96, 97, 99, 100, 104, 105, 106, 108, 109, 110], "linear_model": [2, 5, 39, 59, 75, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logisticregress": [2, 3, 5, 39, 59, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logreg": 2, "cl": [2, 17, 33, 75, 85, 88, 89, 99, 101, 108], "pass": [2, 3, 5, 8, 10, 11, 13, 15, 16, 17, 19, 26, 33, 36, 40, 43, 44, 46, 50, 51, 54, 56, 59, 62, 63, 65, 71, 72, 73, 75, 80, 81, 85, 89, 90, 91, 92, 96, 97, 98, 99, 101, 103, 105, 106, 108], "x_train": [2, 88, 91, 92, 101, 103, 104, 108], "labels_maybe_with_error": 2, "had": [2, 3, 75, 105], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 44, 45, 46, 54, 61, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 89, 94, 102, 103, 106, 107, 108], "pred": [2, 46, 59, 88, 89, 100, 107, 108], "x_test": [2, 88, 91, 92, 101, 104, 108], "might": [2, 5, 10, 54, 63, 75, 80, 88, 89, 91, 92, 93, 97, 99, 105], "case": [2, 3, 10, 13, 16, 39, 51, 54, 63, 75, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108, 110], "standard": [2, 3, 5, 33, 39, 46, 62, 64, 65, 67, 73, 75, 85, 88, 91, 92, 95, 98, 100, 101, 105], "adapt": [2, 12, 13, 18, 40, 42, 59, 61, 75, 106], "skorch": [2, 75, 85, 99], "kera": [2, 61, 68, 71, 75, 85, 99, 105], "scikera": [2, 62, 75, 99], "open": [2, 43, 88, 89, 92, 95, 96, 98, 101, 104, 105, 106, 108, 110], "doesn": [2, 10, 75, 85], "t": [2, 3, 4, 7, 10, 20, 30, 31, 40, 41, 43, 44, 45, 46, 51, 57, 58, 67, 72, 73, 75, 81, 83, 84, 85, 91, 92, 93, 96, 97, 98, 100, 101, 104, 105, 108, 110], "alreadi": [2, 5, 10, 13, 19, 40, 43, 44, 49, 54, 62, 63, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 105, 106, 108], "exist": [2, 5, 10, 15, 21, 40, 43, 44, 56, 58, 62, 68, 70, 72, 75, 85, 86, 88, 89, 91, 92, 96, 103, 110], "made": [2, 5, 13, 19, 40, 44, 55, 75, 88, 89, 93, 96, 97, 99, 100, 103, 105, 107, 108], "easi": [2, 12, 49, 75, 91, 92, 98, 99, 101, 104], "inherit": [2, 7, 41, 75], "baseestim": [2, 44, 75], "yourmodel": [2, 75], "def": [2, 7, 17, 40, 44, 62, 75, 89, 90, 91, 92, 93, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "self": [2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 34, 40, 41, 43, 44, 46, 51, 72, 73, 75, 88, 91, 93, 97, 98, 100, 104, 109, 110], "refer": [2, 10, 13, 19, 40, 44, 45, 64, 65, 67, 68, 70, 71, 72, 75, 79, 80, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 107, 108], "origin": [2, 5, 10, 44, 45, 46, 58, 59, 62, 64, 65, 68, 71, 72, 75, 76, 79, 81, 83, 88, 89, 91, 93, 95, 96, 97, 99, 101, 105, 106, 108, 110], "total": [2, 3, 4, 39, 43, 59, 64, 84, 93, 99, 109], "state": [2, 3, 5, 40, 41, 44, 50, 75, 101, 104, 105, 110], "art": [2, 41, 101, 104], "northcutt": [2, 3, 39, 72, 73], "et": [2, 3, 39, 41, 72, 73], "al": [2, 3, 39, 41, 72, 73], "2021": [2, 3, 39, 72, 73], "weak": [2, 71], "supervis": [2, 10, 91, 92, 99, 103], "find": [2, 5, 9, 10, 13, 16, 17, 19, 22, 23, 25, 26, 28, 29, 30, 31, 34, 35, 39, 40, 42, 43, 44, 45, 46, 50, 56, 58, 59, 61, 68, 71, 72, 73, 75, 77, 81, 83, 85, 86, 91, 98, 100, 102, 107], "uncertainti": [2, 10, 48, 72, 75, 99, 106, 108], "It": [2, 3, 5, 7, 10, 15, 16, 19, 25, 30, 33, 35, 36, 37, 40, 44, 46, 49, 51, 54, 55, 57, 63, 70, 71, 75, 85, 91, 92, 93, 97, 99, 101, 104, 107], "work": [2, 3, 7, 10, 15, 33, 39, 40, 43, 44, 46, 49, 58, 59, 60, 62, 63, 73, 75, 85, 86, 89, 91, 92, 97, 98, 100, 106, 108], "includ": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 40, 42, 43, 44, 54, 58, 59, 61, 63, 64, 67, 68, 72, 73, 75, 79, 80, 81, 83, 85, 86, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 106, 110], "deep": [2, 42, 44, 61, 62, 75, 96], "see": [2, 3, 5, 7, 10, 13, 16, 17, 36, 39, 40, 43, 44, 45, 46, 51, 56, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "subfield": 2, "theori": [2, 101], "machin": [2, 4, 5, 9, 10, 17, 19, 36, 42, 57, 61, 75, 88, 89, 91, 92, 97, 98, 100, 103], "across": [2, 3, 5, 7, 10, 13, 16, 25, 39, 43, 51, 64, 71, 72, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 107, 108], "varieti": [2, 88, 89, 99], "like": [2, 3, 5, 6, 7, 10, 17, 35, 39, 40, 43, 44, 46, 49, 59, 62, 63, 64, 67, 68, 70, 73, 75, 76, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "pu": [2, 59], "input": [2, 3, 5, 9, 13, 19, 29, 39, 40, 43, 44, 49, 51, 54, 55, 58, 59, 60, 62, 71, 75, 85, 86, 89, 92, 93, 96, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "discret": [2, 37, 46, 49, 59, 72, 73, 77, 79, 80], "vector": [2, 3, 4, 5, 10, 13, 19, 46, 49, 51, 52, 54, 59, 72, 73, 85, 89, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105, 106, 109, 110], "would": [2, 3, 5, 10, 40, 43, 44, 46, 55, 59, 65, 75, 85, 89, 91, 93, 99, 100, 101, 106, 108, 110], "obtain": [2, 5, 8, 10, 13, 19, 46, 63, 65, 68, 71, 73, 76, 90, 92, 96, 99, 103, 105, 107, 109, 110], "been": [2, 4, 39, 46, 49, 54, 58, 59, 63, 64, 68, 70, 72, 73, 75, 90, 91, 95, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "dure": [2, 10, 19, 54, 56, 72, 75, 88, 89, 90, 95, 96, 97, 99, 101, 104, 107, 108, 110], "denot": [2, 3, 49, 51, 59, 65, 72, 73, 83], "tild": 2, "paper": [2, 4, 10, 63, 72, 81, 83, 98, 101, 103, 106, 108, 110], "cv_n_fold": [2, 3, 75, 89], "5": [2, 3, 4, 5, 8, 10, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 44, 46, 48, 50, 51, 59, 63, 64, 67, 68, 71, 75, 76, 83, 89, 91, 96, 98, 99, 104, 105, 106, 107, 109, 110], "converge_latent_estim": [2, 3], "pulearn": [2, 59], "find_label_issues_kwarg": [2, 10, 75, 86, 99, 101], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 65, 81, 99], "clean": [2, 70, 73, 75, 76, 85, 88, 89, 91, 92, 98, 108], "even": [2, 3, 7, 9, 10, 39, 43, 48, 49, 59, 75, 90, 97, 99, 100, 101, 103, 104, 105], "messi": [2, 75, 101], "ridden": [2, 75], "autom": [2, 9, 10, 75, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "robust": [2, 49, 54, 75, 92, 97, 99, 100], "prone": [2, 75], "out": [2, 3, 5, 10, 13, 19, 31, 40, 44, 46, 51, 54, 62, 65, 66, 68, 71, 72, 73, 75, 76, 84, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 106, 108, 109, 110], "current": [2, 3, 5, 7, 10, 11, 13, 16, 17, 25, 40, 44, 45, 46, 51, 63, 70, 75, 91, 92, 99, 100, 103, 105], "intend": [2, 13, 14, 16, 17, 18, 19, 35, 36, 37, 47, 54, 63, 79, 83, 90, 91, 92, 96, 101], "A": [2, 3, 4, 5, 7, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 40, 41, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 62, 63, 64, 67, 70, 71, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 107, 110], "follow": [2, 3, 10, 17, 33, 37, 39, 40, 43, 44, 51, 53, 57, 63, 64, 68, 70, 71, 72, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "tutori": [2, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "repo": 2, "wrapper": [2, 13, 62, 88, 89, 90, 108], "around": [2, 13, 70, 91, 92, 100, 105, 106, 110], "fasttext": 2, "store": [2, 4, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 72, 75, 88, 89, 95, 96, 97, 98, 99, 109, 110], "along": [2, 51, 65, 83, 91, 92, 93, 97, 99, 106], "dimens": [2, 59, 77, 80, 93, 99, 106, 109], "select": [2, 9, 10, 29, 53, 63, 73, 93, 100, 103, 106], "split": [2, 3, 5, 10, 15, 43, 51, 58, 59, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 101, 102, 104, 107, 110], "cross": [2, 3, 10, 39, 46, 49, 50, 51, 65, 68, 71, 73, 75, 76, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "fold": [2, 3, 39, 46, 49, 75, 88, 90, 95, 98, 99, 105, 109], "By": [2, 39, 64, 65, 75, 91, 97, 109], "need": [2, 3, 10, 11, 39, 40, 43, 44, 46, 54, 56, 64, 65, 67, 72, 75, 85, 89, 90, 91, 92, 96, 97, 99, 100, 101, 103, 104, 105, 109], "holdout": [2, 3, 75], "comput": [2, 3, 4, 5, 7, 8, 10, 13, 22, 23, 25, 26, 29, 30, 31, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 54, 55, 56, 59, 63, 64, 65, 67, 70, 71, 72, 73, 75, 76, 77, 79, 85, 86, 89, 91, 92, 98, 101, 102, 105, 106, 108, 109], "them": [2, 3, 5, 7, 9, 10, 12, 15, 30, 35, 38, 40, 42, 43, 44, 46, 56, 61, 63, 72, 75, 86, 88, 89, 91, 92, 93, 95, 96, 97, 99, 103, 104, 106, 108, 109, 110], "numer": [2, 3, 4, 5, 10, 13, 16, 25, 33, 37, 51, 54, 55, 70, 72, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 100, 101, 103, 104, 106, 108], "consist": [2, 3, 10, 40, 44, 53, 59, 63, 97, 109, 110], "latent": [2, 3, 49], "thei": [2, 3, 5, 10, 14, 18, 24, 27, 29, 32, 40, 41, 42, 44, 46, 47, 54, 57, 59, 62, 65, 70, 73, 75, 76, 79, 83, 85, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108, 110], "relat": [2, 3, 10, 16, 22, 23, 29, 30, 31, 34, 49, 59, 64, 75, 92, 96, 97], "close": [2, 3, 10, 43, 49, 72, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "form": [2, 3, 10, 40, 41, 44, 49, 58, 59, 73, 75, 99], "equival": [2, 3, 40, 44, 49, 72, 106, 108], "iter": [2, 3, 39, 40, 44, 46, 59, 64, 65, 75, 99, 103, 109], "enforc": [2, 40, 44, 59], "perfectli": [2, 39, 64, 101], "certain": [2, 3, 5, 10, 40, 44, 62, 71, 75, 91, 92, 97, 98, 105, 106], "dict": [2, 3, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 50, 51, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 83, 91, 92, 93, 99, 100, 110], "keyword": [2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 40, 43, 44, 46, 48, 51, 54, 56, 58, 62, 63, 65, 71, 72, 73, 75, 80, 81, 83, 91], "filter": [2, 3, 10, 43, 45, 58, 64, 66, 67, 69, 71, 78, 79, 80, 82, 83, 84, 85, 86, 88, 89, 90, 93, 96, 98, 99, 100, 104, 105, 108, 109, 110], "find_label_issu": [2, 3, 10, 33, 42, 43, 45, 46, 64, 65, 66, 67, 68, 69, 70, 71, 74, 75, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 99, 104, 105, 108, 109, 110], "particularli": [2, 85, 100, 103, 106], "filter_bi": [2, 3, 43, 46, 65, 86, 99], "frac_nois": [2, 46, 65, 81, 99], "min_examples_per_class": [2, 46, 65, 99, 101], "impact": [2, 4, 10, 91, 92, 93, 97], "ml": [2, 4, 5, 9, 10, 18, 75, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 102, 103, 104, 106, 107, 108], "accuraci": [2, 10, 41, 73, 88, 89, 90, 93, 99, 100, 101, 103, 106, 108, 109], "n_job": [2, 43, 46, 65, 77, 79, 81, 99, 100, 106, 109], "disabl": [2, 40, 44, 46, 106], "process": [2, 3, 7, 13, 16, 19, 35, 40, 43, 44, 46, 54, 58, 63, 65, 71, 77, 79, 81, 89, 90, 91, 97, 99, 100, 103, 107], "caus": [2, 46, 51, 91, 92, 97, 99], "rank": [2, 3, 10, 39, 43, 45, 46, 51, 64, 65, 66, 68, 69, 71, 72, 74, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 98, 99, 104, 105, 106, 109, 110], "get_label_quality_scor": [2, 42, 43, 45, 46, 47, 51, 63, 65, 66, 67, 68, 69, 70, 73, 74, 76, 78, 79, 81, 82, 83, 86, 99, 101, 104, 105, 109, 110], "adjust_pred_prob": [2, 10, 67, 72, 73, 101], "control": [2, 5, 9, 10, 13, 19, 43, 46, 63, 71, 72, 75, 81, 83, 91, 92, 97, 98, 99], "how": [2, 3, 5, 10, 13, 15, 16, 17, 19, 25, 39, 40, 41, 43, 44, 49, 59, 63, 64, 67, 68, 70, 72, 73, 75, 79, 83, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 105, 106, 107, 108, 109], "much": [2, 10, 39, 43, 46, 75, 97, 99, 103], "output": [2, 3, 5, 10, 13, 19, 35, 40, 41, 44, 49, 59, 62, 63, 64, 68, 70, 71, 72, 75, 79, 80, 83, 84, 85, 86, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 106, 107, 108], "print": [2, 5, 7, 13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 59, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "suppress": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80, 109, 110], "statement": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80], "big": [2, 43, 65, 71, 75, 101], "limit": [2, 5, 13, 19, 43, 54, 65, 85, 97, 105, 109, 110], "memori": [2, 40, 43, 44, 65, 71, 77, 79, 91, 109], "experiment": [2, 40, 41, 43, 44, 45, 65, 86, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "label_issues_batch": [2, 42, 65, 99], "find_label_issues_batch": [2, 42, 43, 65, 99], "pred_prob": [2, 3, 5, 8, 10, 11, 13, 19, 26, 28, 29, 31, 34, 35, 39, 43, 45, 46, 48, 49, 50, 51, 52, 59, 60, 63, 64, 65, 67, 68, 71, 72, 73, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108], "threshold": [2, 3, 4, 7, 10, 13, 21, 22, 23, 25, 31, 33, 34, 43, 57, 70, 71, 72, 73, 79, 83, 91, 97, 105, 106, 109, 110], "inverse_noise_matrix": [2, 3, 10, 49, 59, 86, 101], "label_issu": [2, 43, 46, 65, 68, 75, 77, 86, 88, 89, 90, 93, 96, 99, 100, 101, 104, 108], "clf_kwarg": [2, 3, 10, 75], "clf_final_kwarg": [2, 75], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 39, 43, 46, 48, 54, 63, 64, 65, 67, 68, 70, 71, 73, 75, 76, 79, 83, 85, 88, 89, 90, 92, 93, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108], "result": [2, 3, 9, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 43, 44, 46, 48, 57, 59, 65, 67, 68, 71, 73, 75, 76, 77, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 108, 109, 110], "identifi": [2, 3, 5, 7, 9, 10, 13, 15, 19, 30, 36, 39, 43, 45, 46, 54, 65, 68, 71, 73, 75, 76, 77, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 101, 104, 106, 108, 109, 110], "final": [2, 10, 75, 88, 95, 97, 100, 105, 107, 108], "remain": [2, 75, 86, 88, 89, 93, 97, 100, 104, 108, 110], "datasetlik": [2, 59, 75], "beyond": [2, 5, 7, 9, 10, 12, 38, 85, 88, 89, 100, 108, 109], "pd": [2, 3, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 50, 62, 63, 64, 75, 83, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 108, 110], "datafram": [2, 3, 5, 7, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 50, 59, 60, 62, 63, 64, 75, 80, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 108, 109, 110], "scipi": [2, 4, 5, 13, 16, 55, 59, 72, 97], "spars": [2, 4, 5, 10, 13, 16, 19, 21, 34, 54, 59, 60, 95, 97], "csr_matrix": [2, 4, 5, 13, 16, 19, 21, 34, 54, 97], "torch": [2, 40, 41, 44, 89, 90, 93, 96, 98, 106], "util": [2, 5, 10, 13, 19, 36, 40, 41, 44, 47, 54, 62, 63, 68, 71, 75, 85, 86, 90, 91, 92, 93, 99, 101, 106], "tensorflow": [2, 59, 62, 85, 90, 99], "object": [2, 5, 10, 13, 15, 16, 19, 35, 36, 40, 41, 43, 44, 51, 54, 56, 59, 60, 62, 65, 68, 69, 70, 71, 72, 75, 83, 85, 89, 90, 92, 93, 95, 97, 99, 100, 101, 102, 104, 108], "list": [2, 3, 5, 10, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 45, 46, 52, 54, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 79, 80, 81, 83, 84, 86, 89, 90, 91, 92, 93, 98, 99, 100, 101, 104, 105, 108, 110], "index_list": 2, "subset": [2, 3, 5, 13, 19, 39, 43, 46, 59, 73, 80, 84, 88, 89, 90, 93, 95, 96, 97, 99, 104, 105, 106, 107, 108, 110], "wa": [2, 3, 15, 17, 43, 57, 59, 63, 64, 70, 72, 84, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 107, 109, 110], "abl": [2, 3, 10, 75, 90, 99, 100, 101, 103, 104], "format": [2, 3, 5, 10, 15, 35, 40, 43, 44, 46, 49, 50, 51, 52, 54, 59, 60, 62, 63, 64, 65, 68, 71, 72, 73, 75, 77, 79, 80, 83, 84, 88, 91, 92, 93, 95, 97, 98, 100, 103, 108, 109, 110], "make": [2, 3, 5, 21, 40, 43, 44, 51, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 108], "sure": [2, 5, 43, 46, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 103, 104, 105, 106, 108], "shuffl": [2, 10, 59, 90, 93, 96, 97, 104, 106], "ha": [2, 3, 5, 6, 10, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 45, 49, 51, 54, 58, 59, 63, 68, 70, 75, 81, 83, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 110], "batch": [2, 43, 59, 62, 63, 77, 79, 93, 99, 106], "order": [2, 5, 10, 37, 39, 40, 44, 45, 46, 49, 50, 51, 57, 59, 63, 64, 65, 68, 71, 72, 73, 77, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 108, 109, 110], "destroi": [2, 59], "oper": [2, 40, 43, 44, 54, 59, 62, 73, 85, 88, 89, 96, 99, 106], "eg": [2, 5, 10, 59, 68, 71, 91, 92, 99, 100], "repeat": [2, 59, 63, 103, 106], "appli": [2, 10, 37, 40, 42, 44, 46, 51, 52, 54, 58, 59, 67, 72, 81, 85, 88, 89, 90, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 109], "array_lik": [2, 3, 39, 46, 59, 65, 72, 76], "some": [2, 3, 5, 10, 17, 25, 39, 40, 42, 44, 46, 49, 54, 58, 59, 61, 63, 64, 65, 67, 68, 71, 72, 73, 75, 77, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "seri": [2, 3, 43, 59, 60, 75, 83, 99, 100], "row": [2, 3, 5, 10, 13, 16, 30, 35, 39, 43, 46, 48, 49, 54, 55, 59, 63, 64, 65, 67, 72, 73, 75, 80, 81, 83, 84, 88, 90, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 110], "rather": [2, 3, 5, 10, 29, 39, 59, 62, 63, 70, 79, 83, 89, 98, 100, 103, 107, 108, 109, 110], "leav": [2, 46], "per": [2, 3, 5, 7, 10, 13, 16, 39, 43, 46, 51, 58, 63, 64, 65, 67, 70, 71, 73, 76, 77, 79, 83, 92, 99, 105, 110], "determin": [2, 3, 10, 15, 19, 25, 29, 33, 39, 43, 46, 51, 54, 59, 63, 65, 68, 70, 73, 79, 83, 91, 97, 99, 100, 103, 105, 106, 108], "cutoff": [2, 3, 55, 106], "consid": [2, 3, 4, 5, 10, 13, 16, 19, 26, 29, 31, 34, 39, 40, 44, 46, 54, 56, 59, 63, 70, 72, 73, 76, 79, 83, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 105, 106, 107, 108, 109], "section": [2, 3, 7, 10, 86, 93, 95, 97, 99, 100, 105], "3": [2, 3, 4, 5, 7, 10, 11, 37, 39, 40, 44, 46, 49, 50, 51, 52, 55, 57, 58, 59, 62, 65, 72, 73, 75, 76, 81, 83, 98, 99, 107], "equat": [2, 3, 49], "advanc": [2, 3, 5, 9, 10, 13, 19, 70, 72, 83, 86, 92, 94, 97, 99, 100, 101], "user": [2, 3, 5, 9, 10, 13, 17, 19, 30, 35, 36, 37, 40, 44, 46, 54, 62, 70, 72, 73, 75, 79, 83, 100, 101], "specifi": [2, 3, 4, 5, 8, 10, 13, 16, 17, 19, 21, 34, 36, 40, 43, 44, 46, 51, 54, 56, 58, 62, 63, 64, 65, 68, 70, 72, 73, 75, 76, 84, 86, 89, 90, 92, 93, 96, 97, 100, 103, 105, 108], "automat": [2, 3, 5, 29, 39, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "greater": [2, 3, 4, 5, 7, 9, 10, 31, 43, 55, 59, 70, 92, 98, 99, 110], "count": [2, 25, 29, 39, 43, 46, 49, 59, 64, 65, 71, 86, 93, 97, 99, 105], "observ": [2, 3, 49, 56, 90, 91, 92, 103, 106, 108], "mislabel": [2, 10, 39, 43, 45, 46, 49, 63, 64, 65, 68, 70, 73, 79, 81, 83, 84, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 105, 108], "one": [2, 3, 5, 7, 10, 29, 39, 40, 43, 44, 45, 46, 51, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 106, 107, 108, 110], "get_label_issu": [2, 42, 43, 74, 75, 88, 89, 101, 108], "either": [2, 3, 4, 7, 10, 40, 43, 44, 46, 55, 63, 65, 70, 72, 73, 77, 79, 92, 97, 99, 104, 105], "boolean": [2, 7, 10, 25, 43, 46, 56, 58, 63, 65, 68, 73, 75, 77, 79, 80, 85, 89, 90, 92, 93, 96, 99, 105, 108, 109], "label_issues_mask": [2, 46, 73, 75, 86], "indic": [2, 3, 4, 5, 7, 10, 13, 16, 25, 39, 43, 44, 45, 46, 48, 51, 54, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "its": [2, 5, 7, 9, 10, 13, 19, 40, 43, 44, 46, 54, 56, 57, 58, 65, 68, 71, 72, 73, 75, 77, 81, 83, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 107, 108, 109, 110], "return_indices_ranked_bi": [2, 43, 46, 65, 81, 86, 88, 89, 99, 101], "significantli": [2, 10, 93, 97, 101, 103, 107], "reduc": [2, 43, 46, 59, 90, 99], "time": [2, 10, 40, 43, 44, 59, 63, 84, 86, 91, 93, 99, 100, 105, 109, 110], "take": [2, 5, 10, 39, 40, 44, 50, 51, 54, 56, 59, 62, 73, 88, 93, 95, 103, 104, 105, 110], "run": [2, 5, 6, 7, 9, 10, 11, 12, 13, 17, 19, 29, 30, 35, 38, 40, 43, 44, 56, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 110], "skip": [2, 10, 40, 44, 75, 90, 97, 99, 100, 104, 110], "slow": [2, 3], "step": [2, 7, 29, 51, 71, 93, 97, 100, 101, 103, 107], "caution": [2, 5, 99, 100], "previous": [2, 5, 13, 16, 59, 72, 75, 86, 88, 90, 91, 95, 96, 100, 103, 107], "assign": [2, 7, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 40, 44, 50, 51, 59, 75, 88, 91, 93, 95, 97, 99, 108, 109, 110], "individu": [2, 4, 7, 10, 13, 16, 29, 40, 44, 45, 63, 67, 70, 73, 75, 81, 83, 86, 88, 92, 95, 97, 98, 99, 103, 104, 105, 110], "still": [2, 43, 44, 59, 72, 88, 93, 99, 106], "extra": [2, 40, 44, 59, 62, 63, 64, 75, 93, 96, 99, 100, 103, 106], "receiv": [2, 10, 40, 44, 45, 64, 67, 68, 75, 77, 81, 92, 105], "overwritten": [2, 75], "callabl": [2, 3, 4, 10, 29, 40, 44, 51, 54, 55, 56, 58, 62, 67, 99], "x_val": 2, "y_val": 2, "map": [2, 3, 15, 43, 44, 47, 50, 58, 59, 71, 73, 75, 80, 90, 91, 92, 93, 97, 99, 101, 104, 110], "appropri": [2, 10, 19, 37, 55, 65, 73, 91, 95, 100, 104, 105], "earli": [2, 93], "stop": [2, 93], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 25, 39, 59, 72, 88, 91, 93, 95, 97, 100, 104, 108, 110], "f": [2, 7, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108], "ignor": [2, 40, 44, 58, 62, 75, 80, 84, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "allow": [2, 13, 39, 40, 43, 44, 48, 56, 59, 63, 71, 72, 75, 77, 79, 89, 90, 93, 97, 99, 107, 109], "access": [2, 10, 16, 40, 44, 75, 92, 93, 98, 104], "hyperparamet": [2, 67, 72, 93], "purpos": [2, 54, 91, 92, 97, 99, 104, 108], "want": [2, 5, 10, 39, 43, 54, 60, 63, 65, 75, 89, 91, 93, 96, 98, 100, 103, 105, 106, 107, 109, 110], "explicitli": [2, 8, 10, 44, 54, 75], "yourself": [2, 5, 43, 92, 97], "altern": [2, 7, 10, 51, 56, 59, 62, 63, 73, 86, 89, 90, 93, 95, 96, 98, 99, 100, 101, 103, 104, 106, 108], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 19, 29, 33, 40, 43, 44, 46, 54, 59, 62, 63, 65, 72, 73, 75, 79, 80, 83, 84, 85, 88, 89, 91, 92, 93, 95, 96, 97, 99, 100, 104, 105, 106, 107, 108, 109], "effect": [2, 10, 30, 40, 44, 63, 72, 75, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 108], "offer": [2, 5, 9, 10, 89, 90, 91, 92, 96, 99, 100, 101, 104], "after": [2, 3, 5, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 63, 75, 89, 91, 93, 96, 97, 99, 100, 101, 103, 105, 106, 107, 108, 109], "attribut": [2, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 51, 56, 72, 75, 88, 91, 97], "label_issues_df": [2, 75, 93], "similar": [2, 10, 39, 40, 44, 56, 59, 63, 67, 68, 70, 72, 75, 79, 83, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105, 106, 109], "document": [2, 3, 5, 13, 17, 19, 39, 40, 43, 44, 45, 46, 51, 58, 62, 64, 65, 67, 70, 71, 72, 75, 79, 80, 81, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "descript": [2, 5, 7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 39, 45, 59, 68, 75, 91, 92], "were": [2, 3, 5, 10, 39, 44, 54, 64, 70, 83, 88, 90, 95, 99, 101, 103, 105, 107, 109], "present": [2, 3, 5, 10, 13, 15, 16, 23, 39, 59, 72, 80, 85, 93, 97, 99, 100, 106], "actual": [2, 3, 5, 10, 39, 54, 63, 64, 73, 92, 99, 101, 107, 110], "num_class": [2, 39, 43, 59, 62, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 106], "uniqu": [2, 34, 59, 80, 91, 97, 99, 100, 104, 106], "given_label": [2, 5, 11, 28, 33, 39, 49, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109, 110], "normal": [2, 3, 10, 21, 29, 34, 46, 48, 51, 57, 58, 59, 73, 97, 99, 101, 106], "trick": [2, 99], "distribut": [2, 3, 5, 10, 29, 31, 39, 44, 46, 50, 57, 63, 71, 72, 73, 85, 91, 92, 93, 95, 96, 97, 100, 105, 106], "account": [2, 39, 63, 67, 72, 73, 89, 96, 99, 101, 103, 104, 106, 108], "word": [2, 3, 58, 83, 84, 99], "remov": [2, 10, 34, 39, 40, 44, 46, 75, 85, 88, 89, 93, 96, 97, 98, 99, 100, 104, 106, 108], "so": [2, 3, 5, 6, 7, 10, 17, 29, 37, 39, 40, 43, 44, 46, 54, 59, 63, 64, 70, 73, 75, 79, 83, 90, 91, 92, 93, 96, 97, 100, 101, 104, 106, 109], "proportion": [2, 10, 46], "just": [2, 3, 5, 10, 13, 16, 35, 39, 41, 43, 59, 62, 73, 75, 77, 85, 86, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 104, 105, 106, 107, 108, 109], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 16, 34, 40, 41, 44, 46, 51, 57, 58, 59, 63, 65, 67, 72, 73, 75, 76, 77, 85, 88, 89, 90, 93, 96, 97, 98, 99, 100, 101, 106, 107, 108], "detect": [2, 5, 7, 9, 13, 16, 17, 19, 21, 25, 31, 45, 54, 57, 66, 68, 69, 70, 71, 72, 73, 74, 75, 78, 82, 85, 88, 89, 91, 94, 98, 100, 102, 104, 108, 109, 110], "arg": [2, 15, 25, 30, 34, 40, 41, 44, 51, 59, 73, 75, 100], "kwarg": [2, 7, 10, 13, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 45, 51, 54, 62, 71, 75, 77, 79, 80, 81, 99], "test": [2, 5, 10, 29, 44, 51, 54, 62, 75, 85, 88, 89, 91, 92, 93, 95, 96, 102, 107, 108, 110], "expect": [2, 3, 10, 40, 44, 46, 51, 54, 63, 72, 73, 75, 88, 89, 99, 100, 101, 103, 104, 105, 108, 110], "class_predict": 2, "evalu": [2, 10, 40, 41, 42, 43, 44, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 107, 108, 109], "simpli": [2, 10, 39, 73, 85, 89, 91, 92, 95, 96, 99, 101, 104, 108, 109, 110], "quantifi": [2, 4, 5, 7, 10, 13, 16, 46, 67, 72, 75, 85, 92, 93, 95, 96, 97, 100, 101, 105], "save_spac": [2, 10, 74, 75], "potenti": [2, 10, 39, 46, 58, 65, 68, 71, 73, 75, 77, 79, 84, 86, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "cach": [2, 89, 96], "panda": [2, 5, 7, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 59, 60, 62, 63, 64, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 108, 109], "unlik": [2, 10, 46, 48, 51, 62, 64, 65, 67, 83, 91, 100, 103, 104, 106, 108], "both": [2, 5, 10, 13, 19, 29, 39, 40, 44, 46, 54, 59, 63, 65, 73, 77, 79, 84, 85, 91, 93, 99, 100, 101, 103, 110], "mask": [2, 43, 46, 58, 59, 65, 68, 73, 75, 77, 79, 80, 85, 98, 99, 103, 105, 109, 110], "prefer": [2, 73, 81, 104], "plan": 2, "subsequ": [2, 3, 40, 44, 56, 89, 96, 99, 101, 105], "invok": [2, 40, 44, 101, 107], "scratch": [2, 54, 75], "To": [2, 5, 7, 9, 10, 12, 13, 16, 19, 29, 38, 40, 43, 44, 45, 46, 62, 63, 65, 67, 71, 72, 73, 75, 76, 77, 79, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "share": [2, 10, 73, 75], "mostli": [2, 59, 70, 75, 100, 104, 108], "longer": [2, 37, 50, 51, 58, 75, 86, 89, 96, 99, 100, 105], "info": [2, 5, 7, 10, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 75, 83, 92, 97, 98, 110], "about": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 41, 43, 48, 63, 64, 67, 71, 75, 80, 83, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106], "docstr": [2, 39, 40, 44, 59, 75, 98, 101], "unless": [2, 40, 44, 54, 75, 99], "our": [2, 3, 10, 62, 63, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "is_label_issu": [2, 11, 33, 75, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "entir": [2, 10, 29, 43, 46, 49, 64, 65, 70, 73, 75, 77, 79, 80, 85, 91, 92, 97, 99, 100, 105, 106, 107, 109, 110], "accur": [2, 3, 5, 9, 10, 13, 19, 39, 43, 46, 55, 63, 64, 65, 68, 71, 73, 75, 76, 77, 79, 80, 86, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 108], "label_qu": [2, 63, 75, 89, 101, 103, 108], "measur": [2, 5, 39, 63, 64, 75, 85, 88, 97, 98, 99, 100, 101, 103, 104, 108, 109, 110], "qualiti": [2, 3, 5, 7, 9, 10, 13, 16, 33, 34, 39, 43, 45, 46, 48, 51, 63, 64, 65, 67, 68, 70, 73, 75, 76, 79, 81, 83, 85, 86, 90, 91, 93, 99, 100, 102], "lower": [2, 4, 5, 7, 10, 13, 16, 31, 43, 51, 57, 63, 64, 67, 70, 71, 73, 75, 76, 79, 83, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "eas": 2, "comparison": [2, 40, 44, 71, 100, 101, 103], "against": [2, 40, 44, 91, 95, 97, 99, 100, 103, 104], "predicted_label": [2, 5, 11, 28, 33, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109], "ad": [2, 40, 44, 92, 103, 108], "precis": [2, 55, 57, 65, 68, 71, 97, 98, 99, 101, 109, 110], "definit": [2, 7, 37, 51, 75, 88, 95], "accessor": [2, 75], "describ": [2, 10, 21, 63, 72, 73, 75, 81, 83, 101, 103, 104, 105, 107, 110], "precomput": [2, 4, 5, 49, 54, 75, 98], "clear": [2, 40, 44, 56, 75, 89, 96, 97, 108], "save": [2, 5, 13, 19, 40, 43, 44, 71, 75, 97, 99, 105, 109, 110], "space": [2, 5, 10, 72, 75, 93, 95, 97, 98], "place": [2, 40, 44, 54, 59, 75, 88, 103], "larg": [2, 9, 10, 43, 54, 75, 93, 99, 105, 106, 109, 110], "deploi": [2, 9, 10, 75, 93, 99, 100], "care": [2, 10, 40, 44, 54, 75, 96, 97, 99, 101], "avail": [2, 4, 5, 7, 10, 15, 17, 36, 44, 56, 75, 99, 100, 101, 103, 105, 108], "cannot": [2, 5, 15, 17, 59, 100, 107, 110], "anymor": 2, "classmethod": [2, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 44, 51, 75], "__init_subclass__": [2, 42, 44, 74, 75], "set_": [2, 44, 75], "_request": [2, 44, 75], "pep": [2, 44, 75], "487": [2, 44, 75], "look": [2, 5, 7, 10, 19, 40, 44, 59, 75, 80, 88, 91, 92, 95, 96, 99, 100, 101, 103, 104, 105, 106, 109, 110], "inform": [2, 5, 7, 10, 13, 16, 19, 36, 40, 44, 56, 59, 63, 64, 68, 71, 75, 80, 83, 84, 85, 90, 91, 95, 96, 97, 98, 100, 101, 103, 106, 109, 110], "__metadata_request__": [2, 44, 75], "infer": [2, 44, 59, 75, 80, 84, 88, 89, 93, 103, 104], "signatur": [2, 40, 44, 75], "accept": [2, 40, 44, 56, 57, 73, 75, 91, 92, 99], "metadata": [2, 10, 44, 75, 93, 110], "through": [2, 5, 7, 44, 75, 89, 90, 92, 96, 97, 98, 99, 100, 103, 105, 106], "develop": [2, 9, 44, 56, 75, 99, 101, 110], "request": [2, 44, 75, 88, 89, 92, 96, 97, 98, 104, 110], "those": [2, 3, 4, 10, 43, 44, 46, 53, 62, 63, 65, 71, 75, 79, 83, 84, 85, 90, 93, 97, 99, 100, 105, 109], "http": [2, 4, 5, 7, 9, 10, 12, 21, 38, 40, 41, 43, 44, 48, 56, 59, 68, 71, 72, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "www": [2, 44, 75, 106], "org": [2, 4, 21, 40, 41, 44, 56, 59, 72, 75, 99, 100, 101, 110], "dev": [2, 44, 75], "0487": [2, 44, 75], "get_metadata_rout": [2, 42, 44, 74, 75], "rout": [2, 44, 75], "pleas": [2, 40, 44, 62, 75, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "guid": [2, 7, 10, 44, 75, 86, 90, 91, 92, 93, 94, 95, 96, 97, 100, 101], "mechan": [2, 40, 44, 75], "metadatarequest": [2, 44, 75], "encapsul": [2, 19, 44, 70, 75], "get_param": [2, 42, 44, 61, 62, 74, 75], "subobject": [2, 44, 75], "param": [2, 10, 40, 44, 62, 72, 75, 99], "name": [2, 5, 6, 7, 10, 11, 13, 15, 16, 35, 37, 39, 40, 44, 50, 51, 55, 59, 62, 63, 64, 71, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "set_fit_request": [2, 42, 44, 74, 75], "str": [2, 3, 4, 5, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 49, 51, 54, 55, 56, 57, 58, 59, 62, 63, 64, 68, 70, 71, 73, 75, 80, 84, 90, 91, 97, 99, 103, 104, 105, 110], "unchang": [2, 40, 44, 75, 97, 110], "relev": [2, 10, 19, 29, 44, 75, 93, 95, 97], "enable_metadata_rout": [2, 44, 75], "set_config": [2, 44, 75], "meta": [2, 44, 75], "rais": [2, 4, 5, 13, 15, 16, 37, 40, 44, 48, 51, 54, 57, 75, 99], "alia": [2, 40, 44, 75], "metadata_rout": [2, 44, 75], "retain": [2, 44, 59, 75], "chang": [2, 35, 37, 40, 43, 44, 48, 75, 83, 88, 89, 90, 91, 96, 99, 100, 105, 106, 110], "version": [2, 4, 5, 7, 9, 10, 12, 14, 18, 24, 27, 32, 38, 40, 42, 44, 47, 48, 59, 61, 62, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "sub": [2, 44, 70, 75], "pipelin": [2, 44, 75, 108], "otherwis": [2, 4, 7, 10, 37, 39, 40, 43, 44, 46, 52, 55, 57, 58, 59, 65, 75, 77, 79, 80, 84, 85, 89, 96, 99, 100], "updat": [2, 13, 16, 40, 43, 44, 54, 62, 75, 86, 91, 93, 100], "set_param": [2, 42, 44, 61, 62, 74, 75], "simpl": [2, 40, 44, 46, 63, 73, 75, 88, 89, 91, 92, 93, 95, 96, 100, 103, 106, 108], "well": [2, 3, 9, 10, 40, 44, 48, 49, 63, 65, 71, 73, 75, 80, 83, 84, 86, 91, 92, 93, 95, 96, 99, 100, 101, 103, 105, 106], "nest": [2, 40, 44, 45, 60, 75, 81, 83, 84, 110], "latter": [2, 40, 44, 75, 106], "compon": [2, 44, 75], "__": [2, 44, 75], "set_score_request": [2, 74, 75], "structur": [3, 72, 95, 97, 99, 100], "unobserv": 3, "less": [3, 4, 5, 10, 34, 43, 51, 63, 72, 73, 77, 79, 83, 93, 95, 97, 98, 99, 100, 101, 105, 110], "channel": [3, 90, 101], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 39, 49, 59, 64, 89, 92, 98], "inv": 3, "confident_joint": [3, 25, 39, 46, 59, 64, 65, 86, 99, 101], "un": 3, "under": [3, 10, 40, 44, 64, 71, 72, 92, 97, 100, 106], "joint": [3, 39, 46, 49, 59, 64, 65, 98], "num_label_issu": [3, 43, 46, 65, 80, 84, 86], "estimation_method": [3, 43], "off_diagon": 3, "multi_label": [3, 39, 46, 59, 60, 65, 104], "don": [3, 10, 85, 92, 93, 96, 101, 105, 108], "statis": 3, "compute_confident_joint": [3, 39, 46, 59, 65, 101], "off": [3, 46, 59, 70, 93, 101, 105, 106], "j": [3, 5, 39, 40, 44, 45, 46, 65, 68, 71, 72, 81, 83, 84, 91, 92, 101, 109, 110], "confident_learn": [3, 46, 65, 101], "off_diagonal_calibr": 3, "calibr": [3, 4, 46, 59, 63, 103], "cj": [3, 49, 59], "axi": [3, 34, 49, 51, 57, 77, 80, 90, 91, 92, 93, 97, 99, 100, 101, 103, 104, 106, 108, 109], "bincount": [3, 91, 92, 101, 103, 104], "alwai": [3, 10, 40, 44, 59, 88, 89, 90, 101, 108], "estimate_issu": 3, "over": [3, 5, 10, 40, 43, 44, 70, 71, 77, 79, 88, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108], "As": [3, 7, 85, 91, 92, 96, 100, 101, 108, 110], "add": [3, 5, 7, 13, 15, 16, 40, 44, 62, 71, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 104], "approach": [3, 39, 43, 46, 62, 88, 95, 97, 100, 101, 104, 106, 108], "custom": [3, 7, 10, 12, 33, 40, 43, 44, 51, 58, 73, 89, 92, 96, 97, 101, 108], "know": [3, 10, 91, 92, 93, 96, 99, 101, 103, 108], "cut": [3, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 35, 105, 106, 110], "underestim": 3, "few": [3, 9, 10, 71, 85, 97, 99, 103, 104, 105, 106, 110], "4": [3, 4, 5, 10, 11, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 50, 51, 58, 67, 68, 70, 71, 73, 76, 83, 98, 99, 104, 109, 110], "detail": [3, 4, 5, 10, 13, 17, 19, 36, 39, 40, 44, 45, 51, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 79, 80, 81, 85, 86, 90, 97, 99, 100, 104, 106, 110], "num_issu": [3, 7, 43, 90, 91, 92, 93, 95, 96, 97, 100, 101], "calibrate_confident_joint": 3, "up": [3, 7, 10, 20, 29, 30, 33, 46, 51, 53, 62, 63, 89, 98, 99, 105, 108, 110], "p_": [3, 39, 46], "pair": [3, 5, 10, 39, 46, 101], "v": [3, 10, 43, 64, 65, 67, 73, 91, 92, 102, 104, 105, 106, 107], "rest": [3, 5, 7, 9, 10, 12, 38, 64, 65, 67, 75, 88, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 108], "fashion": [3, 5, 77, 88], "2x2": 3, "incorrectli": [3, 39, 64, 65, 68, 95, 100, 110], "calibrated_cj": 3, "c": [3, 10, 57, 58, 65, 73, 85, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 105, 106, 107, 108], "whose": [3, 4, 5, 10, 31, 40, 44, 49, 54, 58, 63, 67, 70, 76, 79, 83, 84, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 106, 109, 110], "truli": [3, 106, 109], "estimate_joint": [3, 39, 101], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 65, 71, 101, 105, 107, 109, 110], "return_indices_of_off_diagon": 3, "frequenc": [3, 29, 63, 64, 71, 80, 105, 106], "done": [3, 10, 62, 75, 91, 99, 101, 104, 106, 107], "overfit": [3, 10, 68, 71, 88, 90, 91, 92, 93, 95, 96, 107], "classifict": 3, "singl": [3, 5, 9, 10, 15, 29, 39, 40, 44, 45, 51, 52, 59, 63, 64, 70, 71, 72, 73, 83, 88, 90, 91, 97, 99, 101, 104, 105], "baselin": [3, 40, 46, 89, 106, 108], "proxi": 3, "union": [3, 5, 15, 29, 51, 54, 55, 56, 59, 60, 65, 71, 75, 83, 99], "tupl": [3, 34, 40, 44, 45, 49, 50, 52, 54, 58, 59, 63, 65, 71, 79, 81, 83, 84, 90, 110], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 43, 49, 54, 55, 63, 72, 77, 79, 85, 89, 93, 97, 99, 100, 109], "practic": [3, 88, 89, 92, 93, 100, 101, 106, 108], "complet": [3, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "gist": 3, "cj_ish": 3, "guess": [3, 49, 101, 103], "8": [3, 5, 7, 8, 50, 51, 52, 58, 67, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 103, 104, 105, 106, 108, 109, 110], "parallel": [3, 46, 71, 81, 98], "again": [3, 62, 88, 99, 106], "simplifi": [3, 17, 99], "understand": [3, 9, 10, 39, 64, 71, 92, 97, 101, 102, 108, 109, 110], "100": [3, 4, 40, 44, 54, 55, 57, 72, 73, 88, 89, 91, 92, 93, 95, 97, 98, 99, 100, 101, 104, 105, 106, 110], "optim": [3, 40, 41, 44, 62, 88, 89, 92, 93, 95, 96, 97, 98, 101, 103, 104, 106, 108], "speed": [3, 46, 89, 98, 99, 108], "dtype": [3, 26, 28, 29, 34, 40, 44, 58, 59, 67, 83, 90, 97, 100, 105], "enumer": [3, 40, 44, 90, 91, 92, 93, 97, 110], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 44, 51, 59, 83, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "num_confident_bin": 3, "argmax": [3, 46, 73, 77, 80, 90, 97, 99, 101, 105, 106, 109], "elif": 3, "estimate_lat": 3, "py_method": [3, 49], "cnt": [3, 49], "1d": [3, 5, 13, 15, 19, 35, 43, 46, 51, 52, 54, 59, 60, 67, 76, 88, 90, 97], "eqn": [3, 49], "margin": [3, 46, 49, 51, 73], "marginal_p": [3, 49], "shorthand": [3, 13, 16], "proport": [3, 10, 39, 64, 101, 107], "poorli": [3, 49, 88, 97], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 101], "variabl": [3, 7, 17, 30, 59, 75, 76, 90, 91, 95, 101, 104, 108], "exact": [3, 10, 49, 54, 88, 91, 92, 93, 95, 97, 100], "within": [3, 4, 5, 10, 14, 18, 35, 40, 41, 44, 45, 47, 65, 70, 79, 81, 83, 91, 92, 93, 99, 105, 109], "percent": 3, "often": [3, 39, 49, 64, 99, 101, 107, 109], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 59, 60, 71, 88, 89, 90, 91, 93, 95, 96, 99, 100, 104, 105, 106, 108], "wai": [3, 5, 10, 54, 62, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107], "pro": 3, "con": 3, "pred_proba": [3, 107], "combin": [3, 39, 91, 93, 97, 98, 99, 100, 101, 107, 108], "becaus": [3, 10, 49, 55, 59, 70, 96, 97, 99, 100, 101, 103, 105, 107], "littl": [3, 43, 98, 105, 110], "uniform": [3, 73, 98, 99, 101], "20": [3, 7, 45, 84, 90, 93, 96, 97, 98, 99, 100, 101, 105, 108, 109, 110], "Such": [3, 93, 106], "bound": [3, 26, 28, 40, 44, 58, 67, 68, 70, 71, 105], "reason": [3, 10, 25, 40, 44, 55, 72], "comment": [3, 58, 97, 110], "end": [3, 5, 40, 44, 56, 71], "file": [3, 5, 15, 42, 43, 61, 71, 88, 90, 91, 95, 96, 98, 99, 105, 106, 109, 110], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 101], "handl": [3, 5, 7, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 54, 55, 56, 86, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 101, 104, 106, 108, 109, 110], "five": [3, 68, 71, 101, 105], "estimate_cv_predicted_prob": [3, 101], "estimate_noise_matric": 3, "get_confident_threshold": [3, 42, 43], "amongst": [3, 10, 100, 105], "confident_threshold": [3, 10, 25, 26, 43, 72], "point": [4, 5, 7, 9, 10, 21, 29, 40, 44, 54, 56, 85, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103], "valuat": [4, 9, 21], "help": [4, 39, 40, 44, 71, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 109, 110], "u": [4, 88, 89, 90, 91, 93, 95, 97, 99, 101, 103, 104, 107, 108, 109, 110], "assess": [4, 10, 97, 100, 105], "contribut": [4, 10, 21, 97, 105], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 13, 19, 21, 22, 29, 31, 34, 47, 53, 95, 97], "metric": [4, 5, 10, 21, 22, 24, 29, 31, 34, 47, 53, 54, 56, 57, 59, 62, 71, 72, 88, 89, 90, 93, 95, 96, 97, 100, 101, 108], "10": [4, 10, 21, 22, 26, 29, 31, 34, 40, 41, 54, 71, 72, 73, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "shaplei": [4, 10, 21], "nearest": [4, 5, 10, 13, 19, 26, 29, 31, 53, 54, 55, 56, 57, 72, 92, 96, 97, 106], "neighbor": [4, 5, 10, 13, 19, 21, 26, 29, 31, 47, 54, 55, 56, 57, 72, 91, 92, 93, 95, 96, 97, 99, 106], "knn": [4, 10, 13, 16, 21, 29, 31, 34, 53, 54, 55, 56, 57, 72, 95, 106], "graph": [4, 5, 10, 13, 16, 19, 21, 29, 34, 53, 54], "calcul": [4, 10, 21, 29, 43, 51, 53, 54, 57, 63, 67, 68, 70, 71, 72, 75, 79, 93, 98, 100], "directli": [4, 5, 10, 13, 17, 19, 36, 37, 43, 56, 62, 63, 89, 92, 96, 97, 99, 100, 104, 105, 108], "lowest": [4, 10, 63, 71, 92, 93, 95, 97, 99, 100, 103, 104, 105, 109], "fall": [4, 10, 70, 79, 83, 101, 106], "flag": [4, 10, 25, 29, 46, 51, 64, 65, 68, 75, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 106, 108, 109], "approxim": [4, 10, 21, 43, 56, 72, 97, 103], "top": [4, 5, 10, 39, 43, 45, 46, 59, 65, 68, 71, 73, 80, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 108, 110], "found": [4, 5, 7, 10, 13, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 104, 106, 108, 110], "arxiv": [4, 21, 101], "ab": [4, 21, 101, 105], "1908": 4, "08619": 4, "1911": [4, 21], "07128": [4, 21], "embed": [4, 5, 10, 13, 19, 72, 85, 89, 90, 91, 92, 95, 96, 97, 100, 101, 104, 108], "represent": [4, 5, 10, 13, 19, 37, 40, 44, 52, 54, 65, 85, 89, 90, 91, 92, 93, 96, 99, 100, 101, 106], "suppli": [4, 104, 105, 108], "2d": [4, 5, 13, 19, 35, 43, 51, 52, 54, 58, 59, 63, 88, 90, 97, 104], "num_exampl": [4, 5, 13, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 64, 90, 91, 92, 93, 95, 96, 100, 101], "num_featur": [4, 5, 13, 19, 40, 44, 62], "distanc": [4, 5, 10, 13, 19, 21, 29, 31, 34, 53, 54, 55, 56, 57, 70, 72, 95, 97, 106], "construct": [4, 5, 7, 10, 13, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 53, 54, 56, 62, 97, 100], "nearestneighbor": [4, 5, 10, 21, 54, 56, 72, 95, 106], "cosin": [4, 10, 54, 55, 57, 72, 97, 106], "dim": [4, 72, 93, 109], "euclidean": [4, 5, 10, 54, 55, 57, 70, 72, 95], "dimension": [4, 29, 55, 59, 90, 101, 106], "scikit": [4, 44, 55, 56, 59, 72, 85, 88, 89, 90, 91, 92, 95, 96, 97, 99, 108], "fewer": [4, 10, 46, 59, 72, 97, 105], "stabl": [4, 14, 18, 24, 27, 32, 42, 47, 56, 59, 61, 72, 86, 90, 91, 92, 93, 95, 96, 100, 101], "exce": [4, 54, 93, 97], "transform": [4, 10, 35, 51, 54, 57, 59, 72, 73, 88, 89, 92, 93, 96, 97, 100, 106, 110], "rel": [4, 10, 39, 54, 63, 64, 72, 91, 92, 93, 95, 96, 100, 101, 106], "adjust": [4, 41, 46, 54, 67, 72, 73, 85, 97, 100, 101], "closer": [4, 10, 70, 97, 105], "highli": [4, 92, 93], "influenti": 4, "posit": [4, 5, 10, 40, 44, 57, 59, 71, 97, 98, 106], "convers": 4, "neg": [4, 10, 70, 71, 91, 92, 97, 98], "valueerror": [4, 5, 13, 15, 16, 37, 48, 51, 54, 57, 99], "neither": [4, 5, 10, 17, 55, 105], "nor": [4, 5, 10, 17], "larger": [4, 21, 55, 75, 77, 79, 93, 96, 98, 99], "55": [4, 58, 97, 98, 105, 108], "525": 4, "unifi": 5, "audit": [5, 9, 13, 15, 16, 19, 90, 93, 94, 95, 96, 97, 99, 100, 101, 104, 105, 108], "kind": [5, 6, 7, 10, 97, 98], "addit": [5, 7, 9, 12, 13, 16, 36, 38, 40, 44, 51, 54, 56, 60, 63, 71, 80, 81, 88, 89, 90, 91, 95, 96, 97, 100, 101, 103, 106, 107], "depend": [5, 7, 9, 12, 13, 15, 16, 38, 42, 46, 48, 59, 61, 65, 72, 75, 76, 85, 97, 107], "instal": [5, 7, 9, 12, 38, 40, 42, 43, 44, 46, 61, 62, 77, 79, 97], "pip": [5, 7, 9, 12, 38, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "development": [5, 7, 9, 12, 38], "git": [5, 7, 9, 12, 38, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "github": [5, 7, 9, 12, 38, 40, 41, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108], "com": [5, 7, 9, 12, 38, 40, 41, 43, 48, 59, 72, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "egg": [5, 7, 9, 12, 38, 85, 98], "label_nam": [5, 7, 8, 10, 11, 15, 21, 34, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "image_kei": [5, 10, 13, 93, 97], "interfac": [5, 9, 10, 56, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "librari": [5, 10, 44, 56, 68, 71, 72, 85, 89, 91, 96, 97, 98, 99], "goal": [5, 108], "track": [5, 7, 16, 17, 85, 91, 98, 99, 101], "intermedi": [5, 9, 92], "statist": [5, 10, 13, 16, 25, 29, 39, 63, 64, 71, 92, 95, 96, 97, 100, 101], "convert": [5, 10, 15, 37, 40, 44, 52, 57, 60, 63, 70, 79, 83, 86, 89, 90, 93, 96, 97, 98, 99, 100, 103, 104, 105], "hug": [5, 10, 15, 93], "face": [5, 10, 15, 19, 93, 98, 104], "kei": [5, 7, 10, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 51, 63, 64, 70, 72, 91, 92, 93, 96, 99, 101, 103, 105], "string": [5, 10, 13, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 44, 55, 59, 63, 64, 76, 80, 83, 84, 89, 95, 96, 97, 99, 103, 104, 110], "dictionari": [5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 50, 59, 63, 64, 67, 68, 70, 71, 91, 92, 95, 96, 101, 103, 104, 105], "path": [5, 15, 40, 43, 44, 71, 90, 91, 97, 99, 105], "local": [5, 7, 10, 15, 40, 41, 44, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "text": [5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 45, 51, 72, 81, 83, 84, 85, 87, 91, 92, 94, 98, 99, 100, 101, 102, 103, 106], "txt": [5, 15, 110], "csv": [5, 15, 88, 89, 95, 96, 100, 108], "json": [5, 15], "hub": [5, 15], "multiclass": [5, 15, 18, 51, 59, 63, 104], "regress": [5, 7, 10, 11, 13, 15, 17, 19, 24, 33, 35, 37, 89, 91, 92, 96, 102, 103, 106], "multilabel": [5, 10, 11, 15, 17, 18, 24, 28, 35, 37, 52, 104], "imag": [5, 9, 13, 39, 44, 68, 70, 71, 72, 77, 79, 80, 85, 91, 92, 94, 98, 99, 100, 102, 103, 104, 105, 107, 109], "field": [5, 10, 40, 44], "themselv": [5, 88, 89, 97, 108], "pil": [5, 93], "cleanvis": [5, 10, 13, 97], "level": [5, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 54, 58, 81, 83, 92, 93, 99, 102, 104, 109], "load_dataset": [5, 15, 93], "glue": 5, "sst2": 5, "properti": [5, 9, 13, 15, 16, 37, 40, 44, 97], "has_label": [5, 15], "class_nam": [5, 15, 23, 39, 45, 64, 71, 80, 84, 85, 98, 101, 105, 109, 110], "empti": [5, 15, 49, 63, 92, 97, 99, 104], "find_issu": [5, 6, 7, 8, 10, 11, 13, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_typ": [5, 6, 7, 8, 10, 11, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "sort": [5, 13, 19, 43, 46, 51, 63, 65, 68, 70, 71, 73, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 108, 109, 110], "common": [5, 10, 13, 16, 19, 85, 92, 94, 97, 98, 99, 100, 101, 104, 105, 109], "real": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "world": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "interact": [5, 13, 19, 96, 99], "thereof": [5, 13, 19], "insight": [5, 13, 19, 71, 103], "best": [5, 9, 10, 13, 19, 50, 63, 73, 88, 89, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 110], "properli": [5, 10, 43, 50, 54, 59, 60, 77, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 106, 108, 109], "respect": [5, 40, 44, 68, 71, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105], "lexicograph": [5, 50, 59, 90, 91, 92, 93, 95, 96, 100, 101, 104], "squar": [5, 59, 75, 98, 108], "csr": [5, 54, 97], "evenli": 5, "omit": [5, 70, 71, 93, 97, 105], "itself": [5, 35, 40, 44, 54, 97, 105], "three": [5, 10, 39, 63, 64, 75, 80, 88, 90, 91, 92, 95, 98, 101, 103, 107, 108, 109, 110], "indptr": [5, 97], "wise": 5, "start": [5, 7, 10, 37, 40, 41, 44, 51, 85, 104, 110], "th": [5, 10, 45, 50, 58, 59, 63, 65, 68, 70, 71, 72, 81, 83, 84, 96, 104, 105, 110], "ascend": [5, 39, 64, 93, 101], "segment": [5, 77, 79, 80, 102], "reflect": [5, 10, 54, 88, 89, 95, 96, 100, 103, 105, 106, 108], "maintain": [5, 62], "kneighbors_graph": [5, 21, 56, 95], "illustr": [5, 97], "todens": 5, "second": [5, 51, 59, 71, 73, 91, 95, 99, 101, 110], "duplic": [5, 9, 24, 25, 40, 44, 54, 85, 91, 97, 100, 101, 108], "explicit": 5, "precend": 5, "collect": [5, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 63, 97, 99, 103, 110], "unspecifi": [5, 13, 19, 46, 65], "interest": [5, 13, 19, 25, 80, 84, 88, 89, 96, 97, 100, 101, 108, 109, 110], "constructor": [5, 10, 11, 13, 19, 26, 33, 54, 56], "issuemanag": [5, 9, 13, 16, 17, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 36], "respons": [5, 13, 19, 25, 56, 75, 76, 97, 98, 108, 110], "random_st": [5, 88, 90, 91, 92, 93, 97, 100, 101, 104, 106], "lab": [5, 6, 8, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 43, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108], "comprehens": [5, 85, 93, 97, 100, 104, 108], "nbr": 5, "n_neighbor": [5, 10, 21, 54, 56, 72, 97], "mode": [5, 12, 21, 40, 43, 44, 95, 106], "4x4": 5, "float64": [5, 29, 40, 44, 83], "compress": [5, 10, 54, 59, 77, 79, 97], "toarrai": [5, 54, 97], "NOT": [5, 43, 96], "23606798": 5, "41421356": [5, 54], "configur": [5, 19, 51, 92], "suppos": [5, 10, 68, 88, 89, 106, 108], "who": [5, 70, 88, 95, 97, 101, 110], "manag": [5, 8, 9, 10, 13, 16, 17, 18, 19, 20, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 62, 91, 99], "clean_learning_kwarg": [5, 10, 11, 26, 33, 99, 108], "labelissuemanag": [5, 10, 17, 24, 26], "prune_method": [5, 86], "prune_by_noise_r": [5, 46, 65, 101], "report": [5, 7, 10, 12, 13, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 84, 85, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108, 110], "include_descript": [5, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36], "show_summary_scor": [5, 13, 36, 97, 100], "show_all_issu": [5, 13, 36, 97, 100], "summari": [5, 7, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 45, 61, 62, 64, 69, 78, 79, 81, 82, 83, 86, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 108, 109, 110], "show": [5, 7, 29, 40, 44, 50, 59, 71, 80, 84, 88, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106, 108, 109, 110], "suffer": [5, 10, 13, 16, 25, 65, 73, 84, 97, 110], "onc": [5, 10, 25, 39, 40, 44, 88, 91, 99, 100, 101, 104, 105], "familiar": [5, 97], "overal": [5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 45, 51, 63, 64, 67, 70, 71, 75, 79, 80, 81, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 105, 110], "sever": [5, 7, 10, 13, 15, 16, 25, 40, 43, 44, 46, 67, 70, 72, 73, 79, 83, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 105, 106, 110], "compar": [5, 63, 72, 83, 91, 92, 95, 97, 100, 101, 105], "issue_summari": [5, 7, 10, 13, 16, 97], "With": [5, 9, 10, 43, 89, 96, 99, 101, 103, 108, 109, 110], "usag": [5, 43, 62], "usual": [5, 15, 35, 36, 93, 103, 108], "ti": [5, 63], "exhibit": [5, 7, 10, 13, 16, 80, 90, 91, 92, 93, 95, 96, 100, 101, 105], "ie": [5, 75], "likelihood": [5, 10, 43, 45, 46, 65, 70, 72, 73, 77, 81, 97], "wherea": [5, 10, 59, 65, 88, 89, 97, 107], "outlier": [5, 9, 11, 17, 24, 25, 34, 47, 54, 73, 85, 91, 92, 97, 100, 101, 102, 108], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 101, 108], "global": [5, 7, 10, 25, 40, 44, 98], "non_iid": [5, 10, 11, 17, 29, 92, 93, 95, 96, 97, 100, 101], "hypothesi": [5, 97], "iid": [5, 7, 9, 29, 85, 95, 100, 101], "never": [5, 90, 100, 101, 104, 106, 107], "someth": [5, 7, 10, 40, 44, 73, 105], "123": [5, 91, 92], "456": [5, 88, 89, 90], "nearest_neighbor": 5, "7": [5, 10, 51, 52, 62, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 103, 104, 105, 106, 108, 109, 110], "9": [5, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 45, 51, 52, 67, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 101, 103, 104, 105, 106, 108, 109, 110], "distance_to_nearest_neighbor": [5, 11, 91, 92, 93, 95, 96, 100, 101], "789": 5, "get_issu": [5, 10, 13, 16, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_nam": [5, 6, 7, 10, 13, 16, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 90, 91, 92, 93, 95, 96, 97, 100, 101], "focu": [5, 10, 13, 16, 96, 97, 100, 109, 110], "full": [5, 10, 13, 16, 43, 62, 71, 93, 100, 110], "summar": [5, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 64, 80, 84, 85, 109], "specific_issu": [5, 13, 16], "lie": [5, 10, 72, 73, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101], "get_issue_summari": [5, 10, 13, 16, 92, 97], "get_info": [5, 10, 13, 16, 92, 96, 97, 98], "yet": [5, 20, 30, 62, 98, 100, 103], "list_possible_issue_typ": [5, 17, 18], "regist": [5, 7, 17, 18, 20, 30, 40, 44, 91], "rtype": [5, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44], "registri": [5, 17, 18], "list_default_issue_typ": [5, 17, 18], "folder": [5, 90, 91, 93], "load": [5, 15, 43, 71, 93, 98, 99, 100, 101, 105, 106, 109, 110], "futur": [5, 10, 25, 40, 44, 63, 85, 91, 96], "overwrit": [5, 91], "separ": [5, 39, 51, 67, 91, 92, 93, 97, 99, 100, 105, 107], "static": 5, "rememb": [5, 96, 99, 100, 101], "part": [5, 10, 40, 44, 46, 68, 70, 71, 90, 91, 97, 98, 100, 109, 110], "ident": [5, 10, 25, 59, 96, 97], "datalab": [6, 8, 11, 13, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 85, 88, 89, 98, 100, 103, 108], "walk": [7, 100], "alongsid": [7, 13, 40, 44, 91, 99], "pre": [7, 8, 10, 40, 44, 85, 91, 92, 108], "runtim": [7, 40, 43, 44, 75, 77, 79, 90, 93, 99, 100], "issue_manager_factori": [7, 17, 91], "myissuemanag": [7, 17], "myissuemanagerforregress": 7, "decor": [7, 17], "ll": [7, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "thing": [7, 44, 89, 97, 101, 108], "next": [7, 63, 85, 88, 89, 90, 95, 96, 97, 99, 103, 105, 108, 110], "dummi": 7, "randint": [7, 34, 51, 91, 92, 97], "mark": [7, 10, 86, 105, 106, 108], "regard": [7, 92, 100, 101], "rand": [7, 51, 54, 91, 92, 97], "is_": [7, 10, 91], "_issu": [7, 10, 91], "issue_score_kei": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "whole": [7, 10, 29, 40, 44, 92, 97], "make_summari": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "popul": [7, 96, 100], "verbosity_level": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "std": [7, 105], "raw_scor": 7, "bit": 7, "involv": [7, 43, 80, 84, 97, 99, 104], "intermediate_arg": 7, "min": [7, 51, 70, 83, 91, 99, 106], "sin_filt": 7, "sin": 7, "arang": [7, 97], "kernel": [7, 97], "affect": [7, 10, 40, 44, 55, 77, 83, 96, 97, 99], "easili": [7, 10, 49, 86, 88, 89, 90, 92, 95, 96, 100, 101, 103, 104, 106, 107, 108, 109], "hard": [7, 44, 85, 98, 106], "sai": [7, 10, 40, 44, 97, 104, 109], "anoth": [7, 10, 25, 39, 43, 55, 58, 70, 73, 89, 95, 96, 97, 99, 101, 103, 106], "try": [7, 9, 10, 43, 46, 62, 63, 77, 79, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 107, 108, 109], "won": [7, 40, 44, 91, 92, 99, 104], "issue_manag": [7, 10, 12, 13, 16, 18, 21, 22, 23, 26, 28, 29, 30, 31, 33, 34, 91], "instanti": [7, 19, 43, 62, 72, 89, 90, 92, 95], "477762": 7, "286455": 7, "term": [7, 10, 49, 59, 71, 90, 91, 92, 93, 95, 96, 100, 101], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 22, 31, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 105, 106, 108, 109, 110], "003042": 7, "058117": 7, "11": [7, 10, 62, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "121908": 7, "15": [7, 57, 62, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "169312": 7, "17": [7, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 91, 92, 97, 98, 100, 101], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 34, 85, 100], "group": [8, 9, 29, 34, 85, 98, 100, 105, 110], "dbscan": [8, 10, 34], "hdbscan": 8, "etc": [8, 10, 25, 35, 40, 44, 49, 62, 63, 81, 85, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108], "sensit": [8, 10, 57, 97, 100], "ep": [8, 34, 71], "radiu": 8, "min_sampl": [8, 34], "kmean": [8, 97], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 34, 97], "cluster_id": [8, 10, 11, 34, 97], "labels_": 8, "underperforming_group": [8, 10, 11, 17, 24, 92, 93, 95, 96, 97, 100, 101], "search": [9, 10, 23, 29, 30, 47, 53, 54, 55, 58, 75, 97, 99, 100, 107], "nondefault": 9, "Near": [9, 99], "imbal": [9, 24, 67, 72, 73, 92], "spuriou": [9, 13, 93], "correl": [9, 13, 93], "null": [9, 11, 17, 24, 92, 93, 96, 100, 101], "togeth": [9, 10, 49, 89, 91, 92, 93, 95, 96, 100, 101, 108, 110], "built": [9, 51, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "own": [9, 40, 42, 44, 56, 61, 67, 68, 71, 77, 81, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 108, 109, 110], "prerequisit": 9, "basic": [9, 44, 62, 97, 100, 106], "fulli": [9, 10, 40, 44, 62, 99], "platform": [9, 10, 85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 107, 108], "write": [9, 10], "code": [9, 10, 40, 44, 49, 59, 62, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "being": [9, 10, 13, 16, 39, 40, 44, 46, 51, 58, 59, 73, 88, 95, 99, 100, 101, 108, 109], "100x": [9, 10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "faster": [9, 10, 43, 72, 75, 77, 79, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "intellig": [9, 10, 100], "quickli": [9, 10, 41, 88, 90, 93, 95, 96, 99, 100, 104, 106, 107, 109, 110], "fix": [9, 10, 63, 88, 89, 92, 95, 96, 97, 98, 100, 101, 104, 106, 107, 108], "scientist": [9, 10], "million": [9, 10, 110], "thank": [9, 10], "ai": [9, 10, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 106, 108, 110], "suggest": [9, 10, 39, 63, 64, 70, 89, 93, 96, 97, 99, 108], "power": [9, 10, 93, 98, 101, 110], "automl": [9, 10, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 107, 108], "system": [9, 10, 90, 93, 109], "foundat": [9, 10, 85, 88, 89, 92, 95, 96, 97, 98, 101, 104, 106, 107, 108], "improv": [9, 10, 63, 88, 89, 92, 93, 98, 99, 101, 102, 108, 109], "click": [9, 10, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "tune": [9, 10, 89, 90, 96, 98, 100, 106], "serv": [9, 10, 16, 19, 103], "auto": [9, 10, 88, 89, 92, 98, 99, 100, 108], "free": [9, 10, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "page": [10, 92, 99, 100, 101], "variou": [10, 16, 33, 42, 60, 61, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105], "why": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "matter": [10, 39, 64], "didn": [10, 97, 100], "plu": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "ye": [10, 11], "near_dupl": [10, 11, 17, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "class_imbal": [10, 11, 17, 23, 92, 93, 95, 96, 97, 100, 101], "data_valu": [10, 11, 17, 24, 97], "No": [10, 11, 88, 89, 96, 97, 99], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 70, 88, 89, 110], "issue_scor": 10, "atyp": [10, 72, 91, 92, 93, 95, 96, 100, 101, 106], "datapoint": [10, 34, 46, 51, 59, 73, 76, 85, 88, 89, 90, 91, 92, 95, 96, 99, 100, 107, 108], "is_issu": [10, 25], "primarili": 10, "former": [10, 40, 44], "investig": [10, 90, 97], "expertis": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "interpret": [10, 98, 99, 101, 104, 108], "annot": [10, 39, 50, 63, 64, 65, 67, 68, 70, 71, 80, 83, 84, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 105, 109], "dissimilar": [10, 95, 96], "preced": 10, "incorrect": [10, 70, 73, 76, 88, 90, 91, 92, 93, 95, 96, 97, 100, 101, 105, 108], "due": [10, 43, 46, 73, 77, 79, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108], "appear": [10, 39, 50, 64, 65, 68, 76, 92, 93, 95, 96, 97, 100, 108, 109], "now": [10, 13, 43, 86, 88, 89, 90, 92, 97, 99, 100, 103, 105, 106, 108, 110], "token": [10, 45, 58, 79, 80, 81, 82, 83, 84, 99, 101, 102], "hamper": [10, 93, 98], "analyt": [10, 85, 97, 99, 103], "lead": [10, 70, 73, 93, 97, 100, 105], "draw": [10, 91, 92], "conclus": [10, 96], "let": [10, 40, 44, 72, 73, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "sort_valu": [10, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 108], "head": [10, 88, 89, 90, 92, 93, 95, 96, 97, 98, 100, 101, 103, 108], "97": [10, 88, 98, 99, 100, 101, 105, 108, 110], "064045": 10, "58": [10, 88, 92, 97, 98, 101, 105], "680894": 10, "41": [10, 97, 98, 100, 105, 108], "746043": 10, "794894": 10, "98": [10, 98, 99, 100, 108], "802911": 10, "give": [10, 51, 73, 101, 103, 109], "li": [10, 72], "especi": [10, 88, 89, 93, 97, 99, 108], "veri": [10, 39, 64, 68, 70, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108], "rare": [10, 46, 71, 91, 92, 93, 95, 96, 99, 100, 101], "anomal": [10, 73, 91, 92, 93, 95, 96, 100, 101], "articl": [10, 43, 99], "blog": 10, "unexpect": [10, 40, 44, 96], "consequ": 10, "inspect": [10, 89, 90, 92, 93, 100, 101, 105, 108], "011562": 10, "62": [10, 97, 100, 101, 105, 108], "019657": 10, "22": [10, 90, 91, 93, 97, 98, 100, 101, 104, 105, 110], "035243": 10, "040907": 10, "42": [10, 51, 96, 97, 98, 105, 110], "056865": 10, "smaller": [10, 72, 104, 105], "extrem": [10, 13, 91, 92, 93, 95, 96, 97, 99, 100, 101], "record": [10, 40, 44, 90, 95, 108], "abbrevi": 10, "misspel": 10, "typo": [10, 84], "resolut": 10, "video": [10, 98], "audio": [10, 91, 92, 94, 99], "minor": [10, 58], "variat": 10, "translat": [10, 100], "d": [10, 57, 88, 95, 96, 97, 99, 100, 101, 104, 108, 110], "constant": [10, 34, 75], "median": [10, 33, 57], "question": [10, 25, 85, 101], "nearli": [10, 25, 92, 93, 95, 96], "awar": [10, 86, 101], "presenc": [10, 54, 56, 101], "36": [10, 97, 98, 100, 110], "066009": 10, "80": [10, 41, 88, 95, 100, 104, 108], "003906": 10, "093245": 10, "005599": 10, "27": [10, 95, 97, 98, 100, 101, 105, 110], "156720": 10, "009751": 10, "72": [10, 97, 98, 100, 101, 104, 108], "signific": [10, 88, 89, 92, 95, 96, 98, 100, 101, 104, 106, 108], "violat": [10, 85, 95, 96, 97, 100, 101], "assumpt": [10, 95, 96, 97, 100, 101], "changepoint": [10, 95, 96, 100, 101], "shift": [10, 54, 56, 95, 96, 100, 101], "drift": [10, 92, 95, 97, 100, 101], "autocorrel": [10, 95, 96, 100, 101], "almost": [10, 95, 96, 100, 101], "adjac": [10, 54, 95, 96, 100, 101], "tend": [10, 39, 49, 95, 96, 100, 101, 109, 110], "sequenti": [10, 40, 44, 62, 93], "pai": [10, 96, 97], "attent": [10, 97], "realli": [10, 89, 96, 100, 103, 109], "mere": 10, "highlight": [10, 80, 84, 91, 92, 95, 97, 109], "necessarili": [10, 63, 71, 96, 100, 101], "wrong": [10, 63, 68, 70, 86, 89, 91, 92, 96, 99, 100, 101, 105], "gap": 10, "b": [10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 58, 59, 83, 88, 95, 96, 97, 98, 99, 100, 101, 107, 110], "x1": [10, 68, 71, 105], "x2": [10, 68, 71, 105], "10th": 10, "100th": 10, "90": [10, 83, 88, 95, 100, 101, 107, 108], "similarli": [10, 40, 44, 91, 93, 95, 99, 100, 105], "associ": [10, 15, 19, 35, 37, 40, 44, 71, 103], "blogpost": 10, "proper": [10, 59, 63, 68, 71, 88, 93, 96, 99, 103, 105], "scenario": [10, 54, 56, 73, 91, 92], "underli": [10, 45, 56, 72, 81, 83, 110], "stem": [10, 72, 106], "evolv": 10, "influenc": 10, "act": [10, 70, 91], "accordingli": [10, 35, 54], "emploi": [10, 104, 106], "partit": [10, 107], "ahead": 10, "good": [10, 40, 44, 57, 62, 64, 70, 73, 77, 79, 80, 85, 93, 97, 100], "problem": [10, 35, 43, 51, 80, 85, 91, 92, 93, 96, 97, 99], "deploy": [10, 88, 89, 101, 108], "overlook": [10, 70, 105], "fact": 10, "thu": [10, 39, 44, 64, 88, 90, 95, 96, 100, 101, 107, 110], "diagnos": [10, 92, 99], "24": [10, 90, 97, 98, 100, 101, 103, 105, 108], "681458": 10, "37": [10, 91, 97, 98, 100], "804582": 10, "64": [10, 44, 88, 93, 95, 97, 101, 105], "810646": 10, "815691": 10, "78": [10, 88, 95, 98, 100, 101, 105, 108], "834293": 10, "Be": [10, 44], "cautiou": 10, "behavior": [10, 19, 39, 40, 44, 71, 99], "rarest": [10, 92, 100], "q": [10, 97, 105], "subpar": 10, "special": [10, 54, 58], "techniqu": [10, 105], "smote": 10, "asymmetr": [10, 39], "28": [10, 93, 96, 97, 98, 100, 101, 103, 110], "75": [10, 51, 91, 92, 97, 98, 100, 103, 104, 105, 108, 110], "33": [10, 40, 44, 97, 98, 100, 105], "68": [10, 88, 98, 100, 101, 105], "excess": [10, 93], "dark": [10, 97, 109], "bright": [10, 110], "blurri": [10, 93, 97], "lack": [10, 62, 97, 100], "unusu": [10, 105, 106], "discuss": [10, 99], "earlier": [10, 89, 110], "unintend": [10, 95, 96, 97], "relationship": [10, 39], "irrelev": 10, "exploit": 10, "fail": [10, 15], "unseen": 10, "hold": [10, 15], "aris": 10, "captur": [10, 39, 90, 105, 106, 109], "environment": 10, "preprocess": [10, 88, 89, 92, 95, 97, 106, 108], "systemat": [10, 80, 84, 103], "photograph": 10, "uncorrelated": [10, 97], "strongli": [10, 96, 97], "minu": [10, 73], "sole": [10, 75, 88, 91, 100, 103, 106], "review": [10, 88, 89, 92, 95, 96, 98, 99, 100, 101, 105, 108, 109, 110], "latch": 10, "onto": 10, "troublesom": 10, "spurious_correl": [10, 97], "correlations_df": [10, 97], "blurry_scor": [10, 97], "559": [10, 100], "dark_scor": [10, 93, 97], "808": 10, "light_scor": [10, 97], "723": [10, 95, 100], "odd_size_scor": [10, 97], "957": 10, "odd_aspect_ratio_scor": [10, 97], "835": 10, "grayscale_scor": [10, 97], "003": 10, "spurious": 10, "low_information_scor": [10, 93, 97], "688": [10, 100, 108], "categor": [10, 72, 87, 88, 91, 92, 94, 99, 100, 108], "characterist": [10, 39, 97], "grayscal": [10, 93, 97], "cluster": [10, 21, 34, 100], "slice": [10, 100], "poor": [10, 97, 100], "subpopul": [10, 100], "faq": [10, 85, 92, 93, 95, 96, 102], "get_self_confidence_for_each_label": [10, 51, 73], "r": [10, 43, 75, 91, 92, 97, 108, 109], "tabular": [10, 85, 87, 91, 92, 94, 97, 99, 100, 103], "encod": [10, 52, 71, 77, 80, 88, 89, 95, 96, 99, 100, 108, 109], "71": [10, 97, 98, 100, 101, 105, 108], "70": [10, 83, 95, 97, 100], "69": [10, 100, 101, 108], "subgroup": [10, 97], "wors": [10, 97, 103], "ratio": [10, 97], "miss": [10, 30, 40, 44, 59, 68, 70, 99, 100, 105, 108], "pattern": [10, 97], "isn": [10, 20, 30], "scalabl": 10, "sacrific": 10, "One": [10, 59, 72, 99], "quantif": 10, "39": [10, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 108, 109, 110], "32": [10, 90, 91, 97, 98, 100, 103, 105], "valuabl": [10, 21, 97], "exert": [10, 92], "possible_issue_typ": 10, "label_kwarg": 10, "outlier_kwarg": 10, "near_duplicate_kwarg": 10, "non_iid_kwarg": 10, "class_imbalance_kwarg": 10, "underperforming_group_kwarg": 10, "null_kwarg": 10, "data_valuation_kwarg": 10, "health_summary_paramet": [10, 24, 26, 33], "health_summari": [10, 26, 39, 85, 98], "health_summary_kwarg": 10, "tandem": [10, 98], "view": [10, 40, 44, 45, 46, 79, 81, 83, 85, 88, 89, 90, 91, 92, 95, 96, 98, 100, 101, 103, 104, 105, 106, 107, 108, 110], "strength": [10, 57, 71, 97], "scaling_factor": [10, 31, 57], "ood_kwarg": 10, "outofdistribut": [10, 31, 72, 106], "outsid": [10, 99, 104], "outlierissuemanag": [10, 17, 24, 31], "nearduplicateissuemanag": [10, 17, 22, 24], "noniidissuemanag": [10, 17, 24, 29], "num_permut": [10, 29], "permut": [10, 29], "significance_threshold": [10, 29], "signic": 10, "noniid": [10, 24], "classimbalanceissuemanag": [10, 17, 23, 24], "underperforminggroupissuemanag": [10, 17, 24, 34], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 34], "filter_cluster_id": [10, 24, 34], "clustering_kwarg": [10, 34], "nullissuemanag": [10, 17, 24, 30], "datavaluationissuemanag": [10, 17, 21, 24], "codeblock": 10, "demonstr": [10, 43, 54, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109], "howev": [10, 40, 44, 54, 59, 88, 89, 90, 93, 95, 96, 97, 100, 103, 107, 109], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 64], "versa": [10, 64], "light": [10, 93, 97, 98, 105, 109], "29": [10, 93, 97, 98, 100, 103, 104, 105, 109, 110], "low_inform": [10, 93, 97], "odd_aspect_ratio": [10, 93, 97], "35": [10, 91, 97, 98, 100, 103, 104, 105], "odd_siz": [10, 93, 97], "doc": [10, 40, 44, 72, 85, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 106, 108, 110], "spurious_correlations_kwarg": 10, "enough": [10, 43, 59, 97, 99], "label_scor": [11, 26, 28, 33, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "is_outlier_issu": [11, 91, 92, 93, 95, 96, 97, 100, 101], "outlier_scor": [11, 31, 91, 92, 93, 95, 96, 97, 100, 101, 106], "is_near_duplicate_issu": [11, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_scor": [11, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_set": [11, 22, 24, 91, 92, 93, 95, 96, 99, 100, 101], "is_non_iid_issu": [11, 92, 95, 96, 97, 100, 101], "non_iid_scor": [11, 29, 92, 95, 96, 97, 100, 101], "is_class_imbalance_issu": [11, 92, 97, 100], "class_imbalance_scor": [11, 23, 92, 97, 100], "is_underperforming_group_issu": [11, 92, 97, 100], "underperforming_group_scor": [11, 34, 92, 97, 100], "is_null_issu": [11, 92, 97, 100], "null_scor": [11, 30, 92, 97, 100], "is_data_valuation_issu": [11, 97], "data_valuation_scor": [11, 21, 97], "studio": [12, 85, 88, 89, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "data_issu": [12, 13, 18, 19, 36], "issue_find": [12, 18], "factori": [12, 18, 19], "model_output": [12, 18], "incorpor": [13, 86, 101], "vision": [13, 93], "create_imagelab": [13, 14], "huggingfac": [13, 90, 91, 92, 93, 99], "imagelabdataissuesadapt": [13, 14], "strategi": [13, 16, 51, 97, 99], "dataissu": [13, 16, 18, 19, 36], "_infostrategi": [13, 16], "basi": [13, 16], "filter_based_on_max_preval": 13, "max_num": 13, "collect_issues_from_imagelab": [13, 16], "collect_issues_from_issue_manag": [13, 16], "collect_statist": [13, 16], "reus": [13, 16, 25], "avoid": [13, 16, 40, 43, 44, 46, 54, 59, 65, 68, 71, 75, 77, 79, 91, 92, 99, 100], "recomput": [13, 16, 89], "weighted_knn_graph": [13, 16], "issue_manager_that_computes_knn_graph": [13, 16], "set_health_scor": [13, 16], "health": [13, 16, 26, 39, 64, 85], "correlationvisu": [13, 14], "visual": [13, 68, 69, 71, 88, 91, 92, 93, 108, 110], "title_info": 13, "ncol": [13, 93, 106], "cell_siz": 13, "correlationreport": [13, 14], "anyth": [13, 101], "imagelabreporteradapt": [13, 14], "get_report": [13, 36], "report_str": [13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36], "imagelabissuefinderadapt": [13, 14], "issuefind": [13, 18, 19, 36], "get_available_issue_typ": [13, 19], "handle_spurious_correl": [13, 14], "imagelab_issu": 13, "_": [13, 22, 23, 25, 26, 28, 29, 30, 33, 34, 51, 58, 59, 88, 90, 91, 93, 97, 98, 101, 104], "imagelab": [14, 16, 18], "except": [15, 40, 44, 62, 73, 91, 92, 93, 100, 103], "dataformaterror": [15, 18], "add_not": 15, "with_traceback": 15, "tb": 15, "__traceback__": 15, "datasetdicterror": [15, 18], "datasetdict": 15, "datasetloaderror": [15, 18], "dataset_typ": 15, "sublist": 15, "map_to_int": 15, "abc": [15, 25, 35], "is_avail": [15, 93], "central": [16, 110], "repositori": 16, "get_data_statist": [16, 18], "concret": 17, "subclass": [17, 40, 44, 72, 91], "regressionlabelissuemanag": [17, 24, 32, 33], "multilabelissuemanag": [17, 24, 27, 28], "from_str": [17, 37, 47, 51], "my_issu": 17, "logic": [17, 37, 43, 46, 77, 79, 100], "modeloutput": [18, 35], "multiclasspredprob": [18, 35], "regressionpredict": [18, 35], "multilabelpredprob": [18, 35], "instati": 19, "public": [19, 97, 100, 101, 105, 109, 110], "creation": [19, 44, 97], "execut": [19, 40, 44, 91, 99, 105], "coordin": [19, 68, 70, 71, 105, 110], "At": [19, 71, 99], "direct": [20, 30, 40, 44, 56, 62], "vstack": [21, 59, 93, 98, 99, 101, 103, 104], "25": [21, 29, 40, 51, 57, 92, 93, 97, 98, 100, 101, 103, 104, 105, 110], "classvar": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "short": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 58, 59], "item": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 91, 92, 93, 99, 101, 103, 104], "some_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "additional_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "default_threshold": [21, 24, 31], "collect_info": [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "info_to_omit": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "compos": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 40, 44, 89, 96, 106], "is_x_issu": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "x_score": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_a": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b1": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b2": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "occurr": [22, 23, 25, 29, 30, 31, 34, 58], "median_nn_dist": 22, "bleed": [24, 27, 32, 42], "edg": [24, 27, 32, 42, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108, 110], "sharp": [24, 27, 32, 42], "get_health_summari": [24, 26], "ood": [24, 31, 72, 73, 106], "simplified_kolmogorov_smirnov_test": [24, 29], "outlier_cluster_label": [24, 34], "no_underperforming_cluster_id": [24, 34], "perform_clust": [24, 34], "get_underperforming_clust": [24, 34], "find_issues_with_predict": [24, 32, 33], "find_issues_with_featur": [24, 32, 33], "believ": [25, 109], "priori": [25, 101], "abstract": [25, 35], "applic": [26, 63, 97, 99, 101, 103, 110], "typevar": [26, 28, 40, 44, 58, 67, 70, 71], "scalartyp": [26, 28], "covari": [26, 28, 75, 108], "summary_dict": 26, "neighbor_histogram": 29, "non_neighbor_histogram": 29, "kolmogorov": 29, "smirnov": 29, "largest": [29, 43, 51, 54, 73, 77, 79, 105, 109], "empir": [29, 50, 63], "cumul": 29, "ecdf": 29, "histogram": [29, 95, 97, 108], "absolut": [29, 33], "trial": 29, "null_track": 30, "extend": [30, 52, 62, 93, 97, 100, 105, 106, 110], "superclass": 30, "arbitrari": [30, 39, 79, 83, 91, 106, 108], "prompt": 30, "address": [30, 89, 91, 92, 96, 99], "enabl": [30, 44, 56, 100], "37037": 31, "q3_avg_dist": 31, "iqr_avg_dist": 31, "median_outlier_scor": 31, "issue_threshold": 31, "multipli": [33, 57], "deleg": 33, "confus": [34, 35, 39, 40, 44, 46, 59, 71, 89, 110], "50": [34, 44, 97, 99, 100, 101, 103, 105, 106, 108], "keepdim": [34, 99], "signifi": 34, "absenc": 34, "int64": [34, 90, 100, 103], "npt": 34, "int_": 34, "id": [34, 63, 91, 93, 97, 99, 103], "unique_cluster_id": 34, "exclud": [34, 36, 45, 80, 84, 91, 110], "worst": [34, 51, 103], "performed_clust": 34, "worst_cluster_id": 34, "convent": [35, 37], "subject": [35, 37, 100], "meant": [35, 37], "Not": [35, 56], "mainli": [35, 106, 110], "content": [35, 72, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "fetch": [35, 43, 90, 92, 97, 99], "datset": 36, "enum": [37, 51], "qualnam": [37, 51], "boundari": [37, 51, 91, 92], "continu": [37, 62, 88, 89, 93, 96, 99, 103, 105, 108, 110], "binari": [37, 51, 59, 65, 67, 101, 110], "simultan": [37, 108], "task_str": 37, "is_classif": 37, "__contains__": [37, 47, 51], "member": [37, 40, 44, 51, 91], "typeerror": [37, 51], "12": [37, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "__getitem__": [37, 47, 51], "match": [37, 39, 40, 44, 46, 51, 63, 64, 73, 91, 92, 93, 98, 105, 107, 109], "__iter__": [37, 47, 51], "__len__": [37, 47, 51], "alias": [37, 51], "is_regress": 37, "is_multilabel": 37, "overview": [39, 54, 88, 89, 90, 92, 93, 95, 96, 103, 105, 106, 108, 110], "modifi": [39, 40, 43, 44, 54, 56, 59, 99, 100, 101], "rank_classes_by_label_qu": [39, 92], "merg": [39, 54, 58, 85, 98, 99, 100, 110], "find_overlapping_class": [39, 99, 101], "problemat": [39, 64, 80, 84, 90, 105, 110], "unnorm": [39, 64, 101], "abov": [39, 40, 43, 44, 56, 59, 63, 70, 71, 73, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "model_select": [39, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 108], "cross_val_predict": [39, 44, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 107, 108], "get_data_labels_from_dataset": 39, "yourfavoritemodel": [39, 101], "cv": [39, 51, 88, 90, 91, 92, 95, 97, 100, 101, 103], "df": [39, 59, 84, 90, 97, 99], "overall_label_qu": [39, 64], "col": 39, "prob": [39, 58, 101, 107], "divid": [39, 64, 73], "label_nois": [39, 64], "human": [39, 98, 109, 110], "clearli": [39, 73, 93, 105, 109], "num": [39, 64, 98, 101], "overlap": [39, 85, 97, 98, 99, 101], "ontolog": 39, "publish": [39, 110], "therefor": [39, 73, 97, 100], "vehicl": [39, 98], "truck": [39, 97, 98, 106, 109], "intuit": [39, 64], "car": [39, 98, 105, 109], "frequent": [39, 63, 97, 99, 100, 108], "l": [39, 40, 44, 68, 70, 71], "class1": 39, "class2": 39, "dog": [39, 59, 64, 66, 80, 98, 99, 106, 107, 110], "cat": [39, 59, 64, 66, 98, 99, 106, 107], "co": [39, 40, 41], "noisy_label": [39, 91, 92, 104], "overlapping_class": 39, "descend": [39, 40, 44, 51, 64, 71], "overall_label_health_scor": [39, 64, 101], "half": [39, 40, 42, 44, 64, 98, 110], "health_scor": [39, 64], "classes_by_label_qu": [39, 92], "cnn": [40, 42, 44, 93], "cifar": [40, 41, 97, 98, 106], "teach": [40, 41], "bhanml": 40, "blob": [40, 97], "master": [40, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108], "call_bn": [40, 42], "bn": 40, "input_channel": 40, "n_output": 40, "dropout_r": 40, "top_bn": 40, "architectur": [40, 44], "shown": [40, 71, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 107, 109, 110], "forward": [40, 41, 42, 44, 93, 103], "overridden": [40, 44], "although": [40, 44, 72, 88, 95, 100], "recip": [40, 44], "afterward": [40, 44], "sinc": [40, 44, 48, 60, 64, 71, 79, 83, 99, 100, 103, 104, 105, 107, 110], "hook": [40, 44, 98], "silent": [40, 43, 44], "t_destin": [40, 42, 44], "__call__": [40, 42, 44, 47, 51], "add_modul": [40, 42, 44], "child": [40, 44], "fn": [40, 44, 71], "recurs": [40, 44, 51], "submodul": [40, 44, 53], "children": [40, 42, 44, 110], "nn": [40, 41, 44, 54, 93], "init": [40, 44, 101], "no_grad": [40, 44, 93, 106], "init_weight": [40, 44], "linear": [40, 44, 89, 93, 96], "fill_": [40, 44], "net": [40, 44, 90, 93, 98], "in_featur": [40, 44], "out_featur": [40, 44], "bia": [40, 44, 93], "tensor": [40, 41, 44, 90, 93, 106], "requires_grad": [40, 44], "bfloat16": [40, 42, 44], "cast": [40, 44, 90], "buffer": [40, 42, 44], "datatyp": [40, 44], "xdoctest": [40, 44], "undefin": [40, 44], "var": [40, 44], "buf": [40, 44], "20l": [40, 44], "1l": [40, 44], "5l": [40, 44], "call_super_init": [40, 42, 44], "immedi": [40, 44, 106], "compil": [40, 42, 44, 62], "cpu": [40, 42, 44, 46, 90, 93], "move": [40, 44, 51, 86, 98], "cuda": [40, 42, 44, 90, 93], "devic": [40, 44, 90, 93, 100], "gpu": [40, 44, 89, 90, 96], "live": [40, 44], "copi": [40, 44, 75, 88, 90, 91, 92, 95, 97, 99, 100, 104, 107, 108], "doubl": [40, 42, 44], "dump_patch": [40, 42, 44], "eval": [40, 42, 44, 93, 104, 106], "dropout": [40, 44], "batchnorm": [40, 44], "grad": [40, 44], "extra_repr": [40, 42, 44], "line": [40, 44, 85, 91, 97, 98, 103, 106, 110], "get_buff": [40, 42, 44], "target": [40, 41, 44, 75, 76, 97, 106, 108], "throw": [40, 44], "get_submodul": [40, 42, 44], "explan": [40, 44], "qualifi": [40, 44], "referenc": [40, 44], "attributeerror": [40, 44], "invalid": [40, 44, 96], "resolv": [40, 44, 97, 110], "get_extra_st": [40, 42, 44], "state_dict": [40, 42, 44], "set_extra_st": [40, 42, 44], "build": [40, 44, 54, 93, 97, 109], "picklabl": [40, 44], "serial": [40, 44], "backward": [40, 44, 93], "break": [40, 44, 93, 105], "pickl": [40, 44, 105], "get_paramet": [40, 42, 44], "net_b": [40, 44], "net_c": [40, 44], "conv": [40, 44], "conv2d": [40, 44, 93], "16": [40, 44, 51, 54, 62, 79, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 109, 110], "kernel_s": [40, 44], "stride": [40, 44], "200": [40, 44, 73, 97, 98, 105, 110], "diagram": [40, 44, 107], "degre": [40, 44], "queri": [40, 44, 54, 56, 92, 93, 97, 99, 100, 104], "named_modul": [40, 42, 44], "o": [40, 44, 57, 58, 90, 91, 92, 98, 99, 100, 101, 104, 105, 110], "transit": [40, 44], "ipu": [40, 42, 44], "load_state_dict": [40, 42, 44], "strict": [40, 44, 51], "persist": [40, 44], "strictli": [40, 44], "inplac": [40, 44, 97, 103], "preserv": [40, 44, 59], "namedtupl": [40, 44], "missing_kei": [40, 44], "unexpected_kei": [40, 44], "runtimeerror": [40, 44], "idx": [40, 44, 59, 60, 71, 91, 93, 97, 99, 100, 101, 103, 105, 106], "named_buff": [40, 42, 44], "prefix": [40, 44, 90, 110], "remove_dupl": [40, 44], "prepend": [40, 44], "running_var": [40, 44], "named_children": [40, 42, 44], "conv4": [40, 44], "conv5": [40, 44], "memo": [40, 44], "named_paramet": [40, 42, 44], "register_backward_hook": [40, 42, 44], "deprec": [40, 44, 48], "favor": [40, 44], "register_full_backward_hook": [40, 42, 44], "removablehandl": [40, 44], "register_buff": [40, 42, 44], "running_mean": [40, 44], "register_forward_hook": [40, 42, 44], "with_kwarg": [40, 44], "always_cal": [40, 44], "possibli": [40, 44, 88, 95], "fire": [40, 44, 98], "register_module_forward_hook": [40, 44], "regardless": [40, 44, 91, 92], "register_forward_pre_hook": [40, 42, 44], "And": [40, 44], "forward_pr": [40, 44], "register_module_forward_pre_hook": [40, 44], "gradient": [40, 44, 93, 95, 108], "grad_input": [40, 44], "grad_output": [40, 44], "technic": [40, 44], "caller": [40, 44], "register_module_full_backward_hook": [40, 44], "register_full_backward_pre_hook": [40, 42, 44], "backward_pr": [40, 44], "register_module_full_backward_pre_hook": [40, 44], "register_load_state_dict_post_hook": [40, 42, 44], "post": [40, 44, 54], "incompatible_kei": [40, 44], "modif": [40, 44, 54], "thrown": [40, 44], "register_modul": [40, 42, 44], "register_paramet": [40, 42, 44], "register_state_dict_pre_hook": [40, 42, 44], "keep_var": [40, 44], "requires_grad_": [40, 42, 44], "autograd": [40, 44], "freez": [40, 44, 89, 90, 96], "finetun": [40, 44], "gan": [40, 44], "share_memori": [40, 42, 44], "share_memory_": [40, 44], "destin": [40, 44], "shallow": [40, 44], "releas": [40, 44, 62, 86, 99], "design": [40, 44, 54], "ordereddict": [40, 44], "detach": [40, 44, 93], "non_block": [40, 44], "memory_format": [40, 44], "channels_last": [40, 44], "Its": [40, 44, 51, 64, 70], "complex": [40, 44, 100], "integr": [40, 44, 56, 85, 99], "asynchron": [40, 44], "host": [40, 44], "pin": [40, 44, 89, 96, 98], "desir": [40, 44, 54, 58, 71], "4d": [40, 44], "ignore_w": [40, 44], "determinist": [40, 44, 90], "1913": [40, 44], "3420": [40, 44], "5113": [40, 44], "2325": [40, 44], "env": [40, 44], "torch_doctest_cuda1": [40, 44], "gpu1": [40, 44], "1914": [40, 44], "5112": [40, 44], "2324": [40, 44], "float16": [40, 44], "cdoubl": [40, 44], "3741": [40, 44], "2382": [40, 44], "5593": [40, 44], "4443": [40, 44], "complex128": [40, 44], "6122": [40, 44], "1150": [40, 44], "to_empti": [40, 42, 44], "storag": [40, 44], "dst_type": [40, 44], "xpu": [40, 42, 44], "zero_grad": [40, 42, 44, 93], "set_to_non": [40, 44], "reset": [40, 44], "context": [40, 44, 105], "noisili": [41, 101], "han": 41, "2018": 41, "cifar_cnn": [41, 42], "loss_coteach": [41, 42], "y_1": 41, "y_2": 41, "forget_r": 41, "class_weight": 41, "logit": [41, 62, 93], "decim": [41, 59], "forget": [41, 51, 110], "rate_schedul": 41, "epoch": [41, 42, 44, 93, 99], "initialize_lr_schedul": [41, 42], "lr": [41, 42, 44], "001": [41, 73, 97, 99], "250": [41, 91, 92, 101, 105], "epoch_decay_start": 41, "schedul": 41, "beta": 41, "adam": 41, "adjust_learning_r": [41, 42], "alpha_plan": 41, "beta1_plan": 41, "forget_rate_schedul": [41, 42], "num_gradu": 41, "expon": 41, "tell": [41, 89, 93, 96, 101], "train_load": [41, 44], "model1": [41, 101], "optimizer1": 41, "model2": [41, 101], "optimizer2": 41, "dataload": [41, 93, 106], "parser": 41, "parse_arg": 41, "num_iter_per_epoch": 41, "print_freq": 41, "topk": 41, "top1": 41, "top5": 41, "test_load": 41, "offici": [42, 61, 97, 110], "wish": [42, 61, 100, 106, 109, 110], "adj_confident_thresholds_shar": [42, 43], "labels_shar": [42, 43], "pred_probs_shar": [42, 43], "labelinspector": [42, 43, 99], "get_num_issu": [42, 43], "get_quality_scor": [42, 43], "update_confident_threshold": [42, 43], "score_label_qu": [42, 43], "split_arr": [42, 43], "span_classif": 42, "display_issu": [42, 45, 78, 79, 80, 81, 82, 83, 84, 109, 110], "mnist_pytorch": 42, "get_mnist_dataset": [42, 44], "get_sklearn_digits_dataset": [42, 44], "simplenet": [42, 44], "batch_siz": [42, 43, 44, 77, 79, 93, 99, 106, 109], "log_interv": [42, 44], "momentum": [42, 44], "no_cuda": [42, 44], "test_batch_s": [42, 44, 93], "loader": [42, 44, 93], "set_predict_proba_request": [42, 44], "set_predict_request": [42, 44], "coteach": [42, 86], "mini": [43, 77, 79, 99], "low_self_confid": [43, 46, 65], "self_confid": [43, 46, 47, 51, 65, 67, 73, 81, 83, 88, 89, 99, 101], "conveni": [43, 56, 88, 89, 90, 96, 100], "script": 43, "labels_fil": [43, 99], "pred_probs_fil": [43, 99], "quality_score_kwarg": 43, "num_issue_kwarg": 43, "return_mask": 43, "variant": [43, 63, 109], "read": [43, 48, 92, 99, 101, 106, 110], "zarr": [43, 99], "memmap": [43, 109], "pythonspe": 43, "mmap": [43, 99], "hdf5": 43, "further": [43, 45, 64, 65, 67, 70, 71, 79, 80, 90, 97, 99, 100], "yourfil": 43, "npy": [43, 98, 99, 109], "mmap_mod": [43, 109], "tip": [43, 46, 62, 99], "save_arrai": 43, "your_arrai": 43, "disk": [43, 98, 99], "npz": [43, 110], "maxim": [43, 63, 77, 79, 100, 109], "multiprocess": [43, 46, 65, 77, 79, 93, 99], "linux": [43, 77, 79], "physic": [43, 46, 77, 79, 105], "psutil": [43, 46, 77, 79], "labels_arrai": [43, 60], "predprob": 43, "pred_probs_arrai": 43, "back": [43, 54, 71, 91, 99, 100, 105, 106], "store_result": 43, "becom": [43, 97, 106], "verifi": [43, 56, 99, 100, 103, 106], "long": [43, 63, 72, 100, 103], "chunk": [43, 107], "ram": [43, 98], "end_index": 43, "labels_batch": 43, "pred_probs_batch": 43, "batch_result": 43, "indices_of_examples_with_issu": [43, 99], "shortcut": 43, "encount": [43, 46, 77], "1000": [43, 90, 96, 99, 106], "aggreg": [43, 47, 51, 63, 67, 70, 73, 83, 99, 101, 103], "seen": [43, 99, 100, 106, 110], "far": [43, 63, 100], "label_quality_scor": [43, 67, 70, 73, 76, 101, 105], "method1": 43, "method2": 43, "normalized_margin": [43, 46, 47, 51, 65, 67, 73, 81, 83], "low_normalized_margin": [43, 46, 65], "issue_indic": [43, 70, 93], "update_num_issu": 43, "arr": [43, 99], "chunksiz": 43, "convnet": 44, "bespok": [44, 62], "download": [44, 90, 97, 99, 106], "mnist": [44, 85, 90, 98], "handwritten": 44, "digit": [44, 90, 98], "last": [44, 51, 68, 71, 91, 92, 99, 100, 103, 105, 110], "sklearn_digits_test_s": 44, "01": [44, 73, 75, 90, 97, 101, 104, 105], "templat": 44, "flexibli": 44, "among": [44, 63, 101], "test_set": 44, "overrid": 44, "train_idx": [44, 59, 106], "train_label": [44, 89, 100, 106], "span": [45, 100], "sentenc": [45, 58, 81, 83, 84, 89, 96], "token_classif": [45, 58, 81, 83, 84, 99], "encourag": [46, 65, 73, 76], "multilabel_classif": [46, 64, 65, 67, 73, 99, 104], "pred_probs_by_class": 46, "prune_count_matrix_col": 46, "rank_by_kwarg": [46, 65, 73, 101], "num_to_remove_per_class": [46, 65], "bad": [46, 54, 65, 70, 73, 96, 99], "seem": [46, 101, 104], "aren": 46, "confidence_weighted_entropi": [46, 47, 51, 65, 67, 73, 81, 83], "label_issues_idx": [46, 73, 100], "entropi": [46, 48, 50, 51, 72, 73], "prune_by_class": [46, 65, 101], "predicted_neq_given": [46, 65, 101], "prune_counts_matrix": 46, "smallest": [46, 73], "unus": 46, "number_of_mislabeled_examples_in_class_k": 46, "delet": [46, 85, 89, 99], "too": [46, 51, 54, 72, 93, 99, 100, 105], "thread": [46, 65], "window": [46, 98], "shorter": [46, 68], "find_predicted_neq_given": 46, "find_label_issues_using_argmax_confusion_matrix": 46, "remove_noise_from_class": [47, 59], "clip_noise_r": [47, 59], "clip_valu": [47, 59], "value_count": [47, 59, 99], "value_counts_fill_missing_class": [47, 59], "get_missing_class": [47, 59], "round_preserving_sum": [47, 59], "round_preserving_row_tot": [47, 59], "estimate_pu_f1": [47, 59], "confusion_matrix": [47, 59], "print_square_matrix": [47, 59], "print_noise_matrix": [47, 59, 101], "print_inverse_noise_matrix": [47, 59], "print_joint_matrix": [47, 59, 101], "compress_int_arrai": [47, 59], "train_val_split": [47, 59], "subset_x_i": [47, 59], "subset_label": [47, 59], "subset_data": [47, 59], "extract_indices_tf": [47, 59], "unshuffle_tensorflow_dataset": [47, 59], "is_torch_dataset": [47, 59], "is_tensorflow_dataset": [47, 59], "csr_vstack": [47, 59], "append_extra_datapoint": [47, 59], "get_num_class": [47, 59], "num_unique_class": [47, 59], "get_unique_class": [47, 59], "format_label": [47, 59], "smart_display_datafram": [47, 59], "force_two_dimens": [47, 59], "latent_algebra": [47, 86], "compute_ps_py_inv_noise_matrix": [47, 49], "compute_py_inv_noise_matrix": [47, 49], "compute_inv_noise_matrix": [47, 49], "compute_noise_matrix_from_invers": [47, 49], "compute_pi": [47, 49], "compute_pyx": [47, 49], "label_quality_util": 47, "get_normalized_entropi": [47, 48], "multilabel_util": [47, 104], "stack_compl": [47, 52], "get_onehot_num_class": [47, 52], "int2onehot": [47, 52, 104], "onehot2int": [47, 52, 104], "multilabel_scor": [47, 67], "classlabelscor": [47, 51], "exponential_moving_averag": [47, 51, 67], "softmin": [47, 51, 67, 70, 79, 83], "possible_method": [47, 51], "multilabelscor": [47, 51], "get_class_label_quality_scor": [47, 51], "multilabel_pi": [47, 51], "get_cross_validated_multilabel_pred_prob": [47, 51], "default_k": [47, 53, 54], "features_to_knn": [47, 53, 54], "construct_knn_graph_from_index": [47, 53, 54, 56], "create_knn_graph_and_index": [47, 53, 54], "correct_knn_graph": [47, 53, 54, 97], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [47, 53, 54], "correct_knn_distances_and_indic": [47, 53, 54], "high_dimension_cutoff": [47, 53, 55], "row_count_cutoff": [47, 53, 55], "decide_euclidean_metr": [47, 53, 55], "decide_default_metr": [47, 53, 55], "construct_knn": [47, 53, 56], "transform_distances_to_scor": [47, 57], "correct_precision_error": [47, 57], "token_classification_util": [47, 110], "get_sent": [47, 58, 110], "filter_sent": [47, 58, 110], "process_token": [47, 58], "merge_prob": [47, 58], "color_sent": [47, 58], "assert_valid_input": [47, 60], "assert_valid_class_label": [47, 60], "assert_nonempty_input": [47, 60], "assert_indexing_work": [47, 60], "labels_to_arrai": [47, 60], "labels_to_list_multilabel": [47, 60], "min_allowed_prob": 48, "wikipedia": 48, "activ": [48, 50, 62, 63, 85, 103], "towardsdatasci": 48, "cheatsheet": 48, "ec57bc067c0b": 48, "clip": [48, 59, 90, 97], "behav": 48, "unnecessari": [48, 99], "slightli": [48, 88, 89], "interv": [48, 51, 106], "herein": 49, "inexact": 49, "cours": [49, 100], "propag": 49, "throughout": [49, 59, 75, 84, 90, 103, 109, 110], "increas": [49, 57, 70, 72, 73, 90, 91, 97, 99, 103, 104, 110], "dot": [49, 83, 99], "true_labels_class_count": 49, "pyx": 49, "multiannot": 50, "assert_valid_inputs_multiannot": 50, "labels_multiannot": [50, 63], "ensembl": [50, 51, 63, 73, 88, 95, 99, 104, 106, 108], "allow_single_label": 50, "annotator_id": 50, "assert_valid_pred_prob": 50, "pred_probs_unlabel": [50, 63], "format_multiannotator_label": [50, 63, 103], "formatted_label": [50, 59], "old": [50, 59, 86, 98], "check_consensus_label_class": 50, "consensus_label": [50, 63, 103], "consensus_method": [50, 63], "consensu": [50, 63, 85, 102, 110], "establish": [50, 62, 89, 108], "compute_soft_cross_entropi": 50, "soft": [50, 98], "find_best_temp_scal": 50, "coarse_search_rang": [50, 75, 99], "fine_search_s": [50, 75, 99], "temperatur": [50, 51, 70, 79, 83], "scale": [50, 57, 88, 97, 98, 99, 106, 109], "factor": [50, 51, 57, 77, 79], "minim": [50, 70, 106], "temp_scale_pred_prob": 50, "temp": 50, "sharpen": [50, 98], "smoothen": 50, "get_normalized_margin_for_each_label": [51, 73], "get_confidence_weighted_entropy_for_each_label": [51, 73], "scorer": 51, "alpha": [51, 67, 70, 91, 92, 97, 101, 104, 108], "exponenti": 51, "ema": 51, "s_1": 51, "s_k": 51, "ema_k": 51, "accord": [51, 65, 95, 96, 101, 110], "formula": [51, 57], "_t": 51, "cdot": 51, "s_t": 51, "qquad": 51, "leq": 51, "_1": 51, "recent": [51, 110], "success": 51, "previou": [51, 54, 93, 95, 99, 105], "discount": 51, "s_ema": 51, "175": [51, 93, 100, 101, 105], "underflow": 51, "nan": [51, 63, 88, 95, 97, 100, 103, 108], "aggregated_scor": 51, "base_scor": [51, 100], "base_scorer_kwarg": 51, "aggregator_kwarg": [51, 67], "n_sampl": [51, 97], "n_label": 51, "class_label_quality_scor": 51, "452": 51, "new_scor": 51, "575": [51, 100], "get_label_quality_scores_per_class": [51, 66, 67], "ml_scorer": 51, "binar": [51, 52], "reformat": [51, 90], "wider": 51, "splitter": 51, "kfold": [51, 93], "onevsrestclassifi": [51, 104], "randomforestclassifi": [51, 101, 104], "n_split": [51, 93, 104], "pred_prob_slic": 52, "onehot": 52, "hot": [52, 65, 71, 77, 80, 88, 95, 98, 99, 108, 109], "onehot_matrix": 52, "pairwis": [53, 55, 72], "reli": [54, 72, 89, 90, 91, 92, 96, 105, 106, 108], "sklearn_knn_kwarg": 54, "correction_featur": 54, "discourag": 54, "flexibl": [54, 99], "manner": [54, 67, 88, 89, 97, 103, 108], "701": 54, "900": [54, 88, 95, 108], "436": [54, 100], "000": [54, 89, 93, 96, 97, 98, 110], "idea": [54, 73, 100, 105], "dens": [54, 62, 97], "33140006": 54, "76210367": 54, "correct_exact_dupl": 54, "mutual": [54, 64, 104], "vari": [54, 70, 92], "exact_duplicate_set": 54, "main": [54, 63], "front": [54, 98], "consider": 54, "capabl": [54, 85, 100], "come": [54, 59, 91, 92, 99, 109], "misidentif": 54, "corrected_dist": 54, "corrected_indic": 54, "sqrt": 54, "distant": 54, "suitabl": [55, 63, 88, 95, 97, 100], "slower": 55, "decid": [55, 63, 89, 96, 98, 103, 108, 110], "predefin": 55, "met": [55, 110], "euclidean_dist": [55, 72], "spatial": [55, 72], "decis": [55, 88, 91, 92, 100], "That": [55, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "cosine_dist": 55, "knn_kwarg": 56, "html": [56, 59, 68, 71, 72, 90, 91, 92, 93, 95, 96, 99, 100, 101], "kneighbor": 56, "metric_param": 56, "n_features_in_": 56, "effective_metric_params_": 56, "effective_metric_": 56, "n_samples_fit_": 56, "__sklearn_is_fitted__": 56, "conduct": 56, "is_fit": 56, "trail": 56, "underscor": 56, "avg_dist": 57, "exp": [57, 72, 73, 91], "dt": 57, "right": [57, 68, 71, 89, 96, 104, 105, 106], "pronounc": 57, "differenti": 57, "ly": 57, "rule": [57, 58, 85, 98], "thumb": 57, "ood_features_scor": [57, 72, 106], "88988177": 57, "80519832": 57, "toler": 57, "minkowski": 57, "noth": 57, "epsilon": 57, "sensibl": 57, "fixed_scor": 57, "readabl": 58, "lambda": [58, 90, 91, 99, 100, 103], "long_sent": 58, "headlin": 58, "charact": [58, 59], "s1": 58, "s2": 58, "processed_token": 58, "alecnlcb": 58, "entiti": [58, 85, 99, 110], "mapped_ent": 58, "unique_ident": 58, "loc": [58, 91, 92, 93, 95, 97, 110], "nbitbas": [58, 67], "probs_merg": 58, "0125": [58, 83], "0375": 58, "075": 58, "025": 58, "color": [58, 80, 91, 92, 95, 97, 101, 104, 106, 108, 109], "red": [58, 71, 91, 92, 97, 98, 101, 104, 105, 106, 109], "colored_sent": 58, "termcolor": 58, "31msentenc": 58, "0m": 58, "ancillari": 59, "class_without_nois": 59, "any_other_class": 59, "choos": [59, 73, 88, 95, 99, 101, 108], "tradition": 59, "new_sum": 59, "fill": 59, "major": [59, 63, 86, 93, 106], "versu": [59, 101], "obviou": 59, "cgdeboer": 59, "iteround": 59, "reach": 59, "prob_s_eq_1": 59, "claesen": 59, "f1": [59, 71, 96, 101], "BE": 59, "left_nam": 59, "top_nam": 59, "titl": [59, 91, 92, 97, 101, 104, 106], "short_titl": 59, "round_plac": 59, "pretti": [59, 101], "joint_matrix": 59, "num_possible_valu": 59, "holdout_idx": 59, "extract": [59, 72, 89, 90, 95, 96, 100, 103, 106, 109], "allow_shuffl": 59, "turn": [59, 85, 105], "shuffledataset": 59, "histori": 59, "pre_x": 59, "buffer_s": 59, "csr_matric": 59, "append": [59, 90, 93, 98, 99, 100, 101, 103, 104, 105, 106, 110], "bottom": [59, 68, 71, 97, 105], "to_data": 59, "from_data": 59, "taken": 59, "label_matrix": 59, "canon": 59, "displai": [59, 71, 80, 84, 89, 90, 95, 96, 97, 101, 110], "jupyt": [59, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "notebook": [59, 63, 90, 92, 98, 99, 100, 101, 103, 104, 105, 107, 109, 110], "consol": 59, "allow_missing_class": 60, "allow_one_class": 60, "length_x": 60, "labellik": 60, "labels_list": [60, 65], "keraswrappermodel": [61, 62, 85], "keraswrappersequenti": [61, 62], "tf": [62, 90], "legaci": 62, "newer": 62, "interim": 62, "advis": [62, 104], "stabil": [62, 72], "until": 62, "accommod": 62, "keraswrapp": 62, "huggingface_keras_imdb": 62, "unit": [62, 110], "model_kwarg": [62, 75], "compile_kwarg": 62, "sparsecategoricalcrossentropi": 62, "layer": [62, 89, 90, 96, 106], "my_keras_model": 62, "from_logit": 62, "declar": 62, "apply_softmax": 62, "analysi": 63, "analyz": [63, 85, 97, 101, 103, 104], "get_label_quality_multiannot": [63, 103], "vote": 63, "crowdsourc": [63, 85, 103], "dawid": [63, 103], "skene": [63, 103], "analog": [63, 98, 103], "chosen": [63, 73, 99, 103], "crowdlab": [63, 103], "unlabel": [63, 93, 103, 106, 109], "get_active_learning_scor": [63, 103], "activelab": [63, 103], "priorit": [63, 70, 105, 109, 110], "showcas": 63, "best_qual": 63, "quality_method": 63, "calibrate_prob": 63, "return_detailed_qu": 63, "return_annotator_stat": 63, "return_weight": 63, "label_quality_score_kwarg": 63, "did": [63, 64, 88, 89, 90, 95, 101, 103, 108], "majority_vot": 63, "broken": [63, 71, 98, 108], "highest": [63, 71, 91, 93, 100, 107], "0th": 63, "consensus_quality_scor": [63, 103], "annotator_agr": [63, 103], "reman": 63, "1st": 63, "2nd": [63, 77], "3rd": 63, "consensus_label_suffix": 63, "consensus_quality_score_suffix": 63, "suffix": 63, "emsembl": 63, "weigh": [63, 98], "agreement": [63, 103], "agre": 63, "prevent": [63, 99], "overconfid": [63, 107], "detailed_label_qu": [63, 103], "annotator_stat": [63, 103], "model_weight": 63, "annotator_weight": 63, "warn": 63, "labels_info": 63, "num_annot": [63, 103], "deriv": [63, 103], "quality_annotator_1": 63, "quality_annotator_2": 63, "quality_annotator_m": 63, "annotator_qu": [63, 103], "num_examples_label": [63, 103], "agreement_with_consensu": [63, 103], "worst_class": [63, 103], "trustworthi": [63, 103, 108], "get_label_quality_multiannotator_ensembl": 63, "weigtht": 63, "budget": 63, "retrain": [63, 89, 108], "active_learning_scor": 63, "active_learning_scores_unlabel": 63, "get_active_learning_scores_ensembl": 63, "henc": [63, 90, 91, 100, 103], "get_majority_vote_label": [63, 103], "event": 63, "lastli": [63, 95], "convert_long_to_wide_dataset": 63, "labels_multiannotator_long": 63, "wide": [63, 88, 89, 90], "labels_multiannotator_wid": 63, "common_multilabel_issu": [64, 66], "exclus": [64, 104], "rank_classes_by_multilabel_qu": [64, 66], "overall_multilabel_health_scor": [64, 66], "multilabel_health_summari": [64, 66], "classes_by_multilabel_qu": 64, "inner": [65, 79, 97], "find_multilabel_issues_per_class": [65, 66], "per_class_label_issu": 65, "label_issues_list": 65, "pred_probs_list": [65, 73, 93, 101], "anim": [66, 106], "rat": 66, "predat": 66, "pet": 66, "reptil": 66, "box": [68, 70, 71, 98, 105], "object_detect": [68, 70, 71, 105], "return_indices_ranked_by_scor": [68, 105], "overlapping_label_check": [68, 70], "suboptim": [68, 70], "locat": [68, 70, 97, 105, 109, 110], "bbox": [68, 71, 105], "image_nam": [68, 71], "y1": [68, 71, 105], "y2": [68, 71, 105], "later": [68, 71, 72, 89, 100, 110], "corner": [68, 71, 105], "xyxi": [68, 71, 105], "io": [68, 71, 90, 97, 98], "keras_cv": [68, 71], "bounding_box": [68, 71, 105], "detectron": [68, 71, 105], "detectron2": [68, 71, 105], "readthedoc": [68, 71], "en": [68, 71], "latest": [68, 71], "draw_box": [68, 71], "mmdetect": [68, 71, 105], "swap": [68, 70, 80, 84], "penal": [68, 70], "concern": [68, 70, 85, 92], "issues_from_scor": [69, 70, 78, 79, 80, 82, 83, 84, 105, 109, 110], "compute_overlooked_box_scor": [69, 70], "compute_badloc_box_scor": [69, 70], "compute_swap_box_scor": [69, 70], "pool_box_scores_per_imag": [69, 70], "object_counts_per_imag": [69, 71, 105], "bounding_box_size_distribut": [69, 71, 105], "class_label_distribut": [69, 71, 105], "get_sorted_bbox_count_idx": [69, 71], "plot_class_size_distribut": [69, 71], "plot_class_distribut": [69, 71], "get_average_per_class_confusion_matrix": [69, 71], "calculate_per_class_metr": [69, 71], "aggregation_weight": 70, "imperfect": [70, 99, 100], "chose": [70, 103, 105], "imperfectli": [70, 105], "dirti": [70, 73, 76, 108], "subtyp": 70, "badloc": 70, "nonneg": 70, "high_probability_threshold": 70, "auxiliary_input": [70, 71], "iou": [70, 71], "heavili": 70, "auxiliarytypesdict": 70, "pred_label": [70, 89], "pred_label_prob": 70, "pred_bbox": 70, "lab_label": 70, "lab_bbox": 70, "similarity_matrix": 70, "min_possible_similar": 70, "scores_overlook": 70, "low_probability_threshold": 70, "scores_badloc": 70, "accident": [70, 89, 95, 96, 99], "scores_swap": 70, "box_scor": 70, "image_scor": [70, 79, 109], "discov": [71, 92, 97, 110], "abnorm": [71, 93, 105], "auxiliari": [71, 106, 109], "_get_valid_inputs_for_compute_scor": 71, "object_count": 71, "down": 71, "bbox_siz": 71, "class_distribut": 71, "plot": [71, 91, 92, 97, 101, 104, 106, 108, 109], "sorted_idx": [71, 106], "class_to_show": 71, "hidden": [71, 106], "max_class_to_show": 71, "plt": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "matplotlib": [71, 80, 91, 92, 93, 97, 101, 104, 105, 106, 108], "pyplot": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "prediction_threshold": 71, "overlai": [71, 105], "figsiz": [71, 91, 92, 93, 97, 101, 104, 106], "save_path": [71, 105], "blue": [71, 98, 101, 105], "overlaid": 71, "side": [71, 98, 105], "figur": [71, 97, 101, 104, 106, 108], "extens": [71, 101, 103], "png": [71, 105], "pdf": [71, 72], "svg": 71, "num_proc": [71, 93], "intersect": [71, 99], "tp": 71, "fp": 71, "ground": [71, 98, 101, 103, 108], "truth": [71, 101, 103, 108], "bias": [71, 97], "avg_metr": 71, "distionari": 71, "95": [71, 81, 83, 95, 98, 100, 101, 108], "per_class_metr": 71, "Of": 72, "find_top_issu": [72, 73, 106], "behind": [72, 101], "dist_metr": 72, "subtract": [72, 73], "renorm": [72, 73, 99], "least_confid": 72, "sum_": 72, "log": [72, 73, 86], "softmax": [72, 79, 83, 93], "literatur": 72, "gen": 72, "liu": 72, "lochman": 72, "zach": 72, "openaccess": 72, "thecvf": 72, "cvpr2023": 72, "liu_gen_pushing_the_limits_of_softmax": 72, "based_out": 72, "distribution_detection_cvpr_2023_pap": 72, "fit_scor": [72, 106], "ood_predictions_scor": 72, "pretrain": [72, 89, 90, 96, 100, 106], "adjust_confident_threshold": 72, "probabilist": [72, 88, 90, 91, 92, 95, 96, 106, 107], "order_label_issu": [73, 86], "whichev": [73, 107], "argsort": [73, 89, 93, 96, 101, 105, 106, 108], "max_": 73, "get_label_quality_ensemble_scor": [73, 99, 101], "weight_ensemble_members_bi": 73, "custom_weight": 73, "log_loss_search_t_valu": 73, "0001": [73, 98], "scheme": 73, "log_loss_search": 73, "log_loss": [73, 96], "1e0": 73, "1e1": 73, "1e2": 73, "2e2": 73, "quality_scor": [73, 106], "forth": 73, "top_issue_indic": 73, "rank_bi": [73, 86], "weird": [73, 84], "prob_label": 73, "max_prob_not_label": 73, "AND": [73, 96], "get_epistemic_uncertainti": [74, 75], "get_aleatoric_uncertainti": [74, 75], "corrupt": [75, 108], "linearregress": [75, 99, 108], "y_with_nois": 75, "n_boot": [75, 99], "include_aleatoric_uncertainti": [75, 99], "bootstrap": [75, 99, 108], "resampl": [75, 90, 99], "epistem": [75, 99, 106, 108], "aleator": [75, 99, 108], "model_final_kwarg": 75, "coars": 75, "thorough": [75, 99], "fine": [75, 89, 90, 96, 106], "grain": 75, "grid": [75, 100], "varianc": [75, 101], "epistemic_uncertainti": 75, "residu": [75, 76, 99], "deviat": [75, 105, 108], "aleatoric_uncertainti": 75, "outr": 76, "contin": 76, "raw": [76, 85, 86, 92, 93, 98, 99, 100, 103, 105, 106, 108], "aka": [76, 90, 101, 105, 108, 110], "00323821": 76, "33692597": 76, "00191686": 76, "semant": [77, 79, 80, 102], "pixel": [77, 79, 80, 93, 106, 109], "h": [77, 79, 80, 109], "height": [77, 79, 80, 109], "w": [77, 79, 80, 109], "width": [77, 79, 80, 109], "labels_one_hot": [77, 80, 109], "stream": [77, 106, 110], "downsampl": [77, 79, 109], "shrink": [77, 79], "divis": [77, 79, 91], "common_label_issu": [78, 80, 82, 84, 109, 110], "filter_by_class": [78, 80, 109], "segmant": [79, 80], "num_pixel_issu": [79, 109], "product": [79, 93, 97, 99, 100], "pixel_scor": [79, 109], "enter": 80, "legend": [80, 91, 92, 97, 104, 105, 108, 109], "colormap": 80, "background": [80, 97], "person": [80, 99, 105, 109, 110], "ambigu": [80, 84, 89, 90, 96, 98, 101, 110], "misunderstood": [80, 84], "issues_df": [80, 93], "class_index": 80, "issues_subset": [80, 84], "filter_by_token": [82, 84, 110], "token_score_method": 83, "sentence_score_method": 83, "sentence_score_kwarg": 83, "compris": [83, 84], "token_scor": [83, 110], "converg": 83, "toward": [83, 97], "_softmin_sentence_scor": 83, "sentence_scor": [83, 110], "token_info": 83, "02": [83, 91, 92, 97, 101, 105], "03": [83, 95, 97, 98, 100, 101, 105, 106, 110], "04": [83, 95, 97, 105], "08": [83, 97, 101, 105, 108, 110], "commonli": [84, 86, 91, 92, 104, 110], "But": [84, 96, 100, 101, 108, 110], "restrict": [84, 99], "reliabl": [85, 88, 90, 97, 99, 100, 103, 109], "thousand": 85, "imagenet": [85, 98], "popular": [85, 103, 105], "centric": [85, 93, 102], "minut": [85, 88, 89, 90, 95, 96, 98, 103, 104, 105, 108, 109, 110], "conda": 85, "feature_embed": [85, 106], "your_dataset": [85, 90, 91, 92, 93, 95, 96, 99], "column_name_of_label": [85, 90, 91, 92, 93, 95, 96], "tool": [85, 98, 101, 103], "catch": [85, 100], "dive": [85, 96, 97, 100], "plagu": [85, 92], "untrain": 85, "\u30c4": 85, "label_issues_info": [85, 92], "sklearn_compatible_model": 85, "framework": [85, 104, 105], "complianc": 85, "tag": [85, 104, 110], "sequenc": 85, "recognit": [85, 90, 99, 110], "train_data": [85, 88, 89, 106, 108], "gotten": 85, "test_data": [85, 88, 89, 101, 104, 106, 108], "deal": [85, 92, 97, 100], "feel": [85, 90, 92, 99], "ask": [85, 99], "slack": [85, 99], "project": [85, 100, 108], "welcom": 85, "commun": [85, 99], "guidelin": [85, 105], "piec": 85, "smart": [85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 108], "edit": [85, 99, 100], "unreli": [85, 88, 90, 95, 96, 97, 100], "link": [85, 90, 98, 105], "older": 86, "outlin": 86, "substitut": [86, 100], "v2": [86, 88, 95], "get_noise_indic": 86, "psx": 86, "sorted_index_method": 86, "order_label_error": 86, "label_errors_bool": 86, "latent_estim": 86, "num_label_error": 86, "learningwithnoisylabel": 86, "neatli": 86, "organ": [86, 88, 95, 97, 98, 110], "reorgan": 86, "baseline_method": 86, "research": [86, 101], "polyplex": 86, "terminologi": 86, "label_error": 86, "quickstart": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 103, 104, 105, 106, 108, 109, 110], "sql": [88, 95], "databas": [88, 95], "excel": [88, 95], "parquet": [88, 95], "student": [88, 95, 100, 108, 110], "grade": [88, 95, 100, 108], "exam": [88, 95, 100, 108], "letter": [88, 95, 110], "hundr": [88, 95], "mistak": [88, 89, 93, 95, 96, 100], "extratreesclassifi": 88, "extratre": 88, "Then": [88, 89, 93, 99], "ranked_label_issu": [88, 89], "branch": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "standardscal": [88, 95, 100, 106], "labelencod": [88, 89, 100], "train_test_split": [88, 89, 91, 92, 106], "accuracy_scor": [88, 89, 90, 96, 100, 101], "grades_data": [88, 95], "read_csv": [88, 89, 95, 96, 97, 100, 108], "demo": [88, 92, 95, 104], "stud_id": [88, 95, 100], "exam_1": [88, 95, 100, 108], "exam_2": [88, 95, 100, 108], "exam_3": [88, 95, 100, 108], "letter_grad": [88, 95], "f48f73": [88, 95], "53": [88, 91, 92, 95, 97, 98, 100, 104, 105], "00": [88, 91, 92, 95, 97, 98, 100, 106], "77": [88, 91, 92, 95, 100, 105], "0bd4e7": [88, 95], "81": [88, 95, 96, 100, 105, 108, 110], "great": [88, 95, 98, 100], "particip": [88, 95, 100], "cb9d7a": [88, 95], "61": [88, 95, 97, 101, 105, 108], "94": [88, 95, 98, 100, 101, 105, 108], "9acca4": [88, 95], "48": [88, 95, 97, 98, 101, 105], "x_raw": [88, 95], "labels_raw": 88, "interg": [88, 89], "categorical_featur": [88, 108], "x_encod": [88, 95], "get_dummi": [88, 95, 108], "drop_first": [88, 95], "numeric_featur": [88, 95], "scaler": [88, 95, 106], "x_process": [88, 95], "fit_transform": [88, 95, 97, 100], "bring": [88, 89, 93, 95, 96, 103, 108], "byod": [88, 89, 93, 95, 96, 103, 108], "tress": 88, "held": [88, 90, 95, 96, 98, 105, 106, 107], "straightforward": [88, 90, 95], "benefit": [88, 90, 107, 109], "num_crossval_fold": [88, 90, 95, 100, 103], "tabl": [88, 95, 98, 103], "212": [88, 100, 101], "iloc": [88, 89, 90, 95, 96, 100, 108], "92": [88, 91, 100, 101, 105], "93": [88, 98, 100, 105, 108, 110], "827": 88, "99": [88, 97, 98, 100, 101], "86": [88, 92, 93, 95, 100, 101, 105, 108], "74": [88, 97, 100, 105, 108], "637": [88, 95], "79": [88, 98, 100, 105], "65": [88, 91, 97, 100, 105], "cheat": [88, 100], "0pt": [88, 100], "120": [88, 91, 92, 100], "233": 88, "83": [88, 100, 101, 105, 108, 110], "76": [88, 100, 101, 104, 105, 108], "suspici": [88, 95], "carefulli": [88, 93, 95, 96, 100], "examin": [88, 91, 92, 95, 97, 100, 105], "labels_train": 88, "labels_test": 88, "test_siz": [88, 89, 91, 92], "acc_og": [88, 89], "783068783068783": 88, "robustli": [88, 89, 108], "14": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "acc_cl": [88, 89], "8095238095238095": 88, "blindli": [88, 89, 90, 99, 100, 108], "trust": [88, 89, 90, 99, 100, 101, 103, 107, 108], "effort": [88, 89, 100, 108], "cumbersom": [88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "intent": [89, 96], "servic": [89, 96, 99], "onlin": [89, 96], "bank": [89, 96, 98], "banking77": [89, 96], "oo": [89, 96], "categori": [89, 93, 96, 97, 100], "shortlist": [89, 96, 108], "scope": [89, 96], "logist": [89, 91, 92, 96, 103, 106], "probabilit": [89, 90], "drop": [89, 95, 97, 99, 100, 103, 108], "sentence_transform": [89, 96], "sentencetransform": [89, 96], "payment": [89, 96], "cancel_transf": [89, 96], "transfer": [89, 96], "fund": [89, 96], "cancel": [89, 96], "transact": [89, 96], "my": [89, 96], "revert": [89, 96], "morn": [89, 96], "realis": [89, 96], "yesterdai": [89, 96], "rent": [89, 96], "tomorrow": [89, 96], "raw_text": [89, 96], "raw_label": 89, "raw_train_text": 89, "raw_test_text": 89, "raw_train_label": 89, "raw_test_label": 89, "getting_spare_card": [89, 96], "card_payment_fee_charg": [89, 96], "supported_cards_and_curr": [89, 96], "beneficiary_not_allow": [89, 96], "apple_pay_or_google_pai": [89, 96], "change_pin": [89, 96], "visa_or_mastercard": [89, 96], "lost_or_stolen_phon": [89, 96], "card_about_to_expir": [89, 96], "card": [89, 96, 98], "utter": [89, 96], "encond": 89, "test_label": [89, 100, 101, 104, 106], "suit": [89, 96, 97, 98, 99], "electra": [89, 96], "discrimin": [89, 96], "googl": [89, 96], "train_text": 89, "test_text": 89, "home": [89, 96, 98], "runner": [89, 96], "google_electra": [89, 96], "pool": [89, 96, 99, 106], "leverag": [89, 90, 96, 99, 101, 103], "computation": [89, 90, 96], "intens": [89, 90, 96], "400": [89, 96, 100], "858371": 89, "547274": 89, "826228": 89, "966008": 89, "792449": 89, "identified_issu": [89, 108], "lowest_quality_label": [89, 90, 96, 101, 108], "to_numpi": [89, 96, 97, 100, 108], "44": [89, 97, 98, 104, 105], "646": 89, "390": 89, "628": 89, "121": [89, 101], "702": 89, "863": 89, "135": 89, "337": [89, 100, 105], "735": 89, "print_as_df": 89, "inverse_transform": 89, "charg": [89, 96], "cash": [89, 96], "holidai": [89, 96], "sent": [89, 96, 97, 110], "mine": [89, 96], "expir": [89, 96], "fight": 89, "hors": [89, 98, 106], "duck": [89, 98], "me": [89, 96, 97], "whoever": [89, 96], "consum": [89, 108], "18": [89, 90, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109], "baseline_model": [89, 108], "87": [89, 92, 93, 100, 105, 108], "acceler": [89, 108], "19": [89, 90, 93, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109], "89": [89, 91, 95, 100, 105, 108], "spoken": 90, "500": [90, 97, 100, 106, 110], "english": [90, 98], "pronunci": 90, "wav": 90, "voxceleb": 90, "speech": [90, 110], "your_pred_prob": [90, 91, 92, 95, 96], "tensorflow_io": 90, "huggingface_hub": 90, "reproduc": [90, 95, 97, 100, 101, 103], "command": 90, "wget": [90, 97, 105, 109, 110], "navig": 90, "browser": 90, "jakobovski": 90, "archiv": [90, 110], "v1": 90, "tar": [90, 106], "gz": [90, 106], "mkdir": [90, 110], "spoken_digit": 90, "xf": 90, "6_nicolas_32": 90, "data_path": 90, "listdir": 90, "nondeterminist": 90, "file_nam": 90, "endswith": 90, "file_path": 90, "join": [90, 93, 97, 99, 100], "7_george_26": 90, "0_nicolas_24": 90, "0_nicolas_6": 90, "listen": 90, "display_exampl": 90, "expand": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "pulldown": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "colab": [90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "tfio": 90, "pathlib": 90, "ipython": [90, 97], "load_wav_16k_mono": 90, "filenam": 90, "khz": 90, "file_cont": 90, "read_fil": 90, "sample_r": 90, "decode_wav": 90, "desired_channel": 90, "squeez": 90, "rate_in": 90, "rate_out": 90, "16000": 90, "wav_file_nam": 90, "audio_r": 90, "wav_file_exampl": 90, "plai": [90, 98, 99], "button": 90, "wav_file_name_exampl": 90, "7_jackson_43": 90, "hear": 90, "extractor": 90, "encoderclassifi": 90, "spkrec": 90, "xvect": 90, "feature_extractor": 90, "from_hparam": 90, "run_opt": 90, "uncom": [90, 97], "ffmpeg": 90, "backend": 90, "wav_audio_file_path": 90, "torchaudio": 90, "extract_audio_embed": 90, "emb": [90, 93], "signal": 90, "encode_batch": 90, "embeddings_list": [90, 93], "embeddings_arrai": 90, "512": [90, 93], "196311": 90, "319459": 90, "478975": 90, "2890875": 90, "8170238": 90, "89265": 90, "898056": 90, "256195": 90, "559641": 90, "559721": 90, "62067": 90, "285245": 90, "21": [90, 91, 97, 98, 100, 101, 105, 108, 110], "709627": 90, "5033693": 90, "913803": 90, "819831": 90, "1831515": 90, "208763": 90, "084257": 90, "3210397": 90, "005453": 90, "216152": 90, "478235": 90, "6821785": 90, "053807": 90, "242471": 90, "091424": 90, "78334856": 90, "03954": 90, "23": [90, 93, 97, 98, 100, 101, 105, 108], "569176": 90, "761097": 90, "1258295": 90, "753237": 90, "3508866": 90, "598274": 90, "23712": 90, "2500": 90, "tol": 90, "decreas": [90, 99], "cv_accuraci": 90, "9708": 90, "issue_type_descript": [90, 91, 92, 93, 95, 96, 100, 101], "lt": [90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 106], "gt": [90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 110], "9976": 90, "986": 90, "002161": 90, "176": [90, 98, 101, 104], "002483": 90, "2318": 90, "004411": 90, "1005": 90, "004857": 90, "1871": 90, "007494": 90, "040587": 90, "999207": 90, "999377": 90, "975220": 90, "999367": 90, "identified_label_issu": [90, 96], "516": [90, 100], "1946": 90, "469": 90, "2132": 90, "worth": [90, 101], "6_yweweler_25": 90, "7_nicolas_43": 90, "6_theo_27": 90, "6_yweweler_36": 90, "6_yweweler_14": 90, "6_yweweler_35": 90, "6_nicolas_8": 90, "sound": 90, "quit": [90, 106], "underneath": 91, "hood": [91, 97, 99], "alert": 91, "introduct": 91, "mayb": [91, 92, 96], "your_feature_matrix": [91, 92], "toi": [91, 92, 93, 97, 98, 101, 103, 107], "inf": [91, 92], "mid": [91, 92], "bins_map": [91, 92], "create_data": [91, 92], "y_bin": [91, 92], "y_i": [91, 92], "y_bin_idx": [91, 92], "y_train": [91, 92, 101, 108], "y_test": [91, 92, 101, 108], "y_train_idx": [91, 92], "y_test_idx": [91, 92], "slide": [91, 92, 98], "frame": [91, 92], "x_out": [91, 92], "tini": [91, 92], "concaten": [91, 92, 107], "y_out": [91, 92], "y_out_bin": [91, 92], "y_out_bin_idx": [91, 92], "exact_duplicate_idx": [91, 92], "x_duplic": [91, 92], "y_duplic": [91, 92], "y_duplicate_idx": [91, 92], "noisy_labels_idx": [91, 92, 104], "scatter": [91, 92, 97, 101, 104, 108], "black": [91, 92, 98, 108], "cyan": [91, 92], "plot_data": [91, 92, 97, 101, 104, 108], "fig": [91, 92, 93, 98, 106, 108], "ax": [91, 92, 93, 97, 106, 108], "subplot": [91, 92, 93, 106], "set_titl": [91, 92, 93, 106], "set_xlabel": [91, 92], "x_1": [91, 92], "fontsiz": [91, 92, 93, 97, 101, 104], "set_ylabel": [91, 92], "x_2": [91, 92], "set_xlim": [91, 92], "set_ylim": [91, 92], "linestyl": [91, 92, 97], "circl": [91, 92, 101, 104], "misclassifi": [91, 92], "zip": [91, 92, 93, 97, 105, 110], "label_err": [91, 92], "180": [91, 92, 97, 105], "marker": [91, 92], "facecolor": [91, 92, 97], "edgecolor": [91, 92, 97], "linewidth": [91, 92, 97, 106], "dup": [91, 92], "first_legend": [91, 92], "align": [91, 92], "title_fontproperti": [91, 92], "semibold": [91, 92], "second_legend": [91, 92], "45": [91, 92, 97, 98, 100, 101, 105], "gca": [91, 92], "add_artist": [91, 92], "tight_layout": [91, 92, 97], "ideal": [91, 92], "remaind": 91, "modal": [91, 92, 99, 100, 103], "132": [91, 92, 100, 101, 105], "9318": 91, "006940": 91, "007830": 91, "40": [91, 92, 96, 97, 98, 100], "014828": 91, "107": [91, 92, 97, 101, 104], "021241": 91, "026407": 91, "notic": [91, 101, 103, 105], "3558": [91, 92], "126": [91, 92, 101, 105], "006636": [91, 92], "130": [91, 92], "012571": [91, 92], "129": [91, 92], "127": [91, 92, 100], "014909": [91, 92], "128": [91, 92, 93], "017443": [91, 92], "6160": [91, 92], "131": [91, 92, 100, 109], "000000e": [91, 92, 100], "000002": [91, 92], "463180e": [91, 92], "07": [91, 92, 93, 95, 97, 101, 105, 108], "51": [91, 92, 95, 97, 98, 101, 105], "161148": [91, 92], "859087e": [91, 92], "30": [91, 92, 93, 97, 98, 99, 100, 104, 109, 110], "3453": 91, "029542": 91, "031182": 91, "057961": 91, "058244": 91, "54": [91, 97, 98, 101, 105], "039122": 91, "044598": 91, "105": [91, 105], "105196": 91, "133654": 91, "43": [91, 97, 98, 100, 101, 105], "168033": 91, "125": 91, "101107": 91, "183382": 91, "109": [91, 97, 98, 100, 105], "209259": 91, "211042": 91, "221316": 91, "average_ood_scor": 91, "34530442089193386": 91, "52": [91, 97, 98, 100, 105, 110], "169820": 91, "087324e": 91, "259024": 91, "583757e": 91, "91": [91, 100, 105], "346458": 91, "341292e": 91, "specfi": 91, "new_lab": 91, "scoring_funct": 91, "div": 91, "rem": 91, "inv_scal": 91, "49": [91, 97, 98, 101, 105], "superstitionissuemanag": 91, "unlucki": 91, "superstit": 91, "to_seri": 91, "issues_mask": 91, "summary_scor": 91, "9242": 91, "is_superstition_issu": 91, "superstition_scor": 91, "26": [91, 93, 97, 98, 100, 101, 103, 105, 110], "047581": 91, "090635": 91, "129591": 91, "164840": 91, "lurk": [92, 93, 100, 101], "thoroughli": 92, "8561": 92, "001908": 92, "003564": 92, "007331": 92, "008963": 92, "009664": 92, "0227": 92, "022727": 92, "conceptu": 92, "856061": 92, "355772": 92, "616034": 92, "821750": 92, "926818": 92, "betweeen": 92, "859131": 92, "417707": 92, "664083": 92, "970324": 92, "816953": 92, "375317": 92, "641516": 92, "890575": 92, "910232": 92, "531021": 92, "460593": 92, "601188": 92, "826147": 92, "752808": 92, "321635": 92, "562539": 92, "948362": 92, "890169": 92, "090243": 92, "472909": 92, "746763": 92, "878267": 92, "examples_w_issu": [92, 99], "013445": 92, "025184": 92, "026376": 92, "inde": [92, 96], "miscellan": [92, 94, 110], "428571": 92, "111111": 92, "571429": 92, "407407": 92, "592593": 92, "337838": 92, "092593": 92, "662162": 92, "333333": [92, 98], "952381": 92, "666667": [92, 97], "portion": 92, "huge": [92, 101], "worri": [92, 96, 100], "critic": [92, 107], "60": [93, 97, 101, 108], "torchvis": [93, 97, 106], "tensordataset": 93, "stratifiedkfold": [93, 104], "tqdm": 93, "autonotebook": 93, "math": [93, 100], "fashion_mnist": 93, "num_row": 93, "60000": 93, "transformed_dataset": 93, "with_format": 93, "255": [93, 98], "cpu_count": 93, "torch_dataset": 93, "quick": [93, 104, 106], "super": 93, "relu": 93, "batchnorm2d": 93, "maxpool2d": 93, "lazylinear": 93, "flatten": 93, "get_test_accuraci": 93, "testload": [93, 106], "energi": 93, "trainload": [93, 106], "n_epoch": 93, "patienc": 93, "criterion": 93, "crossentropyloss": 93, "adamw": 93, "best_test_accuraci": 93, "start_epoch": 93, "running_loss": 93, "best_epoch": 93, "end_epoch": 93, "3f": [93, 108], "acc": [93, 101], "time_taken": 93, "compute_embed": 93, "compute_pred_prob": 93, "train_batch_s": 93, "num_work": 93, "worker": [93, 110], "train_id_list": 93, "test_id_list": 93, "train_id": 93, "test_id": 93, "embeddings_model": 93, "ntrain": 93, "trainset": 93, "testset": 93, "pin_memori": 93, "fold_embed": 93, "fold_pred_prob": 93, "finish": 93, "482": 93, "720": 93, "965": [93, 98], "329": [93, 95, 100, 105], "88": [93, 98, 100, 101, 104, 105, 108], "195": [93, 97, 100], "763": 93, "493": 93, "060": 93, "062": 93, "330": [93, 100, 105], "505": 93, "901": 93, "476": [93, 100], "340": [93, 100], "009": [93, 97], "328": [93, 105], "310": 93, "800": 93, "reorder": 93, "hstack": [93, 99, 101, 103], "max_preval": [93, 97], "7714": 93, "3772": 93, "3585": 93, "166": 93, "3651": 93, "27080": 93, "873833e": 93, "40378": 93, "915575e": 93, "25316": 93, "390277e": 93, "06": [93, 100, 101, 105, 110], "2090": 93, "751164e": 93, "14999": 93, "881301e": 93, "9569": 93, "11262": 93, "000003": 93, "coat": [93, 98], "shirt": [93, 98], "19228": 93, "000010": 93, "dress": 93, "32657": 93, "000013": 93, "bag": [93, 98, 106, 107], "21282": 93, "000016": [93, 100], "53564": 93, "000018": [93, 100], "pullov": 93, "6321": 93, "30968": 93, "001267": 93, "30659": 93, "000022": [93, 110], "47824": 93, "001454": 93, "3370": 93, "000026": 93, "54565": 93, "001854": 93, "9762": 93, "258": 93, "47139": 93, "000033": 93, "166980": 93, "986195": 93, "997205": 93, "sandal": [93, 98], "948781": 93, "999358": 93, "54078": 93, "17371": 93, "000025": 93, "plot_label_issue_exampl": 93, "nrow": [93, 106], "ceil": [93, 100], "axes_list": 93, "label_issue_indic": 93, "gl": 93, "sl": 93, "fontdict": 93, "imshow": [93, 106], "cmap": [93, 97, 108], "grai": 93, "subplots_adjust": 93, "hspace": 93, "outsiz": 93, "outlier_issu": [93, 96], "outlier_issues_df": 93, "depict": [93, 104, 105, 106, 107, 109], "plot_outlier_issues_exampl": 93, "n_comparison_imag": 93, "sample_from_class": 93, "number_of_sampl": 93, "non_outlier_indic": 93, "isnul": [93, 97], "non_outlier_indices_excluding_curr": 93, "sampled_indic": 93, "label_scores_of_sampl": 93, "top_score_indic": 93, "top_label_indic": 93, "sampled_imag": 93, "get_image_given_label_and_sampl": 93, "image_from_dataset": 93, "corresponding_label": 93, "comparison_imag": 93, "images_to_plot": 93, "idlist": 93, "iterrow": 93, "near_duplicate_issu": [93, 99], "closest": 93, "counterpart": 93, "near_duplicate_issues_df": 93, "plot_near_duplicate_issue_exampl": 93, "seen_id_pair": 93, "get_image_and_given_label_and_predicted_label": 93, "duplicate_imag": 93, "nd_set": 93, "challeng": 93, "dark_issu": 93, "reveal": [93, 105, 109], "dark_issues_df": 93, "is_dark_issu": [93, 97], "34848": 93, "203922": 93, "50270": 93, "204588": 93, "3936": 93, "213098": 93, "733": 93, "217686": 93, "8094": 93, "230118": 93, "plot_image_issue_exampl": 93, "difficult": 93, "disproportion": [93, 97], "lowinfo_issu": 93, "lowinfo_issues_df": 93, "is_low_information_issu": 93, "53050": 93, "067975": 93, "40875": 93, "089929": 93, "9594": 93, "092601": 93, "34825": 93, "107744": 93, "37530": 93, "108516": 93, "lot": 93, "workflow": [94, 99, 100, 102, 108], "histgradientboostingclassifi": 95, "cat_featur": 95, "boost": [95, 99, 103, 108], "xgboost": [95, 99, 100, 108], "think": [95, 96, 99, 104, 109, 110], "nonzero": 95, "358": 95, "941": 95, "294": [95, 105], "46": [95, 97, 98, 100, 101, 105], "7109": 95, "000005": [95, 96], "886": 95, "000059": 95, "709": [95, 100], "000104": 95, "000169": 95, "689": 95, "000181": 95, "3590": 95, "051882e": 95, "683133e": 95, "536582e": 95, "406589e": 95, "324246e": 95, "6165": 95, "582": [95, 100], "185": [95, 97, 98, 105, 110], "187": [95, 98, 100], "898": 95, "0000": [95, 96, 98, 100, 101], "865": 95, "515002": 95, "837": 95, "556480": 95, "622": 95, "593068": 95, "593207": 95, "920": 95, "618041": 95, "4386345844794593e": 95, "issue_result": 95, "000842": 95, "555944": 95, "004374": 95, "sorted_issu": 95, "73": [95, 97, 98, 100, 104, 105, 108], "deserv": 95, "outlier_result": 95, "sorted_outli": 95, "56": [95, 97, 98, 108], "96": [95, 97, 98, 100, 101, 104, 105, 108], "style": [95, 97, 109], "font": 95, "18px": 95, "ff00ff": 95, "bac": 95, "duplicate_result": 95, "lowest_scoring_dupl": 95, "idxmin": [95, 99], "indices_to_displai": 95, "tolist": [95, 99, 100, 104], "perhap": [95, 101, 103], "second_lowest_scoring_dupl": 95, "next_indices_to_displai": 95, "wari": [95, 96, 99], "your_featur": 96, "text_embed": 96, "data_dict": [96, 101, 103], "85": [96, 100, 105], "38": [96, 97, 98, 105], "9710": 96, "981": 96, "974": 96, "000146": 96, "982": [96, 98], "000224": 96, "971": 96, "000507": 96, "980": [96, 98], "000960": 96, "3584": 96, "994": 96, "009642": 96, "999": 96, "013067": 96, "013841": 96, "433": 96, "014722": 96, "989": 96, "018224": 96, "6070": 96, "160": [96, 108], "095724": 96, "148": 96, "006237": 96, "546": [96, 100], "099341": 96, "514": 96, "006485": 96, "481": 96, "123418": 96, "008165": 96, "313": [96, 100, 105], "564102": 96, "572258": 96, "574915": 96, "31": [96, 97, 98, 100, 101, 103, 105], "575507": 96, "575874": 96, "792090": 96, "257611": 96, "698710": 96, "182121": 96, "771619": 96, "data_with_suggested_label": 96, "suggested_label": 96, "withdraw": 96, "monei": 96, "lowest_quality_outli": 96, "OR": 96, "636c65616e6c616220697320617765736f6d6521": 96, "phone": [96, 98], "gone": 96, "samp": 96, "br": 96, "press": [96, 110], "nonsens": 96, "sens": 96, "detriment": 96, "duplicate_issu": 96, "fee": 96, "go": [96, 97, 98, 101], "p_valu": 96, "benign": 96, "curat": [96, 102], "bigger": 97, "make_classif": 97, "5000": [97, 106], "n_featur": 97, "n_inform": 97, "n_redund": 97, "n_repeat": 97, "n_class": 97, "n_clusters_per_class": 97, "flip_i": 97, "class_sep": 97, "faiss": 97, "x_faiss": 97, "float32": [97, 105], "normalize_l2": 97, "index_factori": 97, "hnsw32": 97, "flat": [97, 98], "metric_inner_product": 97, "a_min": 97, "a_max": 97, "create_knn_graph": 97, "assert": 97, "indices_1d": 97, "ravel": 97, "distances_1d": 97, "sort_graph_by_row_valu": 97, "warn_when_not_sort": 97, "50000": 97, "524": 97, "991400": 97, "356924": 97, "363": [97, 100], "619581": 97, "108": [97, 105], "500000": 97, "651838": 97, "999827": 97, "031217": 97, "933716": 97, "627345": 97, "998540": 97, "530909": 97, "296974": 97, "646765": 97, "942721": 97, "332824": 97, "803246": 97, "625202": 97, "999816": 97, "474031": 97, "706253": 97, "655108": 97, "997703": 97, "131466": 97, "912389": 97, "639200": 97, "4995": 97, "998646": 97, "504755": 97, "746777": 97, "680033": 97, "4996": 97, "894230": 97, "340986": 97, "816472": 97, "640711": 97, "4997": 97, "999100": 97, "428545": 97, "592421": 97, "658949": 97, "4998": 97, "986792": 97, "273710": 97, "618033": 97, "4999": 97, "986776": 97, "273524": 97, "618084": 97, "instabl": 97, "proxim": 97, "analys": 97, "comfort": 97, "explor": [97, 105, 106], "third": 97, "parti": [97, 110], "newsgroup": 97, "alt": [97, 98], "atheism": [97, 98], "sci": [97, 98], "fetch_20newsgroup": 97, "newsgroups_train": 97, "header": 97, "footer": 97, "quot": 97, "df_text": 97, "target_nam": 97, "enlighten": 97, "omnipot": 97, "19apr199320262420": 97, "kelvin": 97, "jpl": 97, "nasa": 97, "gov": 97, "baa": 97, "nhenri": 97, "he": 97, "nno": 97, "ge": 97, "nlucki": 97, "babi": [97, 98], "tfidfvector": 97, "feature_extract": 97, "x_vector": 97, "data_valuation_issu": 97, "147": [97, 101, 105], "500047": 97, "500093": 97, "499953": 97, "1068": 97, "1069": 97, "1070": 97, "1071": 97, "1072": 97, "1073": 97, "concentr": 97, "seaborn": 97, "sn": 97, "distinguish": [97, 100], "strip": 97, "stripplot": 97, "hue": [97, 108], "dodg": 97, "jitter": 97, "axvlin": [97, 106], "xlabel": 97, "ourselv": 97, "make_blob": 97, "center": [97, 98], "cluster_std": 97, "n_noisy_label": 97, "meaning": [97, 99, 100, 106], "silhouette_scor": 97, "gridsearchcv": 97, "silhouett": 97, "cluster_label": 97, "fit_predict": 97, "param_grid": [97, 100], "grid_search": 97, "best_kmean": 97, "best_estimator_": 97, "underperforming_group_issu": 97, "328308": 97, "tab10": 97, "domain": 97, "knowledg": [97, 101], "dataset_tsv": 97, "ag": [97, 108], "gender": 97, "educ": 97, "experi": 97, "highsalari": 97, "indiana": 97, "phd": 97, "male": 97, "bachelor": 97, "femal": 97, "kansa": 97, "school": [97, 98], "ohio": 97, "57": [97, 98, 100, 101, 110], "california": 97, "59": [97, 98, 105], "34": [97, 98, 101, 103, 105, 110], "63": [97, 100, 101, 105, 108], "47": [97, 98, 105], "stringio": 97, "sep": [97, 110], "easier": [97, 101], "simplic": [97, 104], "ordinalencod": 97, "columns_to_encod": 97, "encoded_df": 97, "salari": 97, "573681": 97, "underpin": 97, "caught": 97, "whenev": 97, "generate_data_depend": 97, "num_sampl": 97, "a1": 97, "a2": 97, "a3": 97, "375": 97, "975": 97, "non_iid_issu": 97, "796474": 97, "842432": 97, "922562": 97, "820759": 97, "873136": 97, "887373": 97, "825101": 97, "855875": 97, "751795": 97, "835796": 97, "ylabel": [97, 106], "coolwarm": 97, "colorbar": [97, 108], "strong": 97, "evid": [97, 100], "inter": 97, "mitig": 97, "risk": [97, 100], "deeper": 97, "tsv": 97, "tab": 97, "pars": 97, "annual_spend": 97, "number_of_transact": 97, "last_purchase_d": 97, "rural": 97, "4099": 97, "2024": [97, 110], "6421": 97, "nat": 97, "suburban": 97, "5436": 97, "4046": 97, "66": [97, 98, 100], "3467": 97, "67": [97, 98, 100, 105, 108, 110], "4757": 97, "4199": 97, "4991": 97, "4655": 97, "82": [97, 98, 100, 101, 105, 108], "5584": 97, "urban": 97, "3102": 97, "6637": 97, "9167": 97, "6790": 97, "5327": 97, "parse_d": 97, "lose": 97, "intact": 97, "encode_categorical_column": 97, "placehold": 97, "dropna": [97, 103], "category_to_numb": 97, "_encod": 97, "gender_encod": 97, "location_encod": 97, "focus": [97, 100, 101, 103, 104, 108], "null_issu": 97, "833333": 97, "sorted_indic": [97, 105], "sorted_df": 97, "nice": 97, "styler": 97, "combined_df": 97, "concat": [97, 100, 108], "highlight_null_valu": 97, "val": [97, 101], "yellow": [97, 98], "highlight_datalab_column": 97, "lightblu": 97, "highlight_is_null_issu": 97, "orang": [97, 98], "styled_df": 97, "nbsp": [97, 99, 100, 101], "160000": 97, "820000": 97, "460000": 97, "470000": 97, "960000": 97, "620000": 97, "550000": 97, "660000": 97, "670000": [97, 98], "370000": 97, "530000": 97, "710000": 97, "020000": 97, "320000": 97, "990000": 97, "rarer": 97, "fairer": 97, "randomli": [97, 100, 101], "class_imbalance_issu": 97, "countplot": 97, "xtick": 97, "rotat": 97, "ytick": 97, "filtered_df": 97, "xy": 97, "va": 97, "textual": 97, "get_ytick": 97, "nbar": 97, "nimbal": 97, "get_legend_handles_label": 97, "title_fonts": 97, "aspect": 97, "anomali": [97, 105], "enhanc": [97, 101, 103, 105], "artifici": 97, "directori": [97, 110], "subdirectori": 97, "nc": [97, 105, 109, 110], "unzip": [97, 105, 110], "09": [97, 100, 104, 105, 108, 110], "199": [97, 100, 105], "111": [97, 103, 108], "153": [97, 100, 105], "110": [97, 105], "connect": [97, 110], "443": [97, 110], "await": [97, 110], "ok": [97, 107, 110], "986707": 97, "964k": 97, "963": 97, "58k": 97, "kb": [97, 110], "mb": [97, 110], "imagefold": 97, "load_image_dataset": 97, "data_dir": 97, "root": [97, 106], "image_dataset": 97, "img": [97, 106, 108], "from_dict": [97, 99], "darkened_imag": 97, "job": 97, "015": 97, "label_uncorrelatedness_scor": 97, "image_issu": 97, "nimag": 97, "237196": 97, "197229": 97, "254188": 97, "229170": 97, "208907": 97, "793840": 97, "196": [97, 100, 101, 105], "197": [97, 101, 105], "971560": 97, "198": [97, 101, 105], "862236": 97, "973533": 97, "stronger": 97, "frog": [97, 98, 106], "darken": 97, "concept": 97, "notabl": 97, "preval": 97, "warrant": 97, "programmat": 97, "plot_scores_label": 97, "issues_copi": 97, "boxplot": 97, "refin": 98, "instruct": [98, 99, 100], "studi": [98, 105], "mnist_test_set": 98, "imagenet_val_set": 98, "tench": 98, "goldfish": 98, "white": [98, 110], "shark": 98, "tiger": 98, "hammerhead": 98, "electr": 98, "rai": 98, "stingrai": 98, "cock": 98, "hen": 98, "ostrich": 98, "brambl": 98, "goldfinch": 98, "hous": 98, "finch": 98, "junco": 98, "indigo": 98, "bunt": 98, "american": [98, 110], "robin": 98, "bulbul": 98, "jai": 98, "magpi": 98, "chickade": 98, "dipper": 98, "kite": 98, "bald": 98, "eagl": 98, "vultur": 98, "grei": 98, "owl": 98, "salamand": 98, "smooth": 98, "newt": 98, "spot": [98, 99, 105], "axolotl": 98, "bullfrog": 98, "tree": 98, "tail": 98, "loggerhead": 98, "sea": 98, "turtl": 98, "leatherback": 98, "mud": 98, "terrapin": 98, "band": 98, "gecko": 98, "green": [98, 110], "iguana": 98, "carolina": 98, "anol": 98, "desert": 98, "grassland": 98, "whiptail": 98, "lizard": 98, "agama": 98, "frill": 98, "neck": 98, "allig": 98, "gila": 98, "monster": 98, "european": 98, "chameleon": 98, "komodo": 98, "dragon": 98, "nile": 98, "crocodil": 98, "triceratop": 98, "worm": 98, "snake": 98, "ring": 98, "eastern": 98, "hog": 98, "nose": 98, "kingsnak": 98, "garter": 98, "water": 98, "vine": 98, "night": 98, "boa": 98, "constrictor": 98, "african": 98, "rock": 98, "indian": 98, "cobra": 98, "mamba": 98, "saharan": 98, "horn": 98, "viper": 98, "diamondback": 98, "rattlesnak": 98, "sidewind": 98, "trilobit": 98, "harvestman": 98, "scorpion": 98, "garden": 98, "spider": 98, "barn": 98, "southern": 98, "widow": 98, "tarantula": 98, "wolf": 98, "tick": 98, "centiped": 98, "grous": 98, "ptarmigan": 98, "ruf": 98, "prairi": 98, "peacock": 98, "quail": 98, "partridg": 98, "parrot": 98, "macaw": 98, "sulphur": 98, "crest": 98, "cockatoo": 98, "lorikeet": 98, "coucal": 98, "bee": 98, "eater": 98, "hornbil": 98, "hummingbird": 98, "jacamar": 98, "toucan": 98, "breast": 98, "mergans": 98, "goos": 98, "swan": 98, "tusker": 98, "echidna": 98, "platypu": 98, "wallabi": 98, "koala": 98, "wombat": 98, "jellyfish": 98, "anemon": 98, "brain": 98, "coral": 98, "flatworm": 98, "nematod": 98, "conch": 98, "snail": 98, "slug": 98, "chiton": 98, "chamber": 98, "nautilu": 98, "dung": 98, "crab": 98, "fiddler": 98, "king": 98, "lobster": 98, "spini": 98, "crayfish": 98, "hermit": 98, "isopod": 98, "stork": 98, "spoonbil": 98, "flamingo": 98, "heron": 98, "egret": 98, "bittern": 98, "crane": 98, "bird": [98, 106], "limpkin": 98, "gallinul": 98, "coot": 98, "bustard": 98, "ruddi": 98, "turnston": 98, "dunlin": 98, "redshank": 98, "dowitch": 98, "oystercatch": 98, "pelican": 98, "penguin": 98, "albatross": 98, "whale": 98, "killer": 98, "dugong": 98, "lion": 98, "chihuahua": 98, "japanes": 98, "chin": 98, "maltes": 98, "pekinges": 98, "shih": 98, "tzu": 98, "charl": 98, "spaniel": 98, "papillon": 98, "terrier": 98, "rhodesian": 98, "ridgeback": 98, "afghan": [98, 110], "hound": 98, "basset": 98, "beagl": 98, "bloodhound": 98, "bluetick": 98, "coonhound": 98, "tan": 98, "walker": 98, "foxhound": 98, "redbon": 98, "borzoi": 98, "irish": 98, "wolfhound": 98, "italian": 98, "greyhound": 98, "whippet": 98, "ibizan": 98, "norwegian": 98, "elkhound": 98, "otterhound": 98, "saluki": 98, "scottish": 98, "deerhound": 98, "weimaran": 98, "staffordshir": 98, "bull": 98, "bedlington": 98, "border": 98, "kerri": 98, "norfolk": 98, "norwich": 98, "yorkshir": 98, "wire": 98, "fox": 98, "lakeland": 98, "sealyham": 98, "airedal": 98, "cairn": 98, "australian": 98, "dandi": 98, "dinmont": 98, "boston": 98, "miniatur": 98, "schnauzer": 98, "giant": 98, "tibetan": 98, "silki": 98, "wheaten": 98, "west": 98, "highland": 98, "lhasa": 98, "apso": 98, "retriev": 98, "curli": 98, "golden": 98, "labrador": 98, "chesapeak": 98, "bai": 98, "german": [98, 110], "shorthair": 98, "pointer": 98, "vizsla": 98, "setter": 98, "gordon": 98, "brittani": 98, "clumber": 98, "springer": 98, "welsh": 98, "cocker": 98, "sussex": 98, "kuvasz": 98, "schipperk": 98, "groenendael": 98, "malinoi": 98, "briard": 98, "kelpi": 98, "komondor": 98, "sheepdog": 98, "shetland": 98, "colli": 98, "bouvier": 98, "de": 98, "flandr": 98, "rottweil": 98, "shepherd": 98, "dobermann": 98, "pinscher": 98, "swiss": [98, 110], "mountain": 98, "bernes": 98, "appenzel": 98, "sennenhund": 98, "entlebuch": 98, "boxer": 98, "bullmastiff": 98, "mastiff": 98, "french": 98, "bulldog": 98, "dane": 98, "st": 98, "bernard": 98, "huski": 98, "alaskan": 98, "malamut": 98, "siberian": 98, "dalmatian": 98, "affenpinsch": 98, "basenji": 98, "pug": 98, "leonberg": 98, "newfoundland": 98, "pyrenean": 98, "samoi": 98, "pomeranian": 98, "chow": 98, "keeshond": 98, "griffon": 98, "bruxelloi": 98, "pembrok": 98, "corgi": 98, "cardigan": 98, "poodl": 98, "mexican": 98, "hairless": 98, "tundra": 98, "coyot": 98, "dingo": 98, "dhole": 98, "wild": 98, "hyena": 98, "kit": 98, "arctic": 98, "tabbi": 98, "persian": 98, "siames": 98, "egyptian": 98, "mau": 98, "cougar": 98, "lynx": 98, "leopard": 98, "snow": 98, "jaguar": 98, "cheetah": 98, "brown": [98, 109], "bear": 98, "polar": 98, "sloth": 98, "mongoos": 98, "meerkat": 98, "beetl": 98, "ladybug": 98, "longhorn": 98, "leaf": 98, "rhinocero": 98, "weevil": 98, "fly": 98, "ant": 98, "grasshopp": 98, "cricket": 98, "stick": 98, "insect": 98, "cockroach": 98, "manti": 98, "cicada": 98, "leafhopp": 98, "lacew": 98, "dragonfli": 98, "damselfli": 98, "admir": 98, "ringlet": 98, "monarch": 98, "butterfli": 98, "gossam": 98, "wing": 98, "starfish": 98, "urchin": 98, "cucumb": 98, "cottontail": 98, "rabbit": 98, "hare": 98, "angora": 98, "hamster": 98, "porcupin": 98, "squirrel": 98, "marmot": 98, "beaver": 98, "guinea": 98, "pig": 98, "sorrel": 98, "zebra": 98, "boar": 98, "warthog": 98, "hippopotamu": 98, "ox": 98, "buffalo": 98, "bison": 98, "bighorn": 98, "sheep": 98, "alpin": 98, "ibex": 98, "hartebeest": 98, "impala": 98, "gazel": 98, "dromedari": 98, "llama": 98, "weasel": 98, "mink": 98, "polecat": 98, "foot": 98, "ferret": 98, "otter": 98, "skunk": 98, "badger": 98, "armadillo": 98, "toed": 98, "orangutan": 98, "gorilla": 98, "chimpanze": 98, "gibbon": 98, "siamang": 98, "guenon": 98, "pata": 98, "monkei": 98, "baboon": 98, "macaqu": 98, "langur": 98, "colobu": 98, "probosci": 98, "marmoset": 98, "capuchin": 98, "howler": 98, "titi": 98, "geoffroi": 98, "lemur": 98, "indri": 98, "asian": 98, "eleph": 98, "bush": 98, "snoek": 98, "eel": 98, "coho": 98, "salmon": 98, "beauti": 98, "clownfish": 98, "sturgeon": 98, "garfish": 98, "lionfish": 98, "pufferfish": 98, "abacu": 98, "abaya": 98, "academ": 98, "gown": 98, "accordion": 98, "acoust": 98, "guitar": 98, "aircraft": 98, "carrier": 98, "airlin": 98, "airship": 98, "altar": 98, "ambul": 98, "amphibi": 98, "clock": [98, 110], "apiari": 98, "apron": 98, "wast": 98, "assault": 98, "rifl": 98, "backpack": 98, "bakeri": 98, "balanc": 98, "beam": 98, "balloon": 98, "ballpoint": 98, "pen": 98, "aid": 98, "banjo": 98, "balust": 98, "barbel": 98, "barber": 98, "chair": [98, 105], "barbershop": 98, "baromet": 98, "barrel": 98, "wheelbarrow": 98, "basebal": 98, "basketbal": 98, "bassinet": 98, "bassoon": 98, "swim": 98, "cap": 98, "bath": 98, "towel": 98, "bathtub": 98, "station": 98, "wagon": 98, "lighthous": 98, "beaker": 98, "militari": 98, "beer": 98, "bottl": 98, "glass": 98, "bell": 98, "cot": 98, "bib": 98, "bicycl": [98, 109], "bikini": 98, "binder": 98, "binocular": 98, "birdhous": 98, "boathous": 98, "bobsleigh": 98, "bolo": 98, "tie": 98, "poke": 98, "bonnet": 98, "bookcas": 98, "bookstor": 98, "bow": 98, "brass": 98, "bra": 98, "breakwat": 98, "breastplat": 98, "broom": 98, "bucket": 98, "buckl": 98, "bulletproof": 98, "vest": 98, "butcher": 98, "shop": 98, "taxicab": 98, "cauldron": 98, "candl": 98, "cannon": 98, "cano": 98, "mirror": [98, 105], "carousel": 98, "carton": 98, "wheel": 98, "teller": 98, "cassett": 98, "player": 98, "castl": 98, "catamaran": 98, "cd": 98, "cello": 98, "mobil": [98, 110], "chain": 98, "fenc": [98, 109], "mail": 98, "chainsaw": 98, "chest": 98, "chiffoni": 98, "chime": 98, "china": 98, "cabinet": 98, "christma": 98, "stock": 98, "church": 98, "movi": 98, "theater": 98, "cleaver": 98, "cliff": 98, "dwell": 98, "cloak": 98, "clog": 98, "cocktail": 98, "shaker": 98, "coffe": 98, "mug": 98, "coffeemak": 98, "coil": 98, "lock": 98, "keyboard": 98, "confectioneri": 98, "ship": [98, 106], "corkscrew": 98, "cornet": 98, "cowboi": 98, "boot": 98, "hat": 98, "cradl": 98, "crash": 98, "helmet": 98, "crate": 98, "infant": 98, "bed": 98, "crock": 98, "pot": 98, "croquet": 98, "crutch": 98, "cuirass": 98, "dam": 98, "desk": 98, "desktop": 98, "rotari": 98, "dial": 98, "telephon": 98, "diaper": 98, "watch": 98, "dine": 98, "dishcloth": 98, "dishwash": 98, "disc": 98, "brake": 98, "dock": 98, "sled": 98, "dome": 98, "doormat": 98, "drill": 98, "rig": 98, "drum": 98, "drumstick": 98, "dumbbel": 98, "dutch": 98, "oven": 98, "fan": 98, "locomot": 98, "entertain": 98, "envelop": 98, "espresso": 98, "powder": 98, "feather": 98, "fireboat": 98, "engin": [98, 109], "screen": 98, "sheet": 98, "flagpol": 98, "flute": 98, "footbal": 98, "forklift": 98, "fountain": 98, "poster": 98, "freight": 98, "fry": 98, "pan": 98, "fur": 98, "garbag": 98, "ga": 98, "pump": 98, "goblet": 98, "kart": 98, "golf": 98, "cart": 98, "gondola": 98, "gong": 98, "grand": 98, "piano": 98, "greenhous": 98, "grill": 98, "groceri": 98, "guillotin": 98, "barrett": 98, "hair": 98, "sprai": 98, "hammer": 98, "dryer": 98, "hand": [98, 101], "handkerchief": 98, "drive": 98, "harmonica": 98, "harp": 98, "harvest": 98, "hatchet": 98, "holster": 98, "honeycomb": 98, "hoop": 98, "skirt": 98, "horizont": 98, "bar": 98, "drawn": 98, "hourglass": 98, "ipod": 98, "cloth": 98, "iron": 98, "jack": 98, "lantern": 98, "jean": 98, "jeep": 98, "jigsaw": 98, "puzzl": 98, "pull": 98, "rickshaw": 98, "joystick": 98, "kimono": 98, "knee": 98, "pad": 98, "knot": 98, "ladl": 98, "lampshad": 98, "laptop": 98, "lawn": 98, "mower": 98, "knife": 98, "lifeboat": 98, "lighter": 98, "limousin": 98, "ocean": 98, "liner": 98, "lipstick": 98, "slip": 98, "shoe": 98, "lotion": 98, "speaker": 98, "loup": 98, "sawmil": 98, "magnet": 98, "compass": 98, "mailbox": 98, "tight": 98, "tank": 98, "manhol": 98, "maraca": 98, "marimba": 98, "maypol": 98, "maze": 98, "cup": [98, 105], "medicin": 98, "megalith": 98, "microphon": 98, "microwav": 98, "milk": 98, "minibu": 98, "miniskirt": 98, "minivan": 98, "missil": 98, "mitten": [98, 99], "mix": 98, "bowl": 98, "modem": 98, "monasteri": 98, "monitor": 98, "mope": 98, "mortar": 98, "mosqu": 98, "mosquito": 98, "scooter": 98, "bike": 98, "tent": 98, "mous": [98, 99], "mousetrap": 98, "van": 98, "muzzl": 98, "nail": 98, "brace": 98, "necklac": 98, "nippl": 98, "obelisk": 98, "obo": 98, "ocarina": 98, "odomet": 98, "oil": 98, "oscilloscop": 98, "overskirt": 98, "bullock": 98, "oxygen": 98, "packet": 98, "paddl": 98, "padlock": 98, "paintbrush": 98, "pajama": 98, "palac": [98, 110], "parachut": 98, "park": 98, "bench": 98, "meter": 98, "passeng": 98, "patio": 98, "payphon": 98, "pedest": 98, "pencil": 98, "perfum": 98, "petri": 98, "dish": 98, "photocopi": 98, "plectrum": 98, "pickelhaub": 98, "picket": 98, "pickup": 98, "pier": 98, "piggi": 98, "pill": 98, "pillow": 98, "ping": 98, "pong": 98, "pinwheel": 98, "pirat": 98, "pitcher": 98, "plane": 98, "planetarium": 98, "plastic": 98, "plate": 98, "rack": 98, "plow": 98, "plunger": 98, "polaroid": 98, "camera": 98, "pole": [98, 109], "polic": 98, "poncho": 98, "billiard": 98, "soda": 98, "potter": 98, "prayer": 98, "rug": 98, "printer": 98, "prison": 98, "projectil": 98, "projector": 98, "hockei": 98, "puck": 98, "punch": 98, "purs": 98, "quill": 98, "quilt": 98, "race": 98, "racket": 98, "radiat": 98, "radio": 98, "telescop": 98, "rain": 98, "recreat": 98, "reel": 98, "reflex": 98, "refriger": 98, "remot": 98, "restaur": 98, "revolv": 98, "rotisseri": 98, "eras": 98, "rugbi": 98, "ruler": 98, "safe": 98, "safeti": 98, "salt": 98, "sarong": 98, "saxophon": 98, "scabbard": 98, "bu": [98, 109], "schooner": 98, "scoreboard": 98, "crt": 98, "screw": 98, "screwdriv": 98, "seat": 98, "belt": 98, "sew": 98, "shield": 98, "shoji": 98, "basket": 98, "shovel": 98, "shower": 98, "curtain": 98, "ski": 98, "sleep": 98, "door": 98, "slot": 98, "snorkel": 98, "snowmobil": 98, "snowplow": 98, "soap": 98, "dispens": 98, "soccer": [98, 110], "sock": [98, 99], "solar": 98, "thermal": 98, "collector": 98, "sombrero": 98, "soup": 98, "heater": 98, "shuttl": 98, "spatula": 98, "motorboat": 98, "web": 98, "spindl": 98, "sport": [98, 110], "spotlight": 98, "stage": 98, "steam": 98, "arch": 98, "bridg": 98, "steel": 98, "stethoscop": 98, "scarf": 98, "stone": 98, "wall": [98, 109], "stopwatch": 98, "stove": 98, "strainer": 98, "tram": 98, "stretcher": 98, "couch": 98, "stupa": 98, "submarin": 98, "sundial": 98, "sunglass": 98, "sunscreen": 98, "suspens": 98, "mop": 98, "sweatshirt": 98, "swimsuit": 98, "swing": 98, "switch": 98, "syring": 98, "lamp": 98, "tape": 98, "teapot": 98, "teddi": 98, "televis": [98, 110], "tenni": 98, "thatch": 98, "roof": 98, "thimbl": 98, "thresh": 98, "throne": 98, "tile": 98, "toaster": 98, "tobacco": 98, "toilet": 98, "totem": 98, "tow": 98, "tractor": 98, "semi": 98, "trailer": 98, "trai": 98, "trench": 98, "tricycl": 98, "trimaran": 98, "tripod": 98, "triumphal": 98, "trolleybu": 98, "trombon": 98, "tub": 98, "turnstil": 98, "typewrit": 98, "umbrella": 98, "unicycl": 98, "upright": 98, "vacuum": 98, "cleaner": [98, 100], "vase": 98, "vault": 98, "velvet": 98, "vend": 98, "vestment": 98, "viaduct": 98, "violin": 98, "volleybal": 98, "waffl": 98, "wallet": 98, "wardrob": 98, "sink": 98, "wash": 98, "jug": 98, "tower": 98, "whiskei": 98, "whistl": 98, "wig": 98, "shade": [98, 109], "windsor": 98, "wine": 98, "wok": 98, "wooden": 98, "spoon": 98, "wool": 98, "rail": 98, "shipwreck": 98, "yawl": 98, "yurt": 98, "websit": 98, "comic": 98, "book": 98, "crossword": 98, "traffic": [98, 105, 109], "sign": [98, 109, 110], "dust": 98, "jacket": [98, 105], "menu": 98, "guacamol": 98, "consomm": 98, "trifl": 98, "ic": 98, "cream": 98, "pop": 98, "baguett": 98, "bagel": 98, "pretzel": 98, "cheeseburg": 98, "mash": 98, "potato": 98, "cabbag": 98, "broccoli": 98, "cauliflow": 98, "zucchini": 98, "spaghetti": 98, "squash": 98, "acorn": 98, "butternut": 98, "artichok": 98, "pepper": [98, 99], "cardoon": 98, "mushroom": 98, "granni": 98, "smith": 98, "strawberri": 98, "lemon": 98, "pineappl": 98, "banana": 98, "jackfruit": 98, "custard": 98, "appl": 98, "pomegran": 98, "hai": 98, "carbonara": 98, "chocol": 98, "syrup": 98, "dough": 98, "meatloaf": 98, "pizza": 98, "pie": 98, "burrito": 98, "eggnog": 98, "alp": 98, "bubbl": 98, "reef": 98, "geyser": 98, "lakeshor": 98, "promontori": 98, "shoal": 98, "seashor": 98, "vallei": 98, "volcano": 98, "bridegroom": 98, "scuba": 98, "diver": 98, "rapese": 98, "daisi": 98, "ladi": 98, "slipper": 98, "corn": 98, "rose": 98, "hip": 98, "chestnut": 98, "fungu": 98, "agar": 98, "gyromitra": 98, "stinkhorn": 98, "earth": 98, "star": 98, "wood": 98, "bolet": 98, "ear": 98, "cifar10_test_set": 98, "airplan": [98, 106], "automobil": [98, 106], "deer": [98, 106], "cifar100_test_set": 98, "aquarium_fish": 98, "boi": 98, "camel": 98, "caterpillar": 98, "cattl": [98, 110], "cloud": 98, "dinosaur": 98, "dolphin": 98, "flatfish": 98, "forest": 98, "girl": 98, "kangaroo": 98, "lawn_mow": 98, "man": 98, "maple_tre": 98, "motorcycl": [98, 109], "oak_tre": 98, "orchid": 98, "palm_tre": 98, "pear": 98, "pickup_truck": 98, "pine_tre": 98, "plain": 98, "poppi": 98, "possum": 98, "raccoon": 98, "road": [98, 109], "rocket": 98, "seal": 98, "shrew": 98, "skyscrap": 98, "streetcar": 98, "sunflow": 98, "sweet_pepp": 98, "trout": 98, "tulip": 98, "willow_tre": 98, "woman": [98, 105], "caltech256": 98, "ak47": 98, "bat": 98, "glove": 98, "birdbath": 98, "blimp": 98, "bonsai": 98, "boom": 98, "breadmak": 98, "buddha": 98, "bulldoz": 98, "cactu": 98, "cake": 98, "tire": 98, "cartman": 98, "cereal": 98, "chandeli": 98, "chess": 98, "board": 98, "chimp": 98, "chopstick": 98, "coffin": 98, "coin": 98, "comet": 98, "cormor": 98, "globe": 98, "diamond": 98, "dice": 98, "doorknob": 98, "drink": 98, "straw": 98, "dumb": 98, "eiffel": 98, "elk": 98, "ewer": 98, "eyeglass": 98, "fern": 98, "fighter": 98, "jet": [98, 108], "extinguish": 98, "hydrant": 98, "firework": 98, "flashlight": 98, "floppi": 98, "fri": 98, "frisbe": 98, "galaxi": 98, "giraff": 98, "goat": 98, "gate": 98, "grape": 98, "pick": [98, 99], "hamburg": 98, "hammock": 98, "harpsichord": 98, "hawksbil": 98, "helicopt": 98, "hibiscu": 98, "homer": 98, "simpson": 98, "horsesho": 98, "air": 98, "skeleton": 98, "ibi": 98, "cone": 98, "iri": 98, "jesu": 98, "christ": 98, "joi": 98, "kayak": 98, "ketch": 98, "ladder": 98, "lath": 98, "licens": 98, "lightbulb": 98, "lightn": 98, "mandolin": 98, "mar": 98, "mattress": 98, "megaphon": 98, "menorah": 98, "microscop": 98, "minaret": 98, "minotaur": 98, "motorbik": 98, "mussel": 98, "neckti": 98, "octopu": 98, "palm": 98, "pilot": 98, "paperclip": 98, "shredder": 98, "pci": 98, "peopl": [98, 105], "pez": 98, "picnic": 98, "pram": 98, "prai": 98, "pyramid": 98, "rainbow": 98, "roulett": 98, "saddl": 98, "saturn": 98, "segwai": 98, "propel": 98, "sextant": 98, "music": 98, "skateboard": 98, "smokestack": 98, "sneaker": 98, "boat": 98, "stain": 98, "steer": 98, "stirrup": 98, "superman": 98, "sushi": 98, "armi": [98, 110], "sword": 98, "tambourin": 98, "teepe": 98, "court": 98, "theodolit": 98, "tomato": 98, "tombston": 98, "tour": 98, "pisa": 98, "treadmil": 98, "fork": 98, "tweezer": 98, "unicorn": 98, "vcr": 98, "waterfal": 98, "watermelon": 98, "weld": 98, "windmil": 98, "xylophon": 98, "yarmulk": 98, "yo": 98, "toad": 98, "twenty_news_test_set": 98, "comp": 98, "graphic": [98, 109], "misc": [98, 110], "sy": 98, "ibm": 98, "pc": 98, "hardwar": 98, "mac": 98, "forsal": 98, "rec": 98, "crypt": 98, "electron": 98, "med": 98, "soc": 98, "religion": 98, "christian": [98, 110], "talk": [98, 110], "polit": 98, "gun": 98, "mideast": 98, "amazon": 98, "neutral": 98, "imdb_test_set": 98, "all_class": 98, "20news_test_set": 98, "_load_classes_predprobs_label": 98, "dataset_nam": 98, "labelerror": 98, "url_bas": 98, "5392f6c71473055060be3044becdde1cbc18284d": 98, "url_label": 98, "original_test_label": 98, "_original_label": 98, "url_prob": 98, "cross_validated_predicted_prob": 98, "_pyx": 98, "num_part": 98, "datatset": 98, "bytesio": 98, "allow_pickl": 98, "pred_probs_part": 98, "url": 98, "_of_": 98, "nload": 98, "imdb": 98, "ve": [98, 99, 100, 101, 103, 105], "capit": 98, "29780": 98, "256": [98, 99, 100, 105], "780": 98, "medic": [98, 110], "doctor": 98, "254": [98, 105], "359223": 98, "640777": 98, "184": [98, 101], "258427": 98, "341176": 98, "263158": 98, "658824": 98, "337349": 98, "246575": 98, "662651": 98, "248": 98, "330000": 98, "355769": 98, "251": [98, 105], "167": [98, 101, 105], "252": [98, 100], "112": [98, 100], "253": [98, 105], "022989": 98, "049505": 98, "190": [98, 101, 105], "002216": 98, "000974": 98, "000873": 98, "000739": 98, "32635": 98, "32636": 98, "32637": 98, "32638": 98, "32639": 98, "32640": 98, "051": 98, "002242": 98, "997758": 98, "002088": 98, "001045": 98, "997912": 98, "002053": 98, "997947": 98, "001980": 98, "000991": 98, "998020": 98, "001946": 98, "002915": 98, "998054": 98, "001938": 98, "002904": 98, "998062": 98, "001020": 98, "998980": 98, "001018": 98, "002035": 98, "998982": 98, "999009": 98, "0003": 98, "0002": 98, "071": 98, "067269": 98, "929": 98, "046": 98, "058243": 98, "954": 98, "035": 98, "032096": 98, "031": 98, "012232": 98, "969": 98, "022": 98, "025896": 98, "978": 98, "020": [98, 101], "013092": 98, "018": 98, "013065": 98, "016": 98, "030542": 98, "984": 98, "013": 98, "020833": 98, "987": 98, "012": 98, "010020": 98, "988": 98, "0073": 98, "0020": 98, "0016": 98, "0015": 98, "0014": 98, "0013": 98, "0012": 98, "0010": 98, "0008": 98, "0007": 98, "0006": 98, "0005": 98, "0004": 98, "244": [98, 105], "452381": 98, "459770": 98, "523364": 98, "460784": 98, "446602": 98, "103774": 98, "030612": 98, "110092": 98, "049020": 98, "0034": 98, "0032": 98, "0026": 98, "0025": 98, "4945": 98, "4946": 98, "4947": 98, "4948": 98, "4949": 98, "4950": 98, "846": 98, "7532": 98, "532": 98, "034483": 98, "009646": 98, "965517": 98, "030457": 98, "020513": 98, "969543": 98, "028061": 98, "035443": 98, "971939": 98, "025316": 98, "005168": 98, "974684": 98, "049751": 98, "979487": 98, "019920": 98, "042802": 98, "980080": 98, "017677": 98, "005115": 98, "982323": 98, "012987": 98, "005236": 98, "987013": 98, "012723": 98, "025126": 98, "987277": 98, "010989": 98, "008264": 98, "989011": 98, "010283": 98, "027778": 98, "989717": 98, "009677": 98, "990323": 98, "007614": 98, "010127": 98, "992386": 98, "005051": 98, "994949": 98, "005025": 98, "994975": 98, "005013": 98, "994987": 98, "001859": 98, "001328": 98, "000929": 98, "000664": 98, "186": [98, 101], "188": [98, 101, 104], "189": [98, 101], "snippet": 99, "nlp": [99, 110], "mind": [99, 101], "alphanumer": 99, "facilit": 99, "seamless": 99, "classlabel": 99, "guidanc": 99, "labels_str": 99, "datalab_str": 99, "labels_int": 99, "remap": 99, "datalab_int": 99, "my_dict": 99, "pet_nam": 99, "rover": 99, "rocki": 99, "speci": 99, "datalab_dataset": 99, "number_of_class": 99, "total_number_of_data_point": 99, "feed": 99, "alphabet": 99, "labels_proper_format": 99, "your_classifi": 99, "issues_datafram": 99, "class_predicted_for_flagged_exampl": 99, "class_predicted_for_all_exampl": 99, "grant": 99, "On": [99, 100, 101, 105], "merged_dataset": 99, "label_column_nam": 99, "datataset": 99, "fair": [99, 101], "game": 99, "speedup": [99, 106], "tempfil": 99, "mkdtemp": 99, "sped": 99, "anywai": 99, "pred_probs_merg": 99, "merge_rare_class": 99, "count_threshold": 99, "class_mapping_orig2new": 99, "heath_summari": 99, "num_examples_per_class": 99, "rare_class": 99, "num_classes_merg": 99, "other_class": 99, "labels_merg": 99, "new_c": 99, "merged_prob": 99, "new_class": 99, "original_class": 99, "num_check": 99, "ones_array_ref": 99, "isclos": 99, "though": [99, 101, 110], "successfulli": 99, "virtuou": [99, 103], "cycl": [99, 103], "jointli": 99, "junk": 99, "clutter": 99, "unknown": 99, "caltech": 99, "combined_boolean_mask": 99, "mask1": 99, "mask2": 99, "gradientboostingclassifi": [99, 101], "true_error": [99, 101, 104], "101": [99, 100, 105], "102": [99, 104, 105], "104": [99, 101, 105], "model_to_find_error": 99, "model_to_return": 99, "cl0": 99, "randomizedsearchcv": 99, "expens": 99, "param_distribut": 99, "learning_r": [99, 100, 101], "max_depth": [99, 100, 101], "magnitud": 99, "coeffici": [99, 108], "optin": 99, "environ": [99, 100, 101], "rerun": [99, 100, 101], "cell": [99, 100, 101], "unabl": [99, 100, 101], "render": [99, 100, 101], "nbviewer": [99, 100, 101], "cleanlearninginot": [99, 101], "fittedcleanlearn": [99, 101], "linearregressionlinearregress": 99, "unexpectedli": 99, "emphas": 99, "crucial": 99, "merge_duplicate_set": 99, "merge_kei": 99, "construct_group_kei": 99, "merged_set": 99, "consolidate_set": 99, "issubset": 99, "frozenset": [99, 100], "sets_list": 99, "mutabl": 99, "new_set": 99, "current_set": 99, "intersecting_set": 99, "lowest_score_strategi": 99, "sub_df": 99, "filter_near_dupl": 99, "strategy_fn": 99, "strategy_kwarg": 99, "duplicate_row": 99, "group_kei": 99, "to_keep_indic": 99, "groupbi": 99, "explod": 99, "to_remov": 99, "isin": [99, 106], "kept": 99, "ids_to_remove_seri": 99, "assist": 99, "streamlin": [99, 100], "ux": 99, "agpl": 99, "compani": 99, "commerci": 99, "alter": [99, 100], "email": 99, "team": 99, "anywher": 99, "profession": 99, "expert": 99, "recogn": 100, "vital": 100, "leakag": 100, "comparion": 100, "leak": 100, "blueprint": 100, "divers": 100, "parameter": 100, "tldr": 100, "answer": [100, 101], "subtl": 100, "faith": 100, "danger": 100, "inevit": [100, 106], "xgbclassifi": 100, "123456": 100, "df_train": 100, "s3": [100, 105, 109, 110], "amazonaw": [100, 105, 109, 110], "clos_train_data": 100, "df_test": 100, "clos_test_data": 100, "noisy_letter_grad": 100, "018bff": 100, "076d92": 100, "c80059": 100, "e38f8a": 100, "d57e1a": 100, "grade_l": 100, "notes_l": 100, "train_featur": 100, "train_features_v2": 100, "train_labels_v2": 100, "test_featur": 100, "preprocessed_train_data": 100, "preprocessed_test_data": 100, "haven": 100, "features_df": 100, "heterogenou": 100, "full_df": 100, "reset_index": [100, 103], "749": 100, "583745": 100, "291382": 100, "5837": 100, "748": 100, "604": 100, "510": 100, "227": [100, 104, 105], "719": 100, "690": 100, "444": 100, "547": 100, "647": 100, "2914": 100, "611": 100, "687869": 100, "610": 100, "687883": 100, "612": 100, "688146": 100, "609": 100, "688189": 100, "613": 100, "688713": 100, "2913818469137725": 100, "came": [100, 110], "full_duplicate_result": 100, "train_idx_cutoff": 100, "nd_set_has_index_over_training_cutoff": 100, "exact_dupl": 100, "627": 100, "678": 100, "615": 100, "292": 100, "620": 100, "420": 100, "704": 100, "431": 100, "459": 100, "672": 100, "564": 100, "696": 100, "605": 100, "exact_duplicates_indic": 100, "indices_of_duplicates_to_drop": 100, "4a3f75": 100, "d030b5": 100, "ddd0ba": 100, "8e6d24": 100, "464aab": 100, "ee3387": 100, "61e807": 100, "71d7b9": 100, "83e31f": 100, "edeb53": 100, "cd52b5": 100, "84": [100, 105, 108], "454e51": 100, "042686": 100, "12a73f": 100, "tree_method": 100, "hist": [100, 106], "enable_categor": 100, "booster": 100, "callback": 100, "colsample_bylevel": 100, "colsample_bynod": 100, "colsample_bytre": 100, "early_stopping_round": 100, "eval_metr": 100, "feature_typ": 100, "gamma": 100, "grow_polici": 100, "importance_typ": 100, "interaction_constraint": 100, "max_bin": 100, "max_cat_threshold": 100, "max_cat_to_onehot": 100, "max_delta_step": 100, "max_leav": 100, "min_child_weight": 100, "monotone_constraint": 100, "multi_strategi": 100, "n_estim": [100, 101], "num_parallel_tre": 100, "x27": [100, 101], "softprob": 100, "xgbclassifierifittedxgbclassifi": 100, "test_pred_prob": [100, 106], "test_lab": 100, "test_features_arrai": 100, "134": 100, "798507": 100, "370259": 100, "625352": 100, "524042": 100, "097015": 100, "7985": 100, "000537": 100, "000903": 100, "001743": 100, "106": 100, "001853": 100, "002121": 100, "3703": 100, "752463e": 100, "784418e": 100, "477741e": 100, "134230e": 100, "153555e": 100, "6254": 100, "143272": 100, "146501": 100, "161431": 100, "5240": 100, "765240": 100, "771221": 100, "801589": 100, "801652": 100, "810735": 100, "5240417899434826": 100, "0970": 100, "na": [100, 103], "test_label_issue_result": 100, "test_label_issues_ord": 100, "2bd759": 100, "34ccdd": 100, "bb3bab": 100, "103": [100, 101, 105], "bf1b14": 100, "4787de": 100, "865cbd": 100, "32d53f": 100, "5b2f76": 100, "28f8b4": 100, "df814d": 100, "f17261": 100, "1db3ff": 100, "ded944": 100, "124": [100, 105], "343dd3": 100, "homework": [100, 108], "8d904d": 100, "e4f0d5": 100, "d6d208": 100, "76c083": 100, "695f96": 100, "745c23": 100, "13b36e": 100, "5ba892": 100, "9f0216": 100, "003628": 100, "004006": 100, "004031": 100, "007930": 100, "013226": 100, "015255": 100, "017692": 100, "019767": 100, "036197": 100, "054746": 100, "055110": 100, "062675": 100, "112695": 100, "121059": 100, "171280": 100, "181689": 100, "208001": 100, "275028": 100, "346032": 100, "396350": 100, "401493": 100, "474349": 100, "mislead": 100, "breviti": 100, "indices_to_drop_from_test_data": 100, "df_test_clean": 100, "acc_origin": 100, "tediou": 100, "train_features_arrai": 100, "train_lab": 100, "318": [100, 108], "601": 100, "740433": 100, "344154": 100, "588290": 100, "437267": 100, "146423": 100, "977223": 100, "7404": 100, "162": 100, "000072": 100, "348": 100, "000161": 100, "232": [100, 105], "000256": 100, "205": [100, 105], "000458": 100, "000738": 100, "3442": 100, "588": 100, "358961e": 100, "336": [100, 105], "490911e": 100, "269": 100, "122475e": 100, "321": [100, 105], "374139e": 100, "311": 100, "358617e": 100, "5883": 100, "600": 100, "592": 100, "593": 100, "594": 100, "595": 100, "596": 100, "597": 100, "598": 100, "599": 100, "221": 100, "222": [100, 101], "315": 100, "332": [100, 105], "791060e": 100, "243": [100, 105, 110], "540": 100, "379106e": 100, "396": 100, "397": 100, "398": 100, "399": 100, "4373": 100, "165": [100, 104], "550374": 100, "627357": 100, "627496": 100, "627502": 100, "627919": 100, "43726734378061227": 100, "1464": 100, "506": 100, "393": 100, "508": 100, "9772": 100, "402": 100, "401": 100, "aggress": 100, "faithfulli": 100, "label_issue_result": 100, "566": 100, "568": 100, "571": 100, "572": 100, "574": 100, "576": 100, "578": 100, "585": 100, "587": 100, "590": 100, "near_duplicates_idx": 100, "117": [100, 101, 108], "122": [100, 101, 105], "146": 100, "155": [100, 101, 105], "156": [100, 101], "173": [100, 105], "224": [100, 105], "272": 100, "277": [100, 105], "279": [100, 105], "288": 100, "300": [100, 103, 110], "342": 100, "352": 100, "365": 100, "366": 100, "384": 100, "388": 100, "394": 100, "404": 100, "474": 100, "480": 100, "494": 100, "515": 100, "536": 100, "537": 100, "539": 100, "542": 100, "outliers_idx": 100, "143": [100, 104, 105], "159": [100, 104, 105], "163": [100, 101], "193": [100, 101], "194": [100, 101], "208": 100, "240": [100, 105], "241": 100, "242": [100, 105], "247": [100, 105], "287": [100, 105], "295": [100, 105], "299": [100, 105], "307": [100, 105], "350": 100, "361": 100, "378": 100, "379": 100, "392": 100, "419": 100, "432": 100, "479": 100, "484": 100, "485": 100, "489": 100, "492": 100, "504": 100, "511": 100, "522": 100, "523": 100, "535": 100, "543": 100, "567": 100, "579": 100, "591": 100, "idx_to_drop": 100, "276": [100, 105], "df_train_cur": 100, "clean_clf": 100, "clean_pr": 100, "acc_clean": 100, "inaccur": 100, "hybrid": 100, "quantit": 100, "hyper": 100, "default_edit_param": 100, "drop_label_issu": 100, "drop_outli": 100, "drop_near_dupl": 100, "candid": [100, 105], "edit_data": 100, "percentag": [100, 101], "num_label_issues_to_drop": 100, "num_outliers_to_drop": 100, "dedupl": 100, "unique_clust": 100, "unique_clusters_list": 100, "near_duplicates_idx_to_drop": 100, "n_drop": 100, "label_issues_idx_to_drop": 100, "outliers_idx_to_drop": 100, "train_features_clean": 100, "train_labels_clean": 100, "itertool": 100, "finer": 100, "param_combin": 100, "best_scor": 100, "best_param": 100, "train_features_preprocess": 100, "train_labels_preprocess": 100, "depth": 101, "survei": [101, 110], "scienc": 101, "multivariate_norm": [101, 103, 104], "make_data": [101, 103], "cov": [101, 103, 104], "avg_trac": [101, 104], "py_tru": 101, "noise_matrix_tru": 101, "noise_marix": 101, "s_test": 101, "noisy_test_label": 101, "purpl": 101, "namespac": 101, "exec": 101, "markerfacecolor": [101, 104], "markeredgecolor": [101, 104, 108], "markers": [101, 104, 108], "markeredgewidth": [101, 104, 108], "realist": 101, "7560": 101, "637318e": 101, "896262e": 101, "548391e": 101, "923417e": 101, "375075e": 101, "3454": 101, "014051": 101, "020451": 101, "249": [101, 105], "042594": 101, "043859": 101, "045954": 101, "6120": 101, "023714": 101, "007136": 101, "119": [101, 105, 110], "107266": 101, "033738": 101, "238": [101, 105], "119505": 101, "236": [101, 105], "037843": 101, "614915": 101, "624422": 101, "625965": 101, "626079": 101, "118": 101, "627675": 101, "695223": 101, "323529": 101, "523015": 101, "013720": 101, "675727": 101, "646521": 101, "magic": 101, "liter": 101, "identif": 101, "logisticregressionlogisticregress": 101, "ever": 101, "092": 101, "040": 101, "024": 101, "004": 101, "surpris": 101, "1705": 101, "01936": 101, "ton": 101, "yourfavoritemodel1": 101, "merged_label": 101, "merged_test_label": 101, "newli": [101, 103], "yourfavoritemodel2": 101, "yourfavoritemodel3": 101, "cl3": 101, "takeawai": 101, "my_test_pred_prob": 101, "my_test_pr": 101, "issues_test": 101, "corrected_test_label": 101, "pretend": 101, "cl_test_pr": 101, "fairli": 101, "label_acc": 101, "offset": 101, "nquestion": 101, "overestim": 101, "experienc": 101, "prioiri": 101, "known": 101, "versatil": 101, "label_issues_indic": 101, "213": [101, 105], "218": [101, 105], "152": 101, "170": 101, "214": 101, "164": [101, 104], "191": [101, 105], "206": [101, 105], "115": [101, 105], "201": [101, 105], "174": 101, "150": [101, 103, 105], "169": 101, "151": [101, 105], "168": 101, "precision_scor": 101, "recall_scor": 101, "f1_score": 101, "true_label_issu": 101, "filter_by_list": 101, "718750": [101, 103], "807018": 101, "912": 101, "733333": 101, "800000": 101, "721311": 101, "792793": 101, "908": 101, "676923": 101, "765217": 101, "892": 101, "567901": 101, "702290": 101, "844": 101, "gaug": 101, "label_issues_count": 101, "172": [101, 104], "157": 101, "easiest": 101, "modular": 101, "penalti": 101, "l2": 101, "model3": 101, "cv_pred_probs_1": 101, "cv_pred_probs_2": 101, "cv_pred_probs_3": 101, "label_quality_scores_best": 101, "cv_pred_probs_ensembl": 101, "label_quality_scores_bett": 101, "superior": [101, 107], "timm": 102, "glad": 103, "multiannotator_label": 103, "noisier": 103, "local_data": [103, 104], "true_labels_train": [103, 104], "noise_matrix_bett": 103, "noise_matrix_wors": 103, "transpos": [103, 106], "zfill": 103, "row_na_check": 103, "notna": 103, "a0001": 103, "a0002": 103, "a0003": 103, "a0004": 103, "a0005": 103, "a0006": 103, "a0007": 103, "a0008": 103, "a0009": 103, "a0010": 103, "a0041": 103, "a0042": 103, "a0043": 103, "a0044": 103, "a0045": 103, "a0046": 103, "a0047": 103, "a0048": 103, "a0049": 103, "a0050": 103, "60856743": 103, "41693214": 103, "40908785": 103, "87147629": 103, "64941785": 103, "10774851": 103, "0524466": 103, "71853246": 103, "37169848": 103, "66031048": 103, "multiannotator_util": 103, "crude": 103, "straight": 103, "majority_vote_label": 103, "736118": 103, "757751": 103, "782232": 103, "715565": 103, "824256": 103, "quality_annotator_a0001": 103, "quality_annotator_a0002": 103, "quality_annotator_a0003": 103, "quality_annotator_a0004": 103, "quality_annotator_a0005": 103, "quality_annotator_a0006": 103, "quality_annotator_a0007": 103, "quality_annotator_a0008": 103, "quality_annotator_a0009": 103, "quality_annotator_a0010": 103, "quality_annotator_a0041": 103, "quality_annotator_a0042": 103, "quality_annotator_a0043": 103, "quality_annotator_a0044": 103, "quality_annotator_a0045": 103, "quality_annotator_a0046": 103, "quality_annotator_a0047": 103, "quality_annotator_a0048": 103, "quality_annotator_a0049": 103, "quality_annotator_a0050": 103, "070564": 103, "216078": 103, "119188": 103, "alongisd": 103, "244981": 103, "208333": 103, "295979": 103, "294118": 103, "324197": 103, "310345": 103, "355316": 103, "346154": 103, "439732": 103, "480000": 103, "a0031": 103, "523205": 103, "580645": 103, "a0034": 103, "535313": 103, "607143": 103, "a0021": 103, "606999": 103, "a0015": 103, "609526": 103, "678571": 103, "a0011": 103, "621103": 103, "692308": 103, "improved_consensus_label": 103, "majority_vote_accuraci": 103, "cleanlab_label_accuraci": 103, "8581081081081081": 103, "9797297297297297": 103, "besid": 103, "sorted_consensus_quality_scor": 103, "worst_qual": 103, "better_qu": 103, "worst_quality_accuraci": 103, "better_quality_accuraci": 103, "9893238434163701": 103, "improved_pred_prob": 103, "treat": [103, 104, 108, 110], "analzi": 103, "copyright": 104, "advertis": 104, "violenc": 104, "nsfw": 104, "celeba": 104, "make_multilabel_data": 104, "boxes_coordin": 104, "box_multilabel": 104, "make_multi": 104, "bx1": 104, "by1": 104, "bx2": 104, "by2": 104, "label_list": 104, "ur": 104, "upper": 104, "inidx": 104, "logical_and": 104, "inv_d": 104, "labels_idx": 104, "true_labels_test": 104, "dict_unique_label": 104, "get_color_arrai": 104, "dcolor": 104, "aa4400": 104, "55227f": 104, "55a100": 104, "00ff00": 104, "007f7f": 104, "386b55": 104, "0000ff": 104, "y_onehot": 104, "single_class_label": 104, "stratifi": [104, 107], "kf": 104, "train_index": 104, "test_index": 104, "clf_cv": 104, "x_train_cv": 104, "x_test_cv": 104, "y_train_cv": 104, "y_test_cv": 104, "y_pred_cv": 104, "saw": 104, "num_to_displai": 104, "275": 104, "267": 104, "225": 104, "171": [104, 110], "234": 104, "262": [104, 105], "263": [104, 105], "266": [104, 105], "139": 104, "216": [104, 105], "265": 104, "despit": [104, 110], "suspect": 104, "888": 104, "8224": 104, "9632": 104, "968": 104, "6512": 104, "0444": 104, "774": 104, "labels_binary_format": 104, "labels_list_format": 104, "surround": 105, "scene": 105, "coco": 105, "everydai": 105, "has_label_issu": 105, "objectdetectionbenchmark": 105, "tutorial_obj": 105, "pkl": 105, "example_imag": 105, "_separate_label": 105, "_separate_predict": 105, "begin": 105, "image_path": 105, "rb": 105, "image_to_visu": 105, "seg_map": 105, "334": 105, "bboxes_ignor": 105, "290": 105, "286": 105, "285": 105, "231": 105, "293": 105, "235": 105, "289": 105, "282": 105, "281": 105, "271": 105, "280": 105, "326": 105, "333": 105, "261": 105, "319": 105, "257": 105, "283": 105, "303": 105, "316": 105, "323": 105, "327": 105, "226": 105, "228": 105, "219": 105, "239": 105, "209": 105, "202": 105, "230": 105, "215": 105, "220": 105, "229": 105, "217": [105, 110], "237": 105, "207": [105, 110], "204": 105, "223": 105, "149": 105, "140": 105, "246": 105, "268": 105, "273": 105, "284": 105, "136": 105, "145": 105, "297": 105, "317": 105, "192": 105, "324": 105, "203": 105, "320": 105, "314": 105, "291": 105, "000000481413": 105, "jpg": 105, "42398": 105, "44503": 105, "29968": 105, "21005": 105, "9978472": 105, "forgot": 105, "drew": 105, "label_issue_idx": 105, "num_examples_to_show": 105, "138": 105, "97489622": 105, "70610878": 105, "98764951": 105, "88899237": 105, "99085805": 105, "issue_idx": 105, "95569726e": 105, "03354841e": 105, "57510169e": 105, "58447666e": 105, "39755858e": 105, "issue_to_visu": 105, "000000009483": 105, "95569726168054e": 105, "addition": [105, 109], "visibl": 105, "missmatch": 105, "likelei": 105, "agnost": 105, "vaidat": 105, "inconsist": 105, "000000395701": 105, "033548411774308e": 105, "armchair": 105, "tv": 105, "000000154004": 105, "38300759625496356": 105, "foreground": 105, "000000448410": 105, "0008575101690203273": 105, "crowd": 105, "alon": 105, "resembl": [105, 106], "000000499768": 105, "9748962231208227": 105, "000000521141": 105, "8889923658893665": 105, "000000143931": 105, "9876495074395956": 105, "bonu": 105, "uncov": 105, "irregular": 105, "object_detection_util": 105, "calculate_bounding_box_area": 105, "num_imgs_to_show": 105, "lab_object_count": 105, "pred_object_count": 105, "000000430073": 105, "000000183709": 105, "000000189475": 105, "label_norm": 105, "pred_norm": 105, "area": [105, 109], "lab_area": 105, "pred_area": 105, "lab_area_mean": 105, "lab_area_std": 105, "max_deviation_valu": 105, "max_deviation_class": 105, "deviation_valu": 105, "deviation_class": 105, "mean_area": 105, "std_area": 105, "class_area": 105, "deviations_awai": 105, "max_deviation_index": 105, "num_imgs_to_show_per_class": 105, "class_num": 105, "000000422886": 105, "000000341828": 105, "000000461009": 105, "train_feature_embed": 106, "ood_train_feature_scor": 106, "test_feature_embed": 106, "ood_test_feature_scor": 106, "ood_train_predictions_scor": 106, "train_pred_prob": 106, "ood_test_predictions_scor": 106, "pylab": 106, "rcparam": 106, "baggingclassifi": 106, "therebi": 106, "rescal": 106, "transform_norm": 106, "totensor": 106, "animal_class": 106, "non_animal_class": 106, "animal_idx": 106, "test_idx": 106, "toronto": 106, "edu": 106, "kriz": 106, "170498071": 106, "49690890": 106, "85it": 106, "plot_imag": 106, "visualize_outli": 106, "txt_class": 106, "npimg": 106, "show_label": 106, "data_subset": 106, "resnet50": 106, "corpu": 106, "2048": 106, "embed_imag": 106, "create_model": 106, "strang": 106, "odd": 106, "train_ood_features_scor": 106, "top_train_ood_features_idx": 106, "fun": 106, "negat": 106, "homogen": 106, "bottom_train_ood_features_idx": 106, "test_ood_features_scor": 106, "top_ood_features_idx": 106, "trade": 106, "5th": 106, "percentil": 106, "fifth_percentil": 106, "plt_rang": 106, "train_outlier_scor": 106, "test_outlier_scor": 106, "ood_features_indic": 106, "revisit": 106, "return_invers": 106, "train_feature_embeddings_sc": 106, "test_feature_embeddings_sc": 106, "train_pred_label": 106, "9702": 106, "train_ood_predictions_scor": 106, "test_ood_predictions_scor": 106, "lost": 106, "unsuit": 107, "convention": 107, "aforement": 107, "hypothet": 107, "contrast": 107, "tradit": 107, "disjoint": 107, "out_of_sample_pred_probs_for_a": 107, "out_of_sample_pred_probs_for_b": 107, "out_of_sample_pred_probs_for_c": 107, "out_of_sample_pred_prob": 107, "unsur": 107, "price": 108, "incom": 108, "sensor": 108, "histgradientboostingregressor": 108, "r2_score": 108, "student_grades_r": 108, "final_scor": 108, "true_final_scor": 108, "3d": 108, "mpl_toolkit": 108, "mplot3d": 108, "axes3d": 108, "errors_idx": 108, "add_subplot": 108, "z": 108, "errors_mask": 108, "feature_column": 108, "predicted_column": 108, "x_train_raw": 108, "x_test_raw": 108, "randomforestregressor": 108, "385101": 108, "499503": 108, "698255": 108, "776647": 108, "109373": 108, "170547": 108, "481096": 108, "984759": 108, "645270": 108, "795928": 108, "141": 108, "659": 108, "367": 108, "305": 108, "560": 108, "657": 108, "view_datapoint": 108, "preds_og": 108, "r2_og": 108, "838": 108, "found_label_issu": 108, "preds_cl": 108, "r2_cl": 108, "926": 108, "favorit": 108, "968627e": 108, "228799": 108, "646674e": 108, "402962": 108, "323818e": 108, "952758": 108, "422144e": 108, "456908": 108, "465815e": 108, "753968": 108, "791186e": 108, "110719": 108, "485156e": 108, "670640": 108, "225300e": 108, "749976": 108, "499679e": 108, "947007": 108, "067882e": 108, "648396": 108, "synthia": 109, "imagesegment": 109, "given_mask": 109, "predicted_mask": 109, "set_printopt": [109, 110], "sky": 109, "sidewalk": 109, "veget": 109, "terrain": 109, "rider": 109, "pred_probs_filepath": 109, "1088": 109, "1920": 109, "label_filepath": 109, "synthia_class": 109, "maunal": 109, "100000": 109, "244800": 109, "leftmost": 109, "middl": [109, 110], "infact": 109, "rightmost": 109, "discrep": 109, "3263230": 109, "783381": 109, "275110": 109, "255917": 109, "78225": 109, "55990": 109, "54315": 109, "33591": 109, "24645": 109, "21054": 109, "15045": 109, "14171": 109, "13832": 109, "13498": 109, "11490": 109, "9164": 109, "8769": 109, "6999": 109, "6031": 109, "5011": 109, "mistakenli": 109, "class_issu": 109, "aim": [109, 110], "domin": 109, "bunch": 110, "conll": 110, "2003": 110, "love": 110, "n_i": 110, "optional_list_of_ordered_class_nam": 110, "deepai": 110, "conll2003": 110, "rm": 110, "tokenclassif": 110, "2400": 110, "52e0": 110, "1a00": 110, "940": 110, "982975": 110, "960k": 110, "959": 110, "94k": 110, "inflat": 110, "17045998": 110, "16m": 110, "octet": 110, "26m": 110, "2mb": 110, "bert": 110, "read_npz": 110, "filepath": 110, "corrsespond": 110, "iob2": 110, "given_ent": 110, "entity_map": 110, "readfil": 110, "startswith": 110, "docstart": 110, "isalpha": 110, "isupp": 110, "indices_to_preview": 110, "nsentenc": 110, "eu": 110, "reject": 110, "boycott": 110, "british": 110, "lamb": 110, "00030412": 110, "00023826": 110, "99936208": 110, "00007009": 110, "00002545": 110, "99998795": 110, "00000401": 110, "00000218": 110, "00000455": 110, "00000131": 110, "00000749": 110, "99996115": 110, "00001371": 110, "0000087": 110, "00000895": 110, "99998936": 110, "00000382": 110, "00000178": 110, "00000366": 110, "00000137": 110, "99999101": 110, "00000266": 110, "00000174": 110, "0000035": 110, "00000109": 110, "99998768": 110, "00000482": 110, "00000202": 110, "00000438": 110, "0000011": 110, "00000465": 110, "99996392": 110, "00001105": 110, "0000116": 110, "00000878": 110, "99998671": 110, "00000364": 110, "00000213": 110, "00000472": 110, "00000281": 110, "99999073": 110, "00000211": 110, "00000159": 110, "00000442": 110, "00000115": 110, "peter": 110, "blackburn": 110, "00000358": 110, "00000529": 110, "99995623": 110, "0000129": 110, "0000024": 110, "00001812": 110, "99994141": 110, "00001645": 110, "00002162": 110, "brussel": 110, "1996": 110, "00001172": 110, "00000821": 110, "00004661": 110, "0000618": 110, "99987167": 110, "99999061": 110, "00000201": 110, "00000195": 110, "00000408": 110, "00000135": 110, "2254": 110, "2907": 110, "19392": 110, "9962": 110, "8904": 110, "19303": 110, "12918": 110, "9256": 110, "11855": 110, "18392": 110, "20426": 110, "19402": 110, "14744": 110, "19371": 110, "4645": 110, "10331": 110, "9430": 110, "6143": 110, "18367": 110, "12914": 110, "todai": 110, "weather": 110, "march": 110, "scalfaro": 110, "northern": 110, "himself": 110, "said": 110, "germani": 110, "nastja": 110, "rysich": 110, "north": 110, "spla": 110, "fought": 110, "khartoum": 110, "govern": 110, "south": 110, "1983": 110, "autonomi": 110, "animist": 110, "region": 110, "moslem": 110, "arabis": 110, "mayor": 110, "antonio": 110, "gonzalez": 110, "garcia": 110, "revolutionari": 110, "wednesdai": 110, "troop": 110, "raid": 110, "farm": 110, "stole": 110, "rape": 110, "women": 110, "spring": 110, "chg": 110, "hrw": 110, "12pct": 110, "princ": 110, "photo": 110, "moment": 110, "spokeswoman": 110, "rainier": 110, "told": 110, "reuter": 110, "danila": 110, "carib": 110, "w224": 110, "equip": 110, "radiomet": 110, "earn": 110, "19996": 110, "london": 110, "denom": 110, "sale": 110, "uk": 110, "jp": 110, "fr": 110, "maccabi": 110, "hapoel": 110, "haifa": 110, "tel": 110, "aviv": 110, "hospit": 110, "rever": 110, "roman": 110, "cathol": 110, "nun": 110, "admit": 110, "calcutta": 110, "week": 110, "ago": 110, "fever": 110, "vomit": 110, "allianc": 110, "embattl": 110, "kabul": 110, "salang": 110, "highwai": 110, "mondai": 110, "tuesdai": 110, "suprem": 110, "council": 110, "led": 110, "jumbish": 110, "milli": 110, "movement": 110, "warlord": 110, "abdul": 110, "rashid": 110, "dostum": 110, "dollar": 110, "exchang": 110, "3570": 110, "12049": 110, "born": 110, "1937": 110, "provinc": 110, "anhui": 110, "dai": 110, "shanghai": 110, "citi": 110, "prolif": 110, "author": 110, "teacher": 110, "chines": 110, "16764": 110, "1990": 110, "historian": 110, "alan": 110, "john": 110, "percival": 110, "taylor": 110, "di": 110, "20446": 110, "pace": 110, "bowler": 110, "ian": 110, "harvei": 110, "claim": 110, "victoria": 110, "15514": 110, "cotti": 110, "osc": 110, "foreign": 110, "minist": 110, "7525": 110, "sultan": 110, "specter": 110, "crown": 110, "abdullah": 110, "defenc": 110, "aviat": 110, "jeddah": 110, "saudi": 110, "agenc": 110, "2288": 110, "hi": 110, "customari": 110, "outfit": 110, "champion": 110, "damp": 110, "scalp": 110, "canada": 110, "reign": 110, "olymp": 110, "donovan": 110, "bailei": 110, "1992": 110, "linford": 110, "christi": 110, "britain": 110, "1984": 110, "1988": 110, "carl": 110, "lewi": 110, "ambigi": 110, "punctuat": 110, "chicago": 110, "digest": 110, "philadelphia": 110, "usda": 110, "york": 110, "token_issu": 110, "471": 110, "kean": 110, "year": 110, "contract": 110, "manchest": 110, "19072": 110, "societi": 110, "bite": 110, "deliv": 110, "19910": 110, "father": 110, "clarenc": 110, "woolmer": 110, "renam": 110, "uttar": 110, "pradesh": 110, "india": 110, "ranji": 110, "trophi": 110, "nation": 110, "championship": 110, "captain": 110, "1949": 110, "15658": 110, "19879": 110, "iii": 110, "brian": 110, "shimer": 110, "randi": 110, "jone": 110, "19104": 110}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [39, 0, 0, "-", "dataset"], [42, 0, 0, "-", "experimental"], [46, 0, 0, "-", "filter"], [47, 0, 0, "-", "internal"], [61, 0, 0, "-", "models"], [63, 0, 0, "-", "multiannotator"], [66, 0, 0, "-", "multilabel_classification"], [69, 0, 0, "-", "object_detection"], [72, 0, 0, "-", "outlier"], [73, 0, 0, "-", "rank"], [74, 0, 0, "-", "regression"], [78, 0, 0, "-", "segmentation"], [82, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.data_valuation": [[4, 1, 1, "", "data_shapley_knn"]], "cleanlab.datalab": [[5, 0, 0, "-", "datalab"], [18, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[5, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[5, 4, 1, "", "class_names"], [5, 3, 1, "", "find_issues"], [5, 3, 1, "", "get_info"], [5, 3, 1, "", "get_issue_summary"], [5, 3, 1, "", "get_issues"], [5, 4, 1, "", "has_labels"], [5, 4, 1, "", "info"], [5, 4, 1, "", "issue_summary"], [5, 4, 1, "", "issues"], [5, 4, 1, "", "labels"], [5, 3, 1, "", "list_default_issue_types"], [5, 3, 1, "", "list_possible_issue_types"], [5, 3, 1, "", "load"], [5, 3, 1, "", "report"], [5, 3, 1, "", "save"]], "cleanlab.datalab.internal.adapter": [[13, 0, 0, "-", "imagelab"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, 2, 1, "", "CorrelationReporter"], [13, 2, 1, "", "CorrelationVisualizer"], [13, 2, 1, "", "ImagelabDataIssuesAdapter"], [13, 2, 1, "", "ImagelabIssueFinderAdapter"], [13, 2, 1, "", "ImagelabReporterAdapter"], [13, 1, 1, "", "create_imagelab"], [13, 1, 1, "", "handle_spurious_correlations"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter": [[13, 3, 1, "", "report"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer": [[13, 3, 1, "", "visualize"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter": [[13, 3, 1, "", "collect_issues_from_imagelab"], [13, 3, 1, "", "collect_issues_from_issue_manager"], [13, 3, 1, "", "collect_statistics"], [13, 3, 1, "", "filter_based_on_max_prevalence"], [13, 3, 1, "", "get_info"], [13, 3, 1, "", "get_issue_summary"], [13, 3, 1, "", "get_issues"], [13, 3, 1, "", "set_health_score"], [13, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter": [[13, 3, 1, "", "find_issues"], [13, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter": [[13, 3, 1, "", "get_report"], [13, 3, 1, "", "report"]], "cleanlab.datalab.internal": [[15, 0, 0, "-", "data"], [16, 0, 0, "-", "data_issues"], [19, 0, 0, "-", "issue_finder"], [17, 0, 0, "-", "issue_manager_factory"], [35, 0, 0, "-", "model_outputs"], [36, 0, 0, "-", "report"], [37, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[15, 2, 1, "", "Data"], [15, 5, 1, "", "DataFormatError"], [15, 5, 1, "", "DatasetDictError"], [15, 5, 1, "", "DatasetLoadError"], [15, 2, 1, "", "Label"], [15, 2, 1, "", "MultiClass"], [15, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[16, 2, 1, "", "DataIssues"], [16, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[16, 3, 1, "", "collect_issues_from_imagelab"], [16, 3, 1, "", "collect_issues_from_issue_manager"], [16, 3, 1, "", "collect_statistics"], [16, 3, 1, "", "get_info"], [16, 3, 1, "", "get_issue_summary"], [16, 3, 1, "", "get_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_summary"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "set_health_score"], [16, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[19, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[19, 3, 1, "", "find_issues"], [19, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[21, 0, 0, "-", "data_valuation"], [22, 0, 0, "-", "duplicate"], [23, 0, 0, "-", "imbalance"], [25, 0, 0, "-", "issue_manager"], [26, 0, 0, "-", "label"], [29, 0, 0, "-", "noniid"], [30, 0, 0, "-", "null"], [31, 0, 0, "-", "outlier"], [34, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[21, 6, 1, "", "DEFAULT_THRESHOLD"], [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.duplicate": [[22, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[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, 6, 1, "", "near_duplicate_sets"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[25, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[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.label": [[26, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 3, 1, "", "get_health_summary"], [26, 6, 1, "", "health_summary_parameters"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[28, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[28, 3, 1, "", "collect_info"], [28, 6, 1, "", "description"], [28, 3, 1, "", "find_issues"], [28, 6, 1, "", "info"], [28, 6, 1, "", "issue_name"], [28, 6, 1, "", "issue_score_key"], [28, 6, 1, "", "issues"], [28, 3, 1, "", "make_summary"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[29, 2, 1, "", "NonIIDIssueManager"], [29, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[30, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[30, 3, 1, "", "collect_info"], [30, 6, 1, "", "description"], [30, 3, 1, "", "find_issues"], [30, 6, 1, "", "info"], [30, 6, 1, "", "issue_name"], [30, 6, 1, "", "issue_score_key"], [30, 6, 1, "", "issues"], [30, 3, 1, "", "make_summary"], [30, 3, 1, "", "report"], [30, 6, 1, "", "summary"], [30, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[31, 6, 1, "", "DEFAULT_THRESHOLDS"], [31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "find_issues"], [31, 6, 1, "", "info"], [31, 6, 1, "", "issue_name"], [31, 6, 1, "", "issue_score_key"], [31, 6, 1, "", "issues"], [31, 3, 1, "", "make_summary"], [31, 6, 1, "", "metric"], [31, 6, 1, "", "ood"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[33, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, 2, 1, "", "RegressionLabelIssueManager"], [33, 1, 1, "", "find_issues_with_features"], [33, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[33, 3, 1, "", "collect_info"], [33, 6, 1, "", "description"], [33, 3, 1, "", "find_issues"], [33, 6, 1, "", "info"], [33, 6, 1, "", "issue_name"], [33, 6, 1, "", "issue_score_key"], [33, 6, 1, "", "issues"], [33, 3, 1, "", "make_summary"], [33, 3, 1, "", "report"], [33, 6, 1, "", "summary"], [33, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[34, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [34, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [34, 3, 1, "", "collect_info"], [34, 6, 1, "", "description"], [34, 3, 1, "", "filter_cluster_ids"], [34, 3, 1, "", "find_issues"], [34, 3, 1, "", "get_underperforming_clusters"], [34, 6, 1, "", "info"], [34, 6, 1, "", "issue_name"], [34, 6, 1, "", "issue_score_key"], [34, 6, 1, "", "issues"], [34, 3, 1, "", "make_summary"], [34, 3, 1, "", "perform_clustering"], [34, 3, 1, "", "report"], [34, 6, 1, "", "summary"], [34, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, 7, 1, "", "REGISTRY"], [17, 1, 1, "", "list_default_issue_types"], [17, 1, 1, "", "list_possible_issue_types"], [17, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[35, 2, 1, "", "ModelOutput"], [35, 2, 1, "", "MultiClassPredProbs"], [35, 2, 1, "", "MultiLabelPredProbs"], [35, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[36, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[36, 3, 1, "", "get_report"], [36, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[37, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[37, 6, 1, "", "CLASSIFICATION"], [37, 6, 1, "", "MULTILABEL"], [37, 6, 1, "", "REGRESSION"], [37, 3, 1, "", "__contains__"], [37, 3, 1, "", "__getitem__"], [37, 3, 1, "", "__iter__"], [37, 3, 1, "", "__len__"], [37, 3, 1, "", "from_str"], [37, 4, 1, "", "is_classification"], [37, 4, 1, "", "is_multilabel"], [37, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[39, 1, 1, "", "find_overlapping_classes"], [39, 1, 1, "", "health_summary"], [39, 1, 1, "", "overall_label_health_score"], [39, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[40, 0, 0, "-", "cifar_cnn"], [41, 0, 0, "-", "coteaching"], [43, 0, 0, "-", "label_issues_batched"], [44, 0, 0, "-", "mnist_pytorch"], [45, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[40, 2, 1, "", "CNN"], [40, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[40, 6, 1, "", "T_destination"], [40, 3, 1, "", "__call__"], [40, 3, 1, "", "add_module"], [40, 3, 1, "", "apply"], [40, 3, 1, "", "bfloat16"], [40, 3, 1, "", "buffers"], [40, 6, 1, "", "call_super_init"], [40, 3, 1, "", "children"], [40, 3, 1, "", "compile"], [40, 3, 1, "", "cpu"], [40, 3, 1, "", "cuda"], [40, 3, 1, "", "double"], [40, 6, 1, "", "dump_patches"], [40, 3, 1, "", "eval"], [40, 3, 1, "", "extra_repr"], [40, 3, 1, "", "float"], [40, 3, 1, "id0", "forward"], [40, 3, 1, "", "get_buffer"], [40, 3, 1, "", "get_extra_state"], [40, 3, 1, "", "get_parameter"], [40, 3, 1, "", "get_submodule"], [40, 3, 1, "", "half"], [40, 3, 1, "", "ipu"], [40, 3, 1, "", "load_state_dict"], [40, 3, 1, "", "modules"], [40, 3, 1, "", "named_buffers"], [40, 3, 1, "", "named_children"], [40, 3, 1, "", "named_modules"], [40, 3, 1, "", "named_parameters"], [40, 3, 1, "", "parameters"], [40, 3, 1, "", "register_backward_hook"], [40, 3, 1, "", "register_buffer"], [40, 3, 1, "", "register_forward_hook"], [40, 3, 1, "", "register_forward_pre_hook"], [40, 3, 1, "", "register_full_backward_hook"], [40, 3, 1, "", "register_full_backward_pre_hook"], [40, 3, 1, "", "register_load_state_dict_post_hook"], [40, 3, 1, "", "register_module"], [40, 3, 1, "", "register_parameter"], [40, 3, 1, "", "register_state_dict_pre_hook"], [40, 3, 1, "", "requires_grad_"], [40, 3, 1, "", "set_extra_state"], [40, 3, 1, "", "share_memory"], [40, 3, 1, "", "state_dict"], [40, 3, 1, "", "to"], [40, 3, 1, "", "to_empty"], [40, 3, 1, "", "train"], [40, 6, 1, "", "training"], [40, 3, 1, "", "type"], [40, 3, 1, "", "xpu"], [40, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[41, 1, 1, "", "adjust_learning_rate"], [41, 1, 1, "", "evaluate"], [41, 1, 1, "", "forget_rate_scheduler"], [41, 1, 1, "", "initialize_lr_scheduler"], [41, 1, 1, "", "loss_coteaching"], [41, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[43, 2, 1, "", "LabelInspector"], [43, 7, 1, "", "adj_confident_thresholds_shared"], [43, 1, 1, "", "find_label_issues_batched"], [43, 7, 1, "", "labels_shared"], [43, 7, 1, "", "pred_probs_shared"], [43, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[43, 3, 1, "", "get_confident_thresholds"], [43, 3, 1, "", "get_label_issues"], [43, 3, 1, "", "get_num_issues"], [43, 3, 1, "", "get_quality_scores"], [43, 3, 1, "", "score_label_quality"], [43, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[44, 2, 1, "", "CNN"], [44, 2, 1, "", "SimpleNet"], [44, 1, 1, "", "get_mnist_dataset"], [44, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[44, 3, 1, "", "__init_subclass__"], [44, 6, 1, "", "batch_size"], [44, 6, 1, "", "dataset"], [44, 6, 1, "", "epochs"], [44, 3, 1, "id0", "fit"], [44, 3, 1, "", "get_metadata_routing"], [44, 3, 1, "", "get_params"], [44, 6, 1, "", "loader"], [44, 6, 1, "", "log_interval"], [44, 6, 1, "", "lr"], [44, 6, 1, "", "momentum"], [44, 6, 1, "", "no_cuda"], [44, 3, 1, "id1", "predict"], [44, 3, 1, "id4", "predict_proba"], [44, 6, 1, "", "seed"], [44, 3, 1, "", "set_fit_request"], [44, 3, 1, "", "set_params"], [44, 3, 1, "", "set_predict_proba_request"], [44, 3, 1, "", "set_predict_request"], [44, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[44, 6, 1, "", "T_destination"], [44, 3, 1, "", "__call__"], [44, 3, 1, "", "add_module"], [44, 3, 1, "", "apply"], [44, 3, 1, "", "bfloat16"], [44, 3, 1, "", "buffers"], [44, 6, 1, "", "call_super_init"], [44, 3, 1, "", "children"], [44, 3, 1, "", "compile"], [44, 3, 1, "", "cpu"], [44, 3, 1, "", "cuda"], [44, 3, 1, "", "double"], [44, 6, 1, "", "dump_patches"], [44, 3, 1, "", "eval"], [44, 3, 1, "", "extra_repr"], [44, 3, 1, "", "float"], [44, 3, 1, "", "forward"], [44, 3, 1, "", "get_buffer"], [44, 3, 1, "", "get_extra_state"], [44, 3, 1, "", "get_parameter"], [44, 3, 1, "", "get_submodule"], [44, 3, 1, "", "half"], [44, 3, 1, "", "ipu"], [44, 3, 1, "", "load_state_dict"], [44, 3, 1, "", "modules"], [44, 3, 1, "", "named_buffers"], [44, 3, 1, "", "named_children"], [44, 3, 1, "", "named_modules"], [44, 3, 1, "", "named_parameters"], [44, 3, 1, "", "parameters"], [44, 3, 1, "", "register_backward_hook"], [44, 3, 1, "", "register_buffer"], [44, 3, 1, "", "register_forward_hook"], [44, 3, 1, "", "register_forward_pre_hook"], [44, 3, 1, "", "register_full_backward_hook"], [44, 3, 1, "", "register_full_backward_pre_hook"], [44, 3, 1, "", "register_load_state_dict_post_hook"], [44, 3, 1, "", "register_module"], [44, 3, 1, "", "register_parameter"], [44, 3, 1, "", "register_state_dict_pre_hook"], [44, 3, 1, "", "requires_grad_"], [44, 3, 1, "", "set_extra_state"], [44, 3, 1, "", "share_memory"], [44, 3, 1, "", "state_dict"], [44, 3, 1, "", "to"], [44, 3, 1, "", "to_empty"], [44, 3, 1, "", "train"], [44, 6, 1, "", "training"], [44, 3, 1, "", "type"], [44, 3, 1, "", "xpu"], [44, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[45, 1, 1, "", "display_issues"], [45, 1, 1, "", "find_label_issues"], [45, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[46, 1, 1, "", "find_label_issues"], [46, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [46, 1, 1, "", "find_predicted_neq_given"], [46, 7, 1, "", "pred_probs_by_class"], [46, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[48, 0, 0, "-", "label_quality_utils"], [49, 0, 0, "-", "latent_algebra"], [50, 0, 0, "-", "multiannotator_utils"], [51, 0, 0, "-", "multilabel_scorer"], [52, 0, 0, "-", "multilabel_utils"], [53, 0, 0, "-", "neighbor"], [57, 0, 0, "-", "outlier"], [58, 0, 0, "-", "token_classification_utils"], [59, 0, 0, "-", "util"], [60, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[48, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, 1, 1, "", "compute_inv_noise_matrix"], [49, 1, 1, "", "compute_noise_matrix_from_inverse"], [49, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [49, 1, 1, "", "compute_py"], [49, 1, 1, "", "compute_py_inv_noise_matrix"], [49, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[50, 1, 1, "", "assert_valid_inputs_multiannotator"], [50, 1, 1, "", "assert_valid_pred_probs"], [50, 1, 1, "", "check_consensus_label_classes"], [50, 1, 1, "", "compute_soft_cross_entropy"], [50, 1, 1, "", "find_best_temp_scaler"], [50, 1, 1, "", "format_multiannotator_labels"], [50, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[51, 2, 1, "", "Aggregator"], [51, 2, 1, "", "ClassLabelScorer"], [51, 2, 1, "", "MultilabelScorer"], [51, 1, 1, "", "exponential_moving_average"], [51, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [51, 1, 1, "", "get_label_quality_scores"], [51, 1, 1, "", "multilabel_py"], [51, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[51, 3, 1, "", "__call__"], [51, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[51, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [51, 6, 1, "", "NORMALIZED_MARGIN"], [51, 6, 1, "", "SELF_CONFIDENCE"], [51, 3, 1, "", "__call__"], [51, 3, 1, "", "__contains__"], [51, 3, 1, "", "__getitem__"], [51, 3, 1, "", "__iter__"], [51, 3, 1, "", "__len__"], [51, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[51, 3, 1, "", "__call__"], [51, 3, 1, "", "aggregate"], [51, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[52, 1, 1, "", "get_onehot_num_classes"], [52, 1, 1, "", "int2onehot"], [52, 1, 1, "", "onehot2int"], [52, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[54, 0, 0, "-", "knn_graph"], [55, 0, 0, "-", "metric"], [56, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[54, 7, 1, "", "DEFAULT_K"], [54, 1, 1, "", "construct_knn_graph_from_index"], [54, 1, 1, "", "correct_knn_distances_and_indices"], [54, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [54, 1, 1, "", "correct_knn_graph"], [54, 1, 1, "", "create_knn_graph_and_index"], [54, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[55, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [55, 7, 1, "", "ROW_COUNT_CUTOFF"], [55, 1, 1, "", "decide_default_metric"], [55, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[57, 1, 1, "", "correct_precision_errors"], [57, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, 1, 1, "", "color_sentence"], [58, 1, 1, "", "filter_sentence"], [58, 1, 1, "", "get_sentence"], [58, 1, 1, "", "mapping"], [58, 1, 1, "", "merge_probs"], [58, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[59, 1, 1, "", "append_extra_datapoint"], [59, 1, 1, "", "clip_noise_rates"], [59, 1, 1, "", "clip_values"], [59, 1, 1, "", "compress_int_array"], [59, 1, 1, "", "confusion_matrix"], [59, 1, 1, "", "csr_vstack"], [59, 1, 1, "", "estimate_pu_f1"], [59, 1, 1, "", "extract_indices_tf"], [59, 1, 1, "", "force_two_dimensions"], [59, 1, 1, "", "format_labels"], [59, 1, 1, "", "get_missing_classes"], [59, 1, 1, "", "get_num_classes"], [59, 1, 1, "", "get_unique_classes"], [59, 1, 1, "", "is_tensorflow_dataset"], [59, 1, 1, "", "is_torch_dataset"], [59, 1, 1, "", "num_unique_classes"], [59, 1, 1, "", "print_inverse_noise_matrix"], [59, 1, 1, "", "print_joint_matrix"], [59, 1, 1, "", "print_noise_matrix"], [59, 1, 1, "", "print_square_matrix"], [59, 1, 1, "", "remove_noise_from_class"], [59, 1, 1, "", "round_preserving_row_totals"], [59, 1, 1, "", "round_preserving_sum"], [59, 1, 1, "", "smart_display_dataframe"], [59, 1, 1, "", "subset_X_y"], [59, 1, 1, "", "subset_data"], [59, 1, 1, "", "subset_labels"], [59, 1, 1, "", "train_val_split"], [59, 1, 1, "", "unshuffle_tensorflow_dataset"], [59, 1, 1, "", "value_counts"], [59, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[60, 1, 1, "", "assert_indexing_works"], [60, 1, 1, "", "assert_nonempty_input"], [60, 1, 1, "", "assert_valid_class_labels"], [60, 1, 1, "", "assert_valid_inputs"], [60, 1, 1, "", "labels_to_array"], [60, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[62, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[62, 2, 1, "", "KerasWrapperModel"], [62, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[63, 1, 1, "", "convert_long_to_wide_dataset"], [63, 1, 1, "", "get_active_learning_scores"], [63, 1, 1, "", "get_active_learning_scores_ensemble"], [63, 1, 1, "", "get_label_quality_multiannotator"], [63, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [63, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[64, 0, 0, "-", "dataset"], [65, 0, 0, "-", "filter"], [67, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[64, 1, 1, "", "common_multilabel_issues"], [64, 1, 1, "", "multilabel_health_summary"], [64, 1, 1, "", "overall_multilabel_health_score"], [64, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, 1, 1, "", "find_label_issues"], [65, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[67, 1, 1, "", "get_label_quality_scores"], [67, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[68, 0, 0, "-", "filter"], [70, 0, 0, "-", "rank"], [71, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[68, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[70, 1, 1, "", "compute_badloc_box_scores"], [70, 1, 1, "", "compute_overlooked_box_scores"], [70, 1, 1, "", "compute_swap_box_scores"], [70, 1, 1, "", "get_label_quality_scores"], [70, 1, 1, "", "issues_from_scores"], [70, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[71, 1, 1, "", "bounding_box_size_distribution"], [71, 1, 1, "", "calculate_per_class_metrics"], [71, 1, 1, "", "class_label_distribution"], [71, 1, 1, "", "get_average_per_class_confusion_matrix"], [71, 1, 1, "", "get_sorted_bbox_count_idxs"], [71, 1, 1, "", "object_counts_per_image"], [71, 1, 1, "", "plot_class_distribution"], [71, 1, 1, "", "plot_class_size_distributions"], [71, 1, 1, "", "visualize"]], "cleanlab.outlier": [[72, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[72, 3, 1, "", "fit"], [72, 3, 1, "", "fit_score"], [72, 3, 1, "", "score"]], "cleanlab.rank": [[73, 1, 1, "", "find_top_issues"], [73, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [73, 1, 1, "", "get_label_quality_ensemble_scores"], [73, 1, 1, "", "get_label_quality_scores"], [73, 1, 1, "", "get_normalized_margin_for_each_label"], [73, 1, 1, "", "get_self_confidence_for_each_label"], [73, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[75, 0, 0, "-", "learn"], [76, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[75, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[75, 3, 1, "", "__init_subclass__"], [75, 3, 1, "", "find_label_issues"], [75, 3, 1, "", "fit"], [75, 3, 1, "", "get_aleatoric_uncertainty"], [75, 3, 1, "", "get_epistemic_uncertainty"], [75, 3, 1, "", "get_label_issues"], [75, 3, 1, "", "get_metadata_routing"], [75, 3, 1, "", "get_params"], [75, 3, 1, "", "predict"], [75, 3, 1, "", "save_space"], [75, 3, 1, "", "score"], [75, 3, 1, "", "set_fit_request"], [75, 3, 1, "", "set_params"], [75, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[76, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[77, 0, 0, "-", "filter"], [79, 0, 0, "-", "rank"], [80, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[77, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[79, 1, 1, "", "get_label_quality_scores"], [79, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[80, 1, 1, "", "common_label_issues"], [80, 1, 1, "", "display_issues"], [80, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[81, 0, 0, "-", "filter"], [83, 0, 0, "-", "rank"], [84, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[81, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[83, 1, 1, "", "get_label_quality_scores"], [83, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[84, 1, 1, "", "common_label_issues"], [84, 1, 1, "", "display_issues"], [84, 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, 88, 89, 93, 95, 96, 99, 101, 104, 110], "count": [3, 101], "data_valu": [4, 21], "datalab": [5, 7, 9, 10, 12, 90, 91, 92, 93, 94, 95, 96, 97, 99, 101, 104], "creat": [7, 91, 92, 101, 103], "your": [7, 85, 91, 92, 96, 97, 99, 101], "own": 7, "issu": [7, 9, 10, 24, 33, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "manag": [7, 24], "prerequisit": 7, "implement": 7, "issuemanag": [7, 91], "basic": 7, "check": [7, 85, 97, 100], "intermedi": 7, "advanc": [7, 91], "us": [7, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "gener": [8, 97], "cluster": [8, 97, 99], "id": 8, "guid": [9, 12], "type": [9, 10, 101], "custom": [9, 91], "cleanlab": [9, 10, 85, 88, 89, 90, 93, 95, 96, 99, 101, 103, 104, 105, 106, 108, 109, 110], "studio": [9, 10], "easi": [9, 10, 85, 93], "mode": [9, 10, 85, 93], "can": [10, 92, 98, 99, 101, 103], "detect": [10, 90, 92, 93, 95, 96, 97, 99, 101, 105, 106], "estim": [10, 101, 103, 104], "each": 10, "input": 10, "label": [10, 26, 28, 33, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 101, 103, 104, 105, 108, 109, 110], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 31, 57, 72, 93, 95, 96, 104, 106], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 92, 93, 95, 96], "duplic": [10, 22, 92, 93, 95, 96, 99, 104], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 96, 97], "iid": [10, 96, 97], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 86, 97, 101, 109], "imbal": [10, 23, 97], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 93, 97, 106], "specif": [10, 24, 109], "spuriou": [10, 97], "correl": [10, 97], "between": 10, "properti": 10, "score": [10, 97, 101, 103, 104, 105, 109, 110], "underperform": [10, 97, 99], "group": [10, 97, 99], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 30, 97], "is_null_issu": 10, "null_scor": 10, "data": [10, 15, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "valuat": [10, 97], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 97], "paramet": [10, 101], "get": [12, 91, 92, 103, 104, 105, 109, 110], "start": [12, 98], "api": 12, "refer": 12, "imagelab": 13, "adapt": 14, "data_issu": 16, "factori": 17, "intern": [18, 47], "issue_find": 19, "issue_manag": [24, 25], "regist": 24, "ml": [24, 99, 100, 101], "task": [24, 37], "multilabel": 27, "noniid": 29, "regress": [32, 74, 75, 76, 99, 108], "prioriti": 33, "order": 33, "find": [33, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "underperforming_group": 34, "model_output": 35, "report": [36, 93], "dataset": [39, 64, 85, 89, 90, 92, 93, 96, 97, 98, 99, 101, 104, 105, 106, 108, 109, 110], "cifar_cnn": 40, "coteach": 41, "experiment": 42, "label_issues_batch": 43, "mnist_pytorch": 44, "span_classif": 45, "filter": [46, 65, 68, 77, 81, 101], "label_quality_util": 48, "latent_algebra": 49, "multiannotator_util": 50, "multilabel_scor": 51, "multilabel_util": 52, "neighbor": 53, "knn_graph": 54, "metric": 55, "search": [56, 91], "token_classification_util": 58, "util": 59, "valid": [60, 93, 107], "model": [61, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108], "kera": 62, "multiannot": [63, 103], "multilabel_classif": 66, "rank": [67, 70, 73, 76, 79, 83, 101], "object_detect": 69, "summari": [71, 80, 84], "learn": [75, 92, 99, 101], "segment": [78, 109], "token_classif": [82, 110], "open": [85, 99], "sourc": [85, 99], "document": 85, "quickstart": 85, "1": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "instal": [85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "2": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [85, 92, 101], "sort": [85, 97], "3": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "handl": [85, 99], "error": [85, 89, 93, 99, 101, 103, 104, 105, 108, 109, 110], "train": [85, 88, 89, 90, 97, 99, 100, 106, 108], "robust": [85, 88, 89, 101, 108], "noisi": [85, 88, 89, 100, 101, 108], "4": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 105, 106, 108], "curat": [85, 100], "fix": [85, 99], "level": [85, 98, 101, 110], "5": [85, 88, 90, 92, 93, 95, 97, 100, 101, 103, 108], "improv": [85, 100, 103], "via": [85, 100, 101, 103], "mani": [85, 101], "other": [85, 103, 105, 108], "techniqu": [85, 100], "contribut": 85, "how": [86, 99, 101, 103, 104, 110], "migrat": 86, "version": 86, "0": 86, "from": [86, 88, 89, 91, 92, 100, 101, 108], "pre": [86, 90, 97, 99, 106], "function": [86, 91], "name": 86, "chang": 86, "modul": [86, 101], "new": 86, "remov": 86, "common": [86, 110], "argument": [86, 91], "variabl": 86, "cleanlearn": [87, 99, 101], "tutori": [87, 94, 98, 100, 102], "structur": 88, "tabular": [88, 95], "requir": [88, 89, 91, 92, 93, 95, 96, 103, 104, 105, 106, 108, 109, 110], "depend": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "load": [88, 89, 90, 91, 92, 95, 96, 97, 108], "process": [88, 95, 106, 108], "select": [88, 95], "comput": [88, 90, 93, 95, 96, 97, 99, 100, 103, 107], "out": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "sampl": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "predict": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 107], "probabl": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 107], "more": [88, 89, 92, 101, 108], "spend": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "too": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "much": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "time": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "qualiti": [88, 89, 92, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108, 109, 110], "text": [89, 96, 97, 110], "format": [89, 96, 99, 104, 105], "defin": [89, 93, 96, 97, 108], "potenti": [89, 103, 108], "an": [90, 93, 99], "audio": 90, "import": [90, 91, 92, 93, 98, 101, 103], "them": [90, 98, 100, 101], "speechbrain": 90, "featur": [90, 93, 106], "fit": 90, "linear": 90, "workflow": [91, 97, 101], "audit": [91, 92], "classifi": [91, 92, 97], "instanti": 91, "object": [91, 105], "increment": 91, "specifi": [91, 99], "nondefault": 91, "save": 91, "ad": 91, "A": 92, "unifi": 92, "kind": [92, 105], "skip": [92, 98, 101, 103], "detail": [92, 98, 101, 103], "about": 92, "addit": 92, "inform": [92, 93], "fetch": [93, 98], "normal": 93, "fashion": 93, "mnist": 93, "prepar": [93, 97], "k": [93, 95, 107], "fold": [93, 107], "cross": [93, 107], "embed": [93, 106], "7": [93, 100, 101], "view": 93, "most": [93, 110], "like": 93, "exampl": [93, 99, 101, 106], "sever": 93, "set": [93, 101], "dark": 93, "top": [93, 109], "low": 93, "numer": 95, "categor": [95, 97], "column": 95, "construct": 95, "nearest": 95, "neighbour": 95, "graph": [95, 97], "drift": [96, 104], "miscellan": 97, "acceler": 97, "knn": 97, "obtain": 97, "identifi": [97, 99, 100, 105], "explan": 97, "vector": 97, "perform": [97, 100], "visual": [97, 101, 105, 106, 109], "synthet": 97, "result": 97, "predefin": 97, "slice": [97, 99], "i": [97, 99, 101, 107], "catch": 97, "valu": 97, "encod": 97, "initi": [97, 103], "6": [97, 100, 101], "run": [97, 99], "analysi": [97, 105], "interpret": 97, "understand": 98, "evalu": [98, 100], "health": [98, 101], "8": [98, 100, 101], "popular": 98, "faq": 99, "what": [99, 101, 107], "do": [99, 101], "infer": 99, "correct": [99, 100], "ha": 99, "flag": 99, "should": 99, "v": [99, 100], "test": [99, 100, 101, 106], "big": 99, "limit": 99, "memori": 99, "why": [99, 100], "isn": 99, "t": 99, "work": [99, 101, 103, 110], "me": 99, "differ": [99, 105], "clean": [99, 100, 101], "final": 99, "hyperparamet": [99, 100], "tune": 99, "onli": 99, "one": [99, 101, 104, 109], "doe": [99, 103, 110], "take": 99, "so": 99, "long": 99, "when": [99, 101], "licens": 99, "under": 99, "answer": 99, "question": 99, "split": 100, "did": 100, "you": [100, 101], "make": 100, "thi": [100, 101], "preprocess": 100, "fundament": 100, "problem": 100, "setup": 100, "origin": 100, "baselin": 100, "manual": 100, "address": 100, "algorithm": 100, "better": [100, 103], "strategi": 100, "optim": 100, "9": 100, "conclus": 100, "The": 101, "centric": 101, "ai": 101, "machin": 101, "find_label_issu": 101, "line": 101, "code": 101, "twenti": 101, "lowest": 101, "see": 101, "now": 101, "let": 101, "": 101, "happen": 101, "we": 101, "merg": 101, "seafoam": 101, "green": 101, "yellow": 101, "re": 101, "One": 101, "rule": 101, "overal": [101, 109], "accur": 101, "directli": 101, "fulli": 101, "character": 101, "nois": 101, "matrix": [101, 104], "joint": 101, "prior": 101, "true": 101, "distribut": 101, "flip": 101, "rate": 101, "ani": 101, "again": 101, "support": 101, "lot": 101, "method": 101, "filter_bi": 101, "automat": 101, "everi": 101, "uniqu": 101, "num_label_issu": 101, "threshold": 101, "found": 101, "Not": 101, "sure": 101, "ensembl": 101, "multipl": [101, 103], "predictor": 101, "consensu": 103, "annot": 103, "major": 103, "vote": 103, "statist": 103, "compar": 103, "inspect": 103, "retrain": 103, "further": 103, "multi": 104, "beyond": 104, "mislabel": [104, 109, 110], "given": 104, "hot": 104, "binari": 104, "without": 104, "applic": 104, "real": 104, "download": [105, 109, 110], "objectlab": 105, "exploratori": 105, "pytorch": 106, "timm": 106, "cifar10": 106, "some": 106, "pred_prob": [106, 109, 110], "wai": 108, "semant": 109, "which": 109, "ar": 109, "commonli": 109, "focus": 109, "token": 110, "word": 110, "sentenc": 110, "contain": 110, "particular": 110}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 58}, "alltitles": {"benchmarking": [[0, "module-cleanlab.benchmarking"]], "noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [21, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Spurious Correlations between image-specific properties and labels": [[10, "spurious-correlations-between-image-specific-properties-and-labels"]], "property": [[10, "property"]], "score": [[10, "score"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Spurious Correlations Issue Parameters": [[10, "spurious-correlations-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "adapter": [[14, "adapter"]], "data": [[15, "module-cleanlab.datalab.internal.data"]], "data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[18, "internal"], [47, "internal"]], "issue_finder": [[19, "issue-finder"]], "duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[24, "issue-manager"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[24, "registered-issue-managers"]], "ML task-specific issue managers": [[24, "ml-task-specific-issue-managers"]], "label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[27, "multilabel"]], "noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[30, "null"]], "outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [57, "module-cleanlab.internal.outlier"], [72, "module-cleanlab.outlier"]], "regression": [[32, "regression"], [74, "regression"]], "Priority Order for finding issues:": [[33, null]], "underperforming_group": [[34, "underperforming-group"]], "model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[36, "report"]], "task": [[37, "task"]], "dataset": [[39, "module-cleanlab.dataset"], [64, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "experimental": [[42, "experimental"]], "label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "filter": [[46, "module-cleanlab.filter"], [65, "module-cleanlab.multilabel_classification.filter"], [68, "filter"], [77, "filter"], [81, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[53, "neighbor"]], "knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "search": [[56, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "util": [[59, "module-cleanlab.internal.util"]], "validation": [[60, "module-cleanlab.internal.validation"]], "models": [[61, "models"]], "keras": [[62, "module-cleanlab.models.keras"]], "multiannotator": [[63, "module-cleanlab.multiannotator"]], "multilabel_classification": [[66, "multilabel-classification"]], "rank": [[67, "module-cleanlab.multilabel_classification.rank"], [70, "module-cleanlab.object_detection.rank"], [73, "module-cleanlab.rank"], [79, "module-cleanlab.segmentation.rank"], [83, "module-cleanlab.token_classification.rank"]], "object_detection": [[69, "object-detection"]], "summary": [[71, "summary"], [80, "module-cleanlab.segmentation.summary"], [84, "module-cleanlab.token_classification.summary"]], "regression.learn": [[75, "module-cleanlab.regression.learn"]], "regression.rank": [[76, "module-cleanlab.regression.rank"]], "segmentation": [[78, "segmentation"]], "token_classification": [[82, "token-classification"]], "cleanlab open-source documentation": [[85, "cleanlab-open-source-documentation"]], "Quickstart": [[85, "quickstart"]], "1. Install cleanlab": [[85, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[85, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[85, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[85, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[85, "improve-your-data-via-many-other-techniques"]], "Contributing": [[85, "contributing"]], "Easy Mode": [[85, "easy-mode"], [93, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[86, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[86, "function-and-class-name-changes"]], "Module name changes": [[86, "module-name-changes"]], "New modules": [[86, "new-modules"]], "Removed modules": [[86, "removed-modules"]], "Common argument and variable name changes": [[86, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[87, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[88, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[88, "1.-Install-required-dependencies"], [89, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [96, "1.-Install-required-dependencies"], [108, "1.-Install-required-dependencies"]], "2. Load and process the data": [[88, "2.-Load-and-process-the-data"], [95, "2.-Load-and-process-the-data"], [108, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[88, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [95, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[88, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[88, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[88, "Spending-too-much-time-on-data-quality?"], [89, "Spending-too-much-time-on-data-quality?"], [92, "Spending-too-much-time-on-data-quality?"], [95, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [98, "Spending-too-much-time-on-data-quality?"], [101, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [106, "Spending-too-much-time-on-data-quality?"], [107, "spending-too-much-time-on-data-quality"], [108, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[89, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[89, "2.-Load-and-format-the-text-dataset"], [96, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[89, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[89, "4.-Train-a-more-robust-model-from-noisy-labels"], [108, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[90, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[90, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[90, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[90, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[90, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[90, "5.-Use-cleanlab-to-find-label-issues"], [95, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[91, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[91, "Install-and-import-required-dependencies"]], "Create and load the data": [[91, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[91, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[91, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[91, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[91, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[91, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[91, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[92, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[92, "1.-Install-and-import-required-dependencies"], [93, "1.-Install-and-import-required-dependencies"], [103, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[92, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[92, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[92, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[92, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[92, "Get-additional-information"]], "Near duplicate issues": [[92, "Near-duplicate-issues"], [93, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[93, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[93, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[93, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[93, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[93, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[93, "7.-Use-cleanlab-to-find-issues"]], "View report": [[93, "View-report"]], "Label issues": [[93, "Label-issues"], [95, "Label-issues"], [96, "Label-issues"]], "View most likely examples with label errors": [[93, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[93, "Outlier-issues"], [95, "Outlier-issues"], [96, "Outlier-issues"]], "View most severe outliers": [[93, "View-most-severe-outliers"]], "View sets of near duplicate images": [[93, "View-sets-of-near-duplicate-images"]], "Dark images": [[93, "Dark-images"]], "View top examples of dark images": [[93, "View-top-examples-of-dark-images"]], "Low information images": [[93, "Low-information-images"]], "Datalab Tutorials": [[94, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[95, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[95, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[95, "Near-duplicate-issues"], [96, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[96, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[96, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[96, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[96, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[97, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[97, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[97, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[97, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[97, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[97, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[97, "Explanation:"]], "Data Valuation": [[97, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[97, "1.-Load-and-Prepare-the-Dataset"], [97, "id2"], [97, "id5"]], "2. Vectorize the Text Data": [[97, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[97, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[97, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[97, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[97, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[97, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [97, "id3"]], "3. (Optional) Cluster the Data": [[97, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[97, "4.-Identify-Underperforming-Groups-with-Datalab"], [97, "id4"]], "5. (Optional) Visualize the Results": [[97, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[97, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[97, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[97, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[97, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[97, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[97, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[97, "1.-Load-the-Dataset"], [97, "id8"]], "2: Encode Categorical Values": [[97, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[97, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[97, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[97, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[97, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[97, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[97, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[97, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[97, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[97, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[97, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[97, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[98, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[98, "Install-dependencies-and-import-them"], [101, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[98, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[98, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[99, "FAQ"]], "What data can cleanlab detect issues in?": [[99, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[99, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[99, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[99, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[99, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[99, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[99, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[99, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[99, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[99, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[99, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[99, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[99, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[99, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[100, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[100, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[100, "1.-Install-dependencies"]], "2. Preprocess the data": [[100, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[100, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[100, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[100, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[100, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[100, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[100, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[100, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[100, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[101, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[101, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[101, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[101, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[101, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[101, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[101, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[101, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[101, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[101, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[101, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[101, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[101, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[101, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[101, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[101, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[101, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[101, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[101, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[101, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[101, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[101, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[102, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[103, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[103, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[103, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[103, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[103, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[103, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[103, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[103, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[103, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[104, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[104, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[104, "2.-Format-data,-labels,-and-model-predictions"], [105, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[104, "3.-Use-cleanlab-to-find-label-issues"], [105, "3.-Use-cleanlab-to-find-label-issues"], [109, "3.-Use-cleanlab-to-find-label-issues"], [110, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[104, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[104, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[104, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[104, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[104, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[105, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[105, "1.-Install-required-dependencies-and-download-data"], [109, "1.-Install-required-dependencies-and-download-data"], [110, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[105, "Get-label-quality-scores"], [109, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[105, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[105, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[105, "Other-uses-of-visualize"]], "Exploratory data analysis": [[105, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[106, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[106, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[106, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[106, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[106, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[106, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[107, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[107, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[107, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[108, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[108, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[108, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[109, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[109, "2.-Get-data,-labels,-and-pred_probs"], [110, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[109, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[109, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[109, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[110, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[110, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[110, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[110, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[110, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.data_valuation"], [5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"], [13, "module-cleanlab.datalab.internal.adapter.imagelab"], [15, "module-cleanlab.datalab.internal.data"], [16, "module-cleanlab.datalab.internal.data_issues"], [17, "module-cleanlab.datalab.internal.issue_manager_factory"], [18, "module-cleanlab.datalab.internal"], [19, "module-cleanlab.datalab.internal.issue_finder"], [21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [22, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [23, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [29, "module-cleanlab.datalab.internal.issue_manager.noniid"], [30, "module-cleanlab.datalab.internal.issue_manager.null"], [31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [35, "module-cleanlab.datalab.internal.model_outputs"], [36, "module-cleanlab.datalab.internal.report"], [37, "module-cleanlab.datalab.internal.task"], [39, "module-cleanlab.dataset"], [40, "module-cleanlab.experimental.cifar_cnn"], [41, "module-cleanlab.experimental.coteaching"], [42, "module-cleanlab.experimental"], [43, "module-cleanlab.experimental.label_issues_batched"], [44, "module-cleanlab.experimental.mnist_pytorch"], [45, "module-cleanlab.experimental.span_classification"], [46, "module-cleanlab.filter"], [47, "module-cleanlab.internal"], [48, "module-cleanlab.internal.label_quality_utils"], [49, "module-cleanlab.internal.latent_algebra"], [50, "module-cleanlab.internal.multiannotator_utils"], [51, "module-cleanlab.internal.multilabel_scorer"], [52, "module-cleanlab.internal.multilabel_utils"], [53, "module-cleanlab.internal.neighbor"], [54, "module-cleanlab.internal.neighbor.knn_graph"], [55, "module-cleanlab.internal.neighbor.metric"], [56, "module-cleanlab.internal.neighbor.search"], [57, "module-cleanlab.internal.outlier"], [58, "module-cleanlab.internal.token_classification_utils"], [59, "module-cleanlab.internal.util"], [60, "module-cleanlab.internal.validation"], [61, "module-cleanlab.models"], [62, "module-cleanlab.models.keras"], [63, "module-cleanlab.multiannotator"], [64, "module-cleanlab.multilabel_classification.dataset"], [65, "module-cleanlab.multilabel_classification.filter"], [66, "module-cleanlab.multilabel_classification"], [67, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.filter"], [69, "module-cleanlab.object_detection"], [70, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.object_detection.summary"], [72, "module-cleanlab.outlier"], [73, "module-cleanlab.rank"], [74, "module-cleanlab.regression"], [75, "module-cleanlab.regression.learn"], [76, "module-cleanlab.regression.rank"], [77, "module-cleanlab.segmentation.filter"], [78, "module-cleanlab.segmentation"], [79, "module-cleanlab.segmentation.rank"], [80, "module-cleanlab.segmentation.summary"], [81, "module-cleanlab.token_classification.filter"], [82, "module-cleanlab.token_classification"], [83, "module-cleanlab.token_classification.rank"], [84, "module-cleanlab.token_classification.summary"]], "cleanlab.benchmarking.noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_n_rand_probabilities_that_sum_to_m"]], "generate_noise_matrix_from_trace() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noise_matrix_from_trace"]], "generate_noisy_labels() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noisy_labels"]], "noise_matrix_is_valid() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.noise_matrix_is_valid"]], "randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_params"]], "predict() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict"]], "predict_proba() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict_proba"]], "save_space() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.save_space"]], "score() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.score"]], "set_fit_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_fit_request"]], "set_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_params"]], "set_score_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_score_request"]], "calibrate_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.calibrate_confident_joint"]], "cleanlab.count": [[3, "module-cleanlab.count"]], "compute_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.compute_confident_joint"]], "estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_confident_joint_and_cv_pred_proba"]], "estimate_cv_predicted_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_cv_predicted_probabilities"]], "estimate_joint() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_joint"]], "estimate_latent() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_latent"]], "estimate_noise_matrices() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_noise_matrices"]], "estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_and_noise_matrices_from_probabilities"]], "estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba"]], "get_confident_thresholds() (in module cleanlab.count)": [[3, "cleanlab.count.get_confident_thresholds"]], "num_label_issues() (in module cleanlab.count)": [[3, "cleanlab.count.num_label_issues"]], "cleanlab.data_valuation": [[4, "module-cleanlab.data_valuation"]], "data_shapley_knn() (in module cleanlab.data_valuation)": [[4, "cleanlab.data_valuation.data_shapley_knn"]], "datalab (class in cleanlab.datalab.datalab)": [[5, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[5, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[5, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[12, "module-cleanlab.datalab"]], "correlationreporter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter"]], "correlationvisualizer (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer"]], "imagelabdataissuesadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter"]], "imagelabissuefinderadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter"]], "imagelabreporteradapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_statistics"]], "create_imagelab() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.create_imagelab"]], "filter_based_on_max_prevalence() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.filter_based_on_max_prevalence"]], "find_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.get_available_issue_types"]], "get_info() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issues"]], "get_report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.get_report"]], "handle_spurious_correlations() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.handle_spurious_correlations"]], "report() (cleanlab.datalab.internal.adapter.imagelab.correlationreporter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter.report"]], "report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.report"]], "set_health_score() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.set_health_score"]], "statistics (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter property)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.statistics"]], "visualize() (cleanlab.datalab.internal.adapter.imagelab.correlationvisualizer method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer.visualize"]], "data (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[15, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[15, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[15, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[15, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[15, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[18, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[19, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[30, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_underperforming_clusters() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_underperforming_clusters"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[36, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[36, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[37, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[37, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[39, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.forward"], [40, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[42, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [44, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [44, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [44, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[46, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[46, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[46, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[47, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[48, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[53, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[56, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[57, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[59, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[60, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[61, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[62, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[63, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[64, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[66, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[67, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[68, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[68, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[69, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[70, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[71, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[72, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[72, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[73, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[74, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[75, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[75, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[75, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[76, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[76, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[77, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[77, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[78, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[79, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[80, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[81, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[81, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[82, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[83, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[84, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.filter_by_token"]]}}) \ No newline at end of file +Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/adapter/imagelab", "cleanlab/datalab/internal/adapter/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/multilabel/index", "cleanlab/datalab/internal/issue_manager/multilabel/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/model_outputs", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/experimental/span_classification", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/datalab/workflows", "tutorials/dataset_health", "tutorials/faq", "tutorials/improving_ml_performance", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/guide/table.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/adapter/imagelab.rst", "cleanlab/datalab/internal/adapter/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/multilabel/index.rst", "cleanlab/datalab/internal/issue_manager/multilabel/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/model_outputs.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/experimental/span_classification.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/neighbor/index.rst", "cleanlab/internal/neighbor/knn_graph.rst", "cleanlab/internal/neighbor/metric.rst", "cleanlab/internal/neighbor/search.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/clean_learning/index.rst", "tutorials/clean_learning/tabular.ipynb", "tutorials/clean_learning/text.ipynb", "tutorials/datalab/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/improving_ml_performance.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "imagelab", "adapter", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "Improving ML Performance via Data Curation with Train vs Test Splits", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 86, 91, 92, 101, 103, 104], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 91, 92, 101, 103, 104], "generate_noise_matrix_from_trac": [0, 1, 91, 92, 101, 103, 104], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 19, 43, 48, 50, 51, 52, 53, 57, 58, 59, 70, 93, 97, 98, 110], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 23, 24, 25, 26, 27, 29, 32, 33, 35, 37, 39, 40, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 85, 86, 91, 98, 107], "benchmark": [1, 40, 85, 86, 91, 92, 101, 103, 104], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 100, 102, 107], "": [1, 2, 3, 4, 10, 21, 35, 39, 40, 44, 48, 51, 54, 56, 57, 59, 63, 64, 68, 70, 71, 72, 73, 75, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "core": [1, 43, 46, 77, 79], "algorithm": [1, 2, 8, 10, 34, 41, 45, 56, 57, 59, 63, 72, 81, 83, 85, 88, 89, 92, 95, 96, 97, 98, 99, 101, 103, 104, 106, 108, 110], "These": [1, 2, 3, 4, 5, 8, 10, 24, 40, 42, 44, 45, 46, 47, 54, 61, 63, 64, 67, 71, 72, 76, 80, 81, 83, 84, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "introduc": [1, 10, 90, 97, 99, 100, 101], "synthet": [1, 103, 104, 109], "nois": [1, 2, 3, 39, 46, 49, 59, 64, 91, 92, 97, 98, 103, 108], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 17, 18, 19, 23, 24, 25, 27, 32, 34, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 91, 97, 100, 102, 106, 107], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 19, 35, 37, 39, 43, 45, 46, 49, 51, 52, 59, 63, 64, 65, 66, 67, 72, 73, 81, 82, 83, 84, 85, 86, 87, 90, 91, 92, 97, 100, 102, 103, 106, 107, 108, 109], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 28, 29, 30, 31, 33, 34, 42, 43, 44, 45, 46, 49, 51, 55, 59, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 91, 95, 100, 102, 103, 107], "specif": [1, 3, 5, 9, 13, 17, 18, 19, 30, 36, 37, 42, 54, 55, 56, 61, 65, 68, 71, 80, 84, 93, 95, 96, 97, 100, 101, 105, 110], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "modul": [1, 3, 10, 13, 14, 16, 17, 18, 19, 24, 27, 32, 35, 36, 37, 39, 40, 41, 42, 43, 44, 46, 51, 53, 54, 56, 57, 59, 61, 63, 68, 71, 72, 73, 85, 93, 99, 104], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 17, 19, 21, 26, 33, 37, 39, 40, 41, 43, 44, 46, 49, 53, 54, 56, 57, 59, 62, 63, 64, 65, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 103, 106, 107, 108, 109, 110], "gener": [1, 2, 3, 7, 10, 21, 26, 28, 36, 39, 51, 54, 56, 59, 60, 72, 73, 75, 80, 89, 90, 91, 92, 93, 96, 98, 99, 100, 101, 103, 104, 106, 107, 109, 110], "valid": [1, 2, 3, 5, 10, 15, 35, 37, 39, 46, 47, 49, 50, 51, 54, 56, 57, 59, 63, 65, 68, 71, 73, 75, 76, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "matric": [1, 3, 49, 99], "which": [1, 2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 21, 25, 29, 35, 36, 37, 39, 40, 44, 45, 46, 49, 51, 55, 56, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "learn": [1, 2, 3, 4, 5, 9, 10, 17, 19, 25, 33, 36, 41, 42, 43, 44, 46, 48, 50, 55, 56, 59, 61, 63, 65, 72, 74, 76, 79, 83, 85, 88, 89, 90, 91, 93, 95, 96, 97, 98, 100, 103, 104, 108], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 103, 104, 105, 106, 108, 109, 110], "possibl": [1, 2, 3, 7, 10, 39, 40, 44, 46, 48, 49, 51, 65, 66, 67, 68, 70, 71, 72, 73, 75, 81, 83, 84, 92, 97, 99, 100, 101, 103, 104, 105, 108, 109, 110], "noisi": [1, 2, 3, 10, 34, 39, 41, 44, 46, 49, 59, 64, 65, 67, 73, 75, 76, 77, 79, 80, 86, 91, 92, 95, 96, 97, 99, 102, 103], "given": [1, 2, 3, 5, 10, 17, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "matrix": [1, 2, 3, 5, 10, 13, 19, 21, 34, 39, 46, 48, 49, 52, 54, 59, 60, 65, 68, 70, 71, 72, 73, 95, 97, 105, 106], "trace": [1, 91, 92, 101, 103, 104], "valu": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 21, 25, 29, 30, 35, 37, 39, 40, 41, 43, 44, 46, 48, 49, 51, 54, 55, 56, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 84, 89, 90, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "more": [1, 2, 3, 4, 5, 7, 9, 10, 13, 16, 17, 19, 21, 29, 39, 40, 43, 44, 45, 48, 51, 54, 55, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 109, 110], "function": [1, 2, 3, 4, 5, 7, 10, 13, 16, 17, 19, 26, 29, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 97, 98, 99, 100, 101, 103, 104, 105, 109, 110], "noise_matrix": [1, 2, 3, 10, 49, 59, 91, 92, 101, 103, 104], "py": [1, 3, 36, 40, 41, 46, 49, 51, 91, 92, 101, 103, 104], "verbos": [1, 2, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 43, 46, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 91, 97, 101, 103], "fals": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 50, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 105, 106, 108, 109], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "prior": [1, 2, 3, 39, 46, 49, 51], "repres": [1, 2, 3, 7, 10, 13, 15, 19, 21, 29, 35, 37, 39, 43, 46, 49, 52, 54, 55, 57, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 110], "p": [1, 2, 3, 5, 10, 39, 46, 48, 49, 57, 59, 63, 71, 72, 73, 77, 95, 96, 97, 100, 101, 103, 110], "true_label": [1, 2, 3, 39, 49, 59, 101, 103], "k": [1, 2, 3, 4, 5, 8, 10, 13, 15, 19, 21, 22, 26, 29, 31, 34, 39, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 66, 67, 68, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 88, 90, 91, 92, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "check": [1, 2, 5, 6, 9, 10, 13, 15, 19, 30, 37, 40, 43, 44, 50, 60, 62, 68, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 104, 108], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 15, 16, 25, 29, 41, 44, 49, 51, 57, 70, 75, 89, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108], "achiev": [1, 2, 40, 41, 44, 75, 99, 100, 103, 110], "better": [1, 5, 10, 46, 55, 63, 65, 73, 75, 76, 85, 89, 90, 92, 95, 96, 97, 99, 101, 104, 105, 106, 107, 110], "than": [1, 2, 3, 4, 7, 9, 10, 29, 31, 34, 39, 46, 55, 59, 62, 63, 68, 70, 72, 73, 75, 79, 83, 88, 90, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "random": [1, 2, 3, 7, 10, 21, 34, 43, 51, 54, 63, 73, 75, 88, 90, 91, 92, 93, 95, 97, 99, 100, 101, 103, 104, 106], "perform": [1, 2, 4, 7, 10, 29, 31, 34, 40, 44, 51, 53, 54, 55, 71, 75, 85, 88, 89, 91, 99, 101, 102, 103, 104, 107, 108], "averag": [1, 3, 5, 10, 25, 31, 39, 40, 44, 51, 57, 63, 64, 71, 72, 73, 99, 103, 106], "amount": [1, 3, 93], "paramet": [1, 2, 3, 4, 5, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 93, 96, 97, 100], "np": [1, 2, 3, 4, 5, 7, 13, 19, 21, 34, 39, 41, 43, 45, 46, 48, 49, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "ndarrai": [1, 2, 3, 4, 5, 13, 19, 26, 28, 29, 33, 34, 35, 39, 41, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 97, 110], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 56, 57, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 85, 88, 89, 91, 92, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 15, 19, 21, 29, 35, 39, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "shape": [1, 2, 3, 4, 5, 13, 19, 21, 39, 41, 43, 45, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 90, 97, 98, 99, 101, 104, 105, 106, 109, 110], "condit": [1, 2, 3, 10, 49, 55, 58, 59, 73, 93, 101, 110], "probabl": [1, 2, 3, 5, 8, 10, 13, 19, 26, 28, 31, 34, 35, 39, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 86, 98, 99, 101, 102, 104, 105, 106, 109, 110], "k_": [1, 2, 3, 49, 59], "k_y": [1, 2, 3, 49, 59], "contain": [1, 2, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 46, 48, 49, 53, 54, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109], "fraction": [1, 2, 3, 10, 23, 41, 49, 59, 63, 75, 95, 99, 100], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 100, 103, 104, 105, 107, 108, 109, 110], "everi": [1, 2, 3, 4, 5, 10, 13, 19, 40, 44, 46, 49, 58, 59, 65, 73, 75, 76, 88, 90, 91, 92, 93, 95, 96, 99, 103, 105, 107, 109, 110], "class": [1, 2, 3, 4, 5, 7, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 56, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 103, 104, 105, 106, 107, 108, 110], "other": [1, 2, 3, 5, 10, 13, 19, 25, 30, 39, 40, 42, 43, 44, 46, 49, 52, 54, 59, 60, 61, 63, 64, 67, 71, 72, 73, 75, 80, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 106, 109, 110], "assum": [1, 2, 3, 15, 46, 49, 54, 58, 59, 73, 77, 80, 97, 99, 100, 104, 106, 108, 109, 110], "column": [1, 2, 3, 5, 10, 11, 13, 15, 16, 33, 39, 43, 46, 49, 51, 52, 55, 58, 59, 63, 64, 65, 67, 68, 71, 72, 73, 75, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "sum": [1, 2, 3, 29, 34, 35, 39, 49, 51, 59, 64, 65, 67, 70, 75, 91, 92, 93, 99, 101, 103, 104, 109, 110], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 98, 99, 107], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 17, 19, 23, 25, 26, 28, 29, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "true": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 51, 54, 58, 59, 60, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "return": [1, 2, 3, 4, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 104, 105, 108, 109, 110], "bool": [1, 2, 3, 5, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 51, 54, 58, 59, 63, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 40, 43, 44, 46, 54, 59, 63, 64, 65, 67, 68, 84, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 108, 110], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 17, 19, 21, 25, 26, 30, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 51, 52, 54, 55, 57, 58, 59, 63, 65, 67, 70, 71, 72, 73, 75, 76, 81, 83, 84, 85, 90, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 109, 110], "perfect": [1, 2, 39, 75, 101, 105], "exactli": [1, 3, 10, 39, 40, 44, 46, 66, 72, 91, 92, 93, 95, 96, 100, 101], "yield": [1, 40, 44, 100], "between": [1, 5, 9, 13, 14, 18, 19, 24, 25, 27, 29, 32, 35, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 54, 55, 56, 57, 61, 63, 64, 67, 70, 72, 73, 75, 76, 79, 83, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "below": [1, 3, 4, 5, 10, 39, 40, 43, 44, 46, 48, 51, 57, 63, 64, 65, 70, 71, 79, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "we": [1, 2, 3, 5, 7, 10, 13, 16, 25, 40, 43, 44, 46, 51, 59, 60, 62, 63, 70, 71, 73, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "loop": [1, 3, 49, 59, 93, 105], "implement": [1, 2, 3, 4, 9, 17, 25, 40, 41, 43, 44, 49, 53, 55, 56, 59, 72, 75, 85, 88, 90, 91, 95, 100, 106, 107], "what": [1, 5, 9, 10, 13, 19, 36, 39, 41, 43, 46, 63, 64, 68, 70, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "doe": [1, 2, 3, 7, 10, 43, 44, 46, 51, 54, 57, 60, 70, 71, 75, 77, 79, 83, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 104, 108, 109], "do": [1, 2, 5, 9, 10, 39, 43, 44, 59, 60, 72, 73, 77, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "fast": 1, "explain": [1, 10, 97], "python": [1, 2, 44, 62, 75, 91, 92, 98, 106], "pseudocod": [1, 107], "happen": [1, 10, 46, 65, 96, 103, 109], "n": [1, 2, 3, 5, 7, 39, 40, 43, 44, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 88, 89, 90, 93, 96, 97, 98, 99, 103, 104, 105, 108, 109, 110], "without": [1, 2, 5, 9, 10, 15, 17, 23, 40, 44, 56, 67, 75, 85, 89, 90, 96, 97, 99, 100, 101, 105, 106], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 48, 50, 57, 58, 59, 62, 63, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109], "distinct": [1, 10, 21, 59, 110], "natur": [1, 10, 103, 106], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 84, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 109, 110], "0": [1, 2, 3, 4, 5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "count_joint": 1, "len": [1, 2, 3, 7, 39, 43, 49, 58, 59, 60, 72, 73, 75, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "y": [1, 2, 3, 5, 8, 21, 33, 34, 44, 49, 51, 59, 60, 62, 71, 75, 76, 89, 90, 91, 92, 95, 97, 99, 101, 103, 104, 106, 108], "round": [1, 43, 46, 59, 75, 97, 99, 100, 108], "astyp": [1, 100, 103], "int": [1, 2, 3, 4, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 41, 43, 44, 46, 51, 52, 54, 55, 56, 57, 58, 59, 60, 64, 65, 67, 71, 72, 73, 75, 77, 79, 80, 81, 84, 90, 91, 93, 97, 100, 105, 106], "rang": [1, 3, 5, 7, 10, 15, 49, 51, 57, 59, 71, 75, 76, 93, 97, 98, 99, 101, 103, 104, 105, 106, 108, 109, 110], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 15, 16, 19, 25, 39, 43, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "pragma": 1, "cover": [1, 3, 86, 97, 98, 99], "choic": [1, 8, 46, 55, 57, 93, 99, 104, 106], "replac": [1, 58, 62, 73, 88, 89, 91, 92, 93, 96, 97, 98, 99, 103, 106], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 54, 73, 90, 91, 92], "05": [1, 10, 29, 33, 58, 71, 75, 81, 83, 95, 98, 99, 100, 101, 105], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 91, 92, 101, 103, 104], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 91, 92, 93, 97, 99, 100, 101, 103, 104, 109], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 29, 42, 44, 51, 75, 88, 90, 91, 92, 95, 97, 98, 100, 101, 103, 104], "max_it": [1, 89, 90, 96, 106], "10000": [1, 43, 98, 99], "x": [1, 2, 3, 5, 10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 40, 41, 44, 46, 48, 49, 51, 54, 56, 58, 59, 60, 62, 63, 65, 71, 72, 73, 75, 77, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 106, 108], "diagon": [1, 3, 5, 46, 49, 59], "equal": [1, 3, 10, 15, 54, 65, 70, 80, 107], "creat": [1, 2, 9, 13, 19, 21, 40, 43, 44, 46, 59, 75, 85, 89, 90, 93, 95, 96, 97, 99, 100, 109, 110], "impli": [1, 10, 39, 64, 71], "float": [1, 2, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 42, 43, 44, 46, 48, 50, 51, 57, 58, 59, 63, 64, 65, 67, 70, 71, 75, 79, 83, 90, 91, 92, 100, 101, 103, 104], "entri": [1, 3, 5, 10, 39, 40, 44, 46, 48, 52, 54, 57, 59, 63, 64, 65, 68, 88, 89, 95, 96, 101, 104, 105, 108], "maximum": [1, 10, 13, 72, 80, 84, 97, 109], "minimum": [1, 8, 10, 13, 23, 46, 48, 65, 70, 83, 97], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 13, 19, 29, 40, 44, 46, 54, 70, 75, 91, 99, 100, 101, 103, 105, 106], "default": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 31, 33, 36, 39, 40, 41, 43, 44, 46, 48, 49, 51, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 91, 93, 97, 99, 108, 109], "If": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 29, 31, 37, 39, 40, 43, 44, 46, 48, 49, 51, 54, 55, 58, 59, 62, 63, 64, 65, 68, 70, 71, 72, 75, 76, 77, 79, 80, 83, 84, 85, 86, 88, 89, 90, 91, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "have": [1, 2, 3, 4, 5, 7, 9, 10, 13, 19, 24, 27, 29, 32, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [1, 2, 3, 5, 7, 8, 9, 10, 13, 16, 17, 19, 25, 36, 39, 40, 43, 44, 45, 46, 49, 51, 52, 54, 58, 59, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "necessari": [1, 2, 3, 4, 7, 10, 15, 58, 91, 97], "In": [1, 2, 3, 5, 10, 39, 40, 43, 44, 54, 62, 63, 64, 66, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 107, 108, 109, 110], "particular": [1, 5, 6, 10, 13, 16, 17, 19, 22, 23, 25, 29, 30, 31, 34, 40, 44, 59, 63, 67, 71, 75, 80, 84, 85, 88, 89, 90, 92, 96, 99, 103, 104, 106, 108], "satisfi": [1, 3, 39], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 15, 33, 38, 40, 41, 42, 43, 44, 46, 49, 54, 56, 59, 61, 62, 65, 72, 73, 75, 77, 85, 86, 90, 97, 98, 99, 100, 101, 107], "argument": [1, 2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 34, 35, 40, 43, 44, 45, 46, 51, 54, 56, 60, 62, 63, 64, 65, 67, 70, 71, 72, 73, 75, 79, 80, 81, 83, 89, 92, 93, 96, 97, 98, 99, 104, 105, 108, 110], "when": [1, 2, 3, 4, 5, 10, 15, 17, 26, 29, 40, 44, 46, 49, 51, 54, 56, 57, 59, 62, 65, 67, 68, 70, 72, 73, 75, 76, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 103, 107, 108, 109, 110], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110], "rate": [1, 2, 3, 10, 41, 59, 90, 110], "set": [1, 2, 3, 5, 9, 10, 13, 15, 16, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 43, 44, 46, 50, 51, 53, 54, 55, 57, 59, 62, 63, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 88, 89, 91, 92, 95, 96, 97, 99, 100, 103, 104, 106, 107, 108, 109, 110], "note": [1, 2, 3, 7, 8, 10, 11, 15, 30, 34, 37, 40, 43, 44, 45, 46, 51, 54, 59, 62, 63, 68, 70, 71, 72, 73, 75, 76, 80, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "you": [1, 2, 3, 5, 7, 9, 10, 13, 17, 19, 39, 40, 42, 43, 44, 46, 51, 56, 61, 62, 63, 65, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "high": [1, 2, 10, 19, 43, 46, 54, 55, 59, 70, 73, 75, 88, 89, 91, 92, 93, 97, 98, 100, 101, 105, 108, 109, 110], "mai": [1, 2, 3, 4, 5, 10, 13, 16, 24, 25, 27, 32, 35, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 63, 64, 68, 70, 71, 72, 73, 75, 77, 80, 84, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "imposs": [1, 10, 101], "also": [1, 2, 3, 5, 7, 9, 10, 25, 37, 39, 40, 43, 44, 46, 51, 58, 62, 63, 72, 75, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "low": [1, 10, 13, 59, 63, 85, 91, 92, 96, 97, 101, 105, 109], "zero": [1, 3, 5, 40, 44, 48, 54, 59, 60, 91, 93, 104, 105, 106], "forc": [1, 2, 3, 5, 44, 91, 110], "instead": [1, 2, 3, 10, 13, 16, 19, 36, 39, 40, 43, 44, 46, 49, 59, 62, 63, 65, 67, 71, 72, 73, 75, 76, 79, 81, 83, 86, 88, 89, 90, 93, 95, 97, 99, 100, 101, 104, 105, 106, 108, 109, 110], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 13, 19, 26, 29, 33, 39, 40, 43, 44, 45, 46, 48, 49, 54, 55, 57, 58, 59, 60, 62, 63, 72, 73, 75, 77, 79, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 100, 103, 104, 105, 106, 107, 108, 109, 110], "guarante": [1, 3, 5, 14, 18, 24, 27, 32, 40, 42, 44, 47, 49, 61, 86], "produc": [1, 2, 5, 9, 10, 13, 19, 51, 63, 73, 75, 77, 79, 85, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "higher": [1, 5, 10, 39, 46, 48, 49, 51, 57, 62, 63, 64, 75, 92, 96, 97, 99, 105], "opposit": [1, 110], "occur": [1, 3, 10, 39, 58, 70, 91, 92, 93, 99, 100, 106], "small": [1, 3, 10, 39, 43, 51, 54, 57, 59, 64, 71, 89, 93, 96, 98, 100, 104, 106], "numpi": [1, 3, 4, 5, 7, 10, 15, 21, 34, 35, 43, 44, 45, 51, 54, 57, 58, 60, 62, 67, 70, 75, 76, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "max": [1, 46, 72, 73, 92, 93, 97, 100, 106], "tri": [1, 40, 44, 107], "befor": [1, 2, 3, 10, 40, 44, 57, 59, 72, 75, 80, 88, 89, 96, 97, 99, 100, 101, 103, 106, 108], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 19, 26, 31, 33, 39, 40, 43, 44, 46, 49, 51, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 90, 91, 92, 93, 95, 99, 101, 104, 108, 109], "left": [1, 2, 46, 48, 57, 59, 65, 68, 71, 91, 92, 104, 105, 106, 109], "stochast": 1, "exceed": 1, "m": [1, 5, 40, 44, 50, 51, 54, 55, 63, 68, 70, 71, 72, 91, 92, 98, 103, 104, 105, 110], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 40, 44, 62, 99, 101, 109], "length": [1, 5, 15, 29, 30, 39, 41, 46, 59, 65, 68, 72, 73, 75, 77, 80, 84, 88, 90, 97, 100, 104, 106, 109, 110], "must": [1, 2, 3, 4, 5, 7, 13, 19, 39, 40, 41, 42, 44, 46, 49, 51, 52, 57, 59, 61, 62, 63, 64, 65, 72, 73, 75, 77, 79, 80, 81, 83, 84, 90, 97, 100, 103, 107, 109, 110], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 15, 39, 43, 46, 52, 59, 60, 63, 65, 71, 77, 79, 80, 81, 83, 84, 88, 89, 90, 99, 100, 103, 104, 105, 109, 110], "ball": [1, 98], "bin": [1, 3, 65, 91, 92, 106], "ensur": [1, 2, 10, 40, 44, 54, 56, 57, 59, 60, 62, 70, 73, 75, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 106, 107, 108], "most": [1, 3, 5, 7, 10, 13, 19, 39, 43, 46, 51, 62, 63, 64, 65, 68, 70, 71, 72, 73, 76, 79, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109], "least": [1, 4, 10, 21, 34, 39, 43, 63, 64, 70, 73, 83, 93, 99, 100, 103, 106, 109], "int_arrai": [1, 59], "can": [2, 3, 4, 5, 7, 8, 9, 13, 16, 17, 19, 36, 37, 39, 40, 41, 42, 43, 44, 46, 50, 51, 52, 54, 55, 56, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 93, 95, 96, 97, 100, 104, 105, 106, 107, 108, 109, 110], "model": [2, 3, 4, 5, 9, 10, 11, 13, 19, 21, 33, 35, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 56, 58, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 102, 107, 109, 110], "For": [2, 3, 5, 7, 9, 10, 12, 13, 19, 25, 38, 39, 40, 43, 44, 46, 49, 51, 54, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 81, 83, 84, 85, 88, 89, 90, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "regular": [2, 3, 43, 62], "multi": [2, 3, 4, 10, 35, 39, 40, 43, 44, 46, 50, 51, 52, 59, 60, 64, 65, 66, 67, 72, 73, 85, 97, 99, 100, 101, 102], "task": [2, 5, 7, 10, 11, 12, 13, 15, 17, 18, 19, 28, 33, 36, 39, 43, 49, 51, 52, 57, 59, 63, 65, 73, 75, 85, 89, 90, 96, 97, 98, 99, 100, 101, 104, 106, 108, 109, 110], "cleanlearn": [2, 3, 10, 26, 33, 40, 59, 62, 74, 75, 76, 85, 86, 88, 89, 100, 108], "wrap": [2, 40, 44, 53, 62, 72, 75, 85, 88, 89, 91, 92, 95, 96, 101, 108], "instanc": [2, 3, 5, 6, 7, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 62, 71, 72, 75, 80, 88, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "sklearn": [2, 3, 4, 5, 8, 10, 21, 34, 39, 44, 51, 55, 56, 59, 62, 72, 75, 76, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 107, 108], "classifi": [2, 3, 44, 51, 59, 63, 66, 72, 73, 85, 86, 88, 89, 90, 95, 96, 99, 103, 104, 106, 107, 109, 110], "adher": [2, 44, 75], "estim": [2, 3, 4, 5, 9, 13, 16, 25, 39, 43, 44, 46, 49, 59, 63, 64, 65, 70, 72, 75, 77, 79, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 102, 105, 106, 107, 108, 109, 110], "api": [2, 3, 17, 62, 68, 71, 72, 75, 86, 97, 99, 108], "defin": [2, 3, 5, 7, 10, 17, 25, 39, 40, 41, 43, 44, 46, 73, 75, 77, 85, 91, 92, 95, 98, 99, 100, 103, 106, 110], "four": [2, 10, 98, 101, 110], "clf": [2, 3, 5, 51, 75, 85, 88, 95, 97, 99, 100, 101, 104], "fit": [2, 3, 5, 8, 10, 21, 42, 44, 54, 56, 61, 62, 72, 74, 75, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 107, 108, 110], "sample_weight": [2, 44, 75, 101], "predict_proba": [2, 5, 39, 42, 44, 51, 61, 62, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 106], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 13, 19, 25, 26, 28, 31, 33, 34, 35, 37, 39, 42, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 89, 98, 99, 101, 102, 106, 108, 109, 110], "score": [2, 3, 4, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 45, 46, 48, 51, 57, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 79, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 106, 108], "data": [2, 3, 4, 5, 7, 8, 9, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 41, 42, 43, 44, 45, 46, 51, 52, 54, 55, 56, 59, 61, 62, 63, 64, 65, 66, 70, 72, 73, 74, 75, 80, 81, 82, 83, 84, 86, 93, 94, 102], "e": [2, 3, 5, 10, 15, 25, 35, 39, 40, 43, 44, 46, 49, 51, 52, 54, 59, 60, 63, 64, 65, 66, 68, 71, 72, 73, 75, 77, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "featur": [2, 3, 4, 5, 8, 10, 11, 13, 19, 21, 22, 26, 29, 30, 31, 33, 34, 51, 54, 55, 56, 59, 72, 75, 85, 88, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 108], "element": [2, 3, 5, 39, 45, 46, 48, 59, 63, 65, 73, 80, 81, 83, 89, 90, 96, 97, 99, 110], "first": [2, 5, 10, 20, 29, 30, 39, 43, 51, 54, 59, 63, 64, 68, 71, 73, 75, 85, 88, 89, 90, 91, 93, 95, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "index": [2, 10, 29, 39, 46, 53, 54, 56, 58, 59, 60, 64, 73, 75, 80, 83, 84, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "should": [2, 3, 5, 7, 10, 17, 25, 29, 34, 35, 39, 40, 43, 44, 46, 48, 49, 51, 54, 56, 57, 58, 59, 62, 63, 64, 67, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "correspond": [2, 3, 5, 10, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 39, 40, 43, 44, 45, 46, 48, 49, 51, 54, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "differ": [2, 5, 7, 10, 13, 14, 16, 18, 24, 27, 29, 30, 32, 39, 40, 42, 43, 44, 46, 47, 51, 54, 57, 59, 60, 61, 63, 68, 70, 72, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 106, 107, 108], "sampl": [2, 3, 5, 8, 10, 13, 19, 23, 34, 46, 48, 51, 54, 55, 56, 65, 68, 71, 73, 75, 76, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 108, 109, 110], "size": [2, 10, 34, 40, 43, 44, 46, 51, 54, 55, 65, 70, 71, 75, 77, 79, 89, 93, 95, 99, 101, 103, 104, 105, 107, 109], "here": [2, 5, 7, 10, 17, 43, 46, 49, 62, 63, 64, 65, 67, 68, 71, 72, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "re": [2, 5, 40, 44, 56, 58, 63, 75, 85, 88, 89, 90, 91, 95, 96, 97, 99, 100, 108, 109, 110], "weight": [2, 10, 40, 41, 44, 51, 54, 63, 70, 73, 75, 89, 90, 91, 92, 96], "loss": [2, 41, 62, 73, 75, 93, 100], "while": [2, 3, 10, 40, 43, 44, 50, 51, 59, 75, 85, 93, 97, 99, 100, 101, 103, 104, 108], "train": [2, 3, 4, 5, 9, 10, 13, 19, 21, 35, 40, 41, 42, 44, 51, 59, 62, 63, 68, 71, 72, 75, 76, 86, 91, 92, 93, 95, 96, 98, 101, 102, 103, 104, 105, 107, 109, 110], "support": [2, 3, 4, 5, 13, 15, 17, 36, 37, 43, 45, 51, 59, 60, 62, 72, 73, 83, 85, 86, 90, 91, 92, 93, 97, 99], "your": [2, 3, 5, 9, 10, 13, 19, 39, 40, 42, 43, 44, 46, 51, 56, 59, 61, 62, 63, 64, 65, 67, 72, 73, 75, 76, 77, 79, 80, 86, 88, 89, 90, 93, 95, 98, 100, 103, 104, 105, 106, 107, 108, 109, 110], "recommend": [2, 5, 7, 10, 13, 16, 19, 43, 46, 63, 91, 92, 93, 97, 99, 100, 107, 108], "furthermor": 2, "correctli": [2, 3, 10, 39, 40, 44, 46, 49, 54, 60, 64, 65, 70, 71, 75, 77, 89, 96, 97, 99, 104, 105, 108, 109], "clonabl": [2, 75], "via": [2, 5, 7, 10, 11, 13, 16, 19, 21, 25, 39, 41, 43, 44, 51, 55, 59, 63, 68, 71, 72, 73, 75, 76, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 102, 104, 105, 106, 107, 108, 109, 110], "base": [2, 3, 4, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 45, 46, 49, 50, 51, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 67, 70, 72, 73, 75, 76, 79, 81, 83, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "clone": [2, 75, 104], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 43, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 67, 71, 75, 81, 86, 91, 97, 99, 101, 103, 104, 105, 106, 108, 110], "multipl": [2, 3, 5, 10, 13, 15, 16, 37, 39, 46, 57, 58, 63, 64, 65, 67, 70, 71, 75, 85, 91, 92, 93, 95, 99, 102, 104, 105, 108], "g": [2, 3, 5, 10, 15, 25, 35, 39, 40, 44, 46, 52, 54, 59, 65, 66, 68, 71, 72, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "manual": [2, 75, 85, 88, 89, 90, 97, 99, 106, 107, 108, 110], "pytorch": [2, 40, 41, 44, 75, 85, 90, 93, 99, 102, 104, 109], "call": [2, 3, 5, 6, 10, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 51, 59, 62, 72, 75, 89, 90, 91, 92, 96, 99, 101, 104, 106, 107, 108, 109, 110], "__init__": [2, 41, 75, 93], "independ": [2, 3, 10, 64, 75, 96, 97, 100, 107, 108, 110], "compat": [2, 40, 43, 44, 56, 62, 75, 76, 79, 83, 85, 88, 89, 97, 99, 107, 108], "neural": [2, 41, 62, 72, 75, 90, 93, 99, 104, 106, 108], "network": [2, 40, 41, 44, 62, 72, 75, 89, 90, 93, 96, 99, 104, 106, 108], "typic": [2, 10, 40, 44, 56, 72, 75, 88, 89, 90, 92, 93, 95, 96, 100, 106, 107], "initi": [2, 3, 10, 16, 21, 40, 44, 54, 63, 75, 88, 96, 99, 100], "insid": [2, 44, 75, 99, 101], "There": [2, 3, 7, 54, 85, 101, 103], "two": [2, 3, 10, 21, 29, 39, 40, 43, 44, 52, 54, 55, 56, 59, 68, 70, 71, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "new": [2, 7, 9, 10, 17, 25, 40, 43, 44, 50, 54, 58, 59, 63, 75, 89, 90, 91, 96, 98, 99, 100, 106, 107, 110], "notion": 2, "confid": [2, 3, 10, 25, 39, 43, 46, 49, 51, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 79, 83, 85, 88, 93, 100, 101, 103, 104, 105, 107, 109, 110], "packag": [2, 5, 7, 9, 10, 12, 13, 14, 18, 38, 42, 46, 47, 59, 61, 62, 68, 71, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "prune": [2, 3, 46, 65, 75, 86, 100, 105], "everyth": [2, 71, 101], "els": [2, 71, 91, 93, 97, 98, 99, 100, 103, 104, 105], "mathemat": [2, 3, 10, 49, 104], "keep": [2, 16, 17, 59, 85, 91, 97, 98, 99, 100, 109], "belong": [2, 3, 10, 39, 46, 48, 49, 54, 64, 65, 66, 67, 72, 73, 77, 81, 83, 84, 92, 93, 100, 101, 104, 106, 109, 110], "2": [2, 3, 4, 5, 7, 10, 11, 13, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 76, 80, 81, 83, 84, 98, 99, 107], "error": [2, 3, 5, 10, 40, 44, 45, 46, 48, 49, 59, 64, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 83, 86, 88, 90, 91, 92, 95, 96, 97, 98, 100, 102], "erron": [2, 3, 39, 46, 49, 59, 64, 65, 73, 75, 76, 77, 106, 108], "import": [2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 43, 45, 51, 54, 57, 58, 63, 67, 70, 75, 76, 81, 83, 84, 85, 88, 89, 95, 96, 97, 99, 100, 104, 105, 106, 108, 109, 110], "linear_model": [2, 5, 39, 59, 75, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logisticregress": [2, 3, 5, 39, 59, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logreg": 2, "cl": [2, 17, 33, 75, 85, 88, 89, 99, 101, 108], "pass": [2, 3, 5, 8, 10, 11, 13, 15, 16, 17, 19, 26, 33, 36, 40, 43, 44, 46, 50, 51, 54, 56, 59, 62, 63, 65, 71, 72, 73, 75, 80, 81, 85, 89, 90, 91, 92, 96, 97, 98, 99, 101, 103, 105, 106, 108], "x_train": [2, 88, 91, 92, 101, 103, 104, 108], "labels_maybe_with_error": 2, "had": [2, 3, 75, 105], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 44, 45, 46, 54, 61, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 89, 94, 102, 103, 106, 107, 108], "pred": [2, 46, 59, 88, 89, 100, 107, 108], "x_test": [2, 88, 91, 92, 101, 104, 108], "might": [2, 5, 10, 54, 63, 75, 80, 88, 89, 91, 92, 93, 97, 99, 105], "case": [2, 3, 10, 13, 16, 39, 51, 54, 63, 75, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108, 110], "standard": [2, 3, 5, 33, 39, 46, 62, 64, 65, 67, 73, 75, 85, 88, 91, 92, 95, 98, 100, 101, 105], "adapt": [2, 12, 13, 18, 40, 42, 59, 61, 75, 106], "skorch": [2, 75, 85, 99], "kera": [2, 61, 68, 71, 75, 85, 99, 105], "scikera": [2, 62, 75, 99], "open": [2, 43, 88, 89, 92, 95, 96, 98, 101, 104, 105, 106, 108, 110], "doesn": [2, 10, 75, 85], "t": [2, 3, 4, 7, 10, 20, 30, 31, 40, 41, 43, 44, 45, 46, 51, 57, 58, 67, 72, 73, 75, 81, 83, 84, 85, 91, 92, 93, 96, 97, 98, 100, 101, 104, 105, 108, 110], "alreadi": [2, 5, 10, 13, 19, 40, 43, 44, 49, 54, 62, 63, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 105, 106, 108], "exist": [2, 5, 10, 15, 21, 40, 43, 44, 56, 58, 62, 68, 70, 72, 75, 85, 86, 88, 89, 91, 92, 96, 103, 110], "made": [2, 5, 13, 19, 40, 44, 55, 75, 88, 89, 93, 96, 97, 99, 100, 103, 105, 107, 108], "easi": [2, 12, 49, 75, 91, 92, 98, 99, 101, 104], "inherit": [2, 7, 41, 75], "baseestim": [2, 44, 75], "yourmodel": [2, 75], "def": [2, 7, 17, 40, 44, 62, 75, 89, 90, 91, 92, 93, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "self": [2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 34, 40, 41, 43, 44, 46, 51, 72, 73, 75, 88, 91, 93, 97, 98, 100, 104, 109, 110], "refer": [2, 10, 13, 19, 40, 44, 45, 64, 65, 67, 68, 70, 71, 72, 75, 79, 80, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 107, 108], "origin": [2, 5, 10, 44, 45, 46, 58, 59, 62, 64, 65, 68, 71, 72, 75, 76, 79, 81, 83, 88, 89, 91, 93, 95, 96, 97, 99, 101, 105, 106, 108, 110], "total": [2, 3, 4, 39, 43, 59, 64, 84, 93, 99, 109], "state": [2, 3, 5, 40, 41, 44, 50, 75, 101, 104, 105, 110], "art": [2, 41, 101, 104], "northcutt": [2, 3, 39, 72, 73], "et": [2, 3, 39, 41, 72, 73], "al": [2, 3, 39, 41, 72, 73], "2021": [2, 3, 39, 72, 73], "weak": [2, 71], "supervis": [2, 10, 91, 92, 99, 103], "find": [2, 5, 9, 10, 13, 16, 17, 19, 22, 23, 25, 26, 28, 29, 30, 31, 34, 35, 39, 40, 42, 43, 44, 45, 46, 50, 56, 58, 59, 61, 68, 71, 72, 73, 75, 77, 81, 83, 85, 86, 91, 98, 100, 102, 107], "uncertainti": [2, 10, 48, 72, 75, 99, 106, 108], "It": [2, 3, 5, 7, 10, 15, 16, 19, 25, 30, 33, 35, 36, 37, 40, 44, 46, 49, 51, 54, 55, 57, 63, 70, 71, 75, 85, 91, 92, 93, 97, 99, 101, 104, 107], "work": [2, 3, 7, 10, 15, 33, 39, 40, 43, 44, 46, 49, 58, 59, 60, 62, 63, 73, 75, 85, 86, 89, 91, 92, 97, 98, 100, 106, 108], "includ": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 40, 42, 43, 44, 54, 58, 59, 61, 63, 64, 67, 68, 72, 73, 75, 79, 80, 81, 83, 85, 86, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 106, 110], "deep": [2, 42, 44, 61, 62, 75, 96], "see": [2, 3, 5, 7, 10, 13, 16, 17, 36, 39, 40, 43, 44, 45, 46, 51, 56, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "subfield": 2, "theori": [2, 101], "machin": [2, 4, 5, 9, 10, 17, 19, 36, 42, 57, 61, 75, 88, 89, 91, 92, 97, 98, 100, 103], "across": [2, 3, 5, 7, 10, 13, 16, 25, 39, 43, 51, 64, 71, 72, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 107, 108], "varieti": [2, 88, 89, 99], "like": [2, 3, 5, 6, 7, 10, 17, 35, 39, 40, 43, 44, 46, 49, 59, 62, 63, 64, 67, 68, 70, 73, 75, 76, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "pu": [2, 59], "input": [2, 3, 5, 9, 13, 19, 29, 39, 40, 43, 44, 49, 51, 54, 55, 58, 59, 60, 62, 71, 75, 85, 86, 89, 92, 93, 96, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "discret": [2, 37, 46, 49, 59, 72, 73, 77, 79, 80], "vector": [2, 3, 4, 5, 10, 13, 19, 46, 49, 51, 52, 54, 59, 72, 73, 85, 89, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105, 106, 109, 110], "would": [2, 3, 5, 10, 40, 43, 44, 46, 55, 59, 65, 75, 85, 89, 91, 93, 99, 100, 101, 106, 108, 110], "obtain": [2, 5, 8, 10, 13, 19, 46, 63, 65, 68, 71, 73, 76, 90, 92, 96, 99, 103, 105, 107, 109, 110], "been": [2, 4, 39, 46, 49, 54, 58, 59, 63, 64, 68, 70, 72, 73, 75, 90, 91, 95, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "dure": [2, 10, 19, 54, 56, 72, 75, 88, 89, 90, 95, 96, 97, 99, 101, 104, 107, 108, 110], "denot": [2, 3, 49, 51, 59, 65, 72, 73, 83], "tild": 2, "paper": [2, 4, 10, 63, 72, 81, 83, 98, 101, 103, 106, 108, 110], "cv_n_fold": [2, 3, 75, 89], "5": [2, 3, 4, 5, 8, 10, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 44, 46, 48, 50, 51, 59, 63, 64, 67, 68, 71, 75, 76, 83, 89, 91, 96, 98, 99, 104, 105, 106, 107, 109, 110], "converge_latent_estim": [2, 3], "pulearn": [2, 59], "find_label_issues_kwarg": [2, 10, 75, 86, 99, 101], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 65, 81, 99], "clean": [2, 70, 73, 75, 76, 85, 88, 89, 91, 92, 98, 108], "even": [2, 3, 7, 9, 10, 39, 43, 48, 49, 59, 75, 90, 97, 99, 100, 101, 103, 104, 105], "messi": [2, 75, 101], "ridden": [2, 75], "autom": [2, 9, 10, 75, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "robust": [2, 49, 54, 75, 92, 97, 99, 100], "prone": [2, 75], "out": [2, 3, 5, 10, 13, 19, 31, 40, 44, 46, 51, 54, 62, 65, 66, 68, 71, 72, 73, 75, 76, 84, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 106, 108, 109, 110], "current": [2, 3, 5, 7, 10, 11, 13, 16, 17, 25, 40, 44, 45, 46, 51, 63, 70, 75, 91, 92, 99, 100, 103, 105], "intend": [2, 13, 14, 16, 17, 18, 19, 35, 36, 37, 47, 54, 63, 79, 83, 90, 91, 92, 96, 101], "A": [2, 3, 4, 5, 7, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 40, 41, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 62, 63, 64, 67, 70, 71, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 107, 110], "follow": [2, 3, 10, 17, 33, 37, 39, 40, 43, 44, 51, 53, 57, 63, 64, 68, 70, 71, 72, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "tutori": [2, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "repo": 2, "wrapper": [2, 13, 62, 88, 89, 90, 108], "around": [2, 13, 70, 91, 92, 100, 105, 106, 110], "fasttext": 2, "store": [2, 4, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 72, 75, 88, 89, 95, 96, 97, 98, 99, 109, 110], "along": [2, 51, 65, 83, 91, 92, 93, 97, 99, 106], "dimens": [2, 59, 77, 80, 93, 99, 106, 109], "select": [2, 9, 10, 29, 53, 63, 73, 93, 100, 103, 106], "split": [2, 3, 5, 10, 15, 43, 51, 58, 59, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 101, 102, 104, 107, 110], "cross": [2, 3, 10, 39, 46, 49, 50, 51, 65, 68, 71, 73, 75, 76, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "fold": [2, 3, 39, 46, 49, 75, 88, 90, 95, 98, 99, 105, 109], "By": [2, 39, 64, 65, 75, 91, 97, 109], "need": [2, 3, 10, 11, 39, 40, 43, 44, 46, 54, 56, 64, 65, 67, 72, 75, 85, 89, 90, 91, 92, 96, 97, 99, 100, 101, 103, 104, 105, 109], "holdout": [2, 3, 75], "comput": [2, 3, 4, 5, 7, 8, 10, 13, 22, 23, 25, 26, 29, 30, 31, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 54, 55, 56, 59, 63, 64, 65, 67, 70, 71, 72, 73, 75, 76, 77, 79, 85, 86, 89, 91, 92, 98, 101, 102, 105, 106, 108, 109], "them": [2, 3, 5, 7, 9, 10, 12, 15, 30, 35, 38, 40, 42, 43, 44, 46, 56, 61, 63, 72, 75, 86, 88, 89, 91, 92, 93, 95, 96, 97, 99, 103, 104, 106, 108, 109, 110], "numer": [2, 3, 4, 5, 10, 13, 16, 25, 33, 37, 51, 54, 55, 70, 72, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 100, 101, 103, 104, 106, 108], "consist": [2, 3, 10, 40, 44, 53, 59, 63, 97, 109, 110], "latent": [2, 3, 49], "thei": [2, 3, 5, 10, 14, 18, 24, 27, 29, 32, 40, 41, 42, 44, 46, 47, 54, 57, 59, 62, 65, 70, 73, 75, 76, 79, 83, 85, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108, 110], "relat": [2, 3, 10, 16, 22, 23, 29, 30, 31, 34, 49, 59, 64, 75, 92, 96, 97], "close": [2, 3, 10, 43, 49, 72, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "form": [2, 3, 10, 40, 41, 44, 49, 58, 59, 73, 75, 99], "equival": [2, 3, 40, 44, 49, 72, 106, 108], "iter": [2, 3, 39, 40, 44, 46, 59, 64, 65, 75, 99, 103, 109], "enforc": [2, 40, 44, 59], "perfectli": [2, 39, 64, 101], "certain": [2, 3, 5, 10, 40, 44, 62, 71, 75, 91, 92, 97, 98, 105, 106], "dict": [2, 3, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 50, 51, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 83, 91, 92, 93, 99, 100, 110], "keyword": [2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 40, 43, 44, 46, 48, 51, 54, 56, 58, 62, 63, 65, 71, 72, 73, 75, 80, 81, 83, 91], "filter": [2, 3, 10, 43, 45, 58, 64, 66, 67, 69, 71, 78, 79, 80, 82, 83, 84, 85, 86, 88, 89, 90, 93, 96, 98, 99, 100, 104, 105, 108, 109, 110], "find_label_issu": [2, 3, 10, 33, 42, 43, 45, 46, 64, 65, 66, 67, 68, 69, 70, 71, 74, 75, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 99, 104, 105, 108, 109, 110], "particularli": [2, 85, 100, 103, 106], "filter_bi": [2, 3, 43, 46, 65, 86, 99], "frac_nois": [2, 46, 65, 81, 99], "min_examples_per_class": [2, 46, 65, 99, 101], "impact": [2, 4, 10, 91, 92, 93, 97], "ml": [2, 4, 5, 9, 10, 18, 75, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 102, 103, 104, 106, 107, 108], "accuraci": [2, 10, 41, 73, 88, 89, 90, 93, 99, 100, 101, 103, 106, 108, 109], "n_job": [2, 43, 46, 65, 77, 79, 81, 99, 100, 106, 109], "disabl": [2, 40, 44, 46, 106], "process": [2, 3, 7, 13, 16, 19, 35, 40, 43, 44, 46, 54, 58, 63, 65, 71, 77, 79, 81, 89, 90, 91, 97, 99, 100, 103, 107], "caus": [2, 46, 51, 91, 92, 97, 99], "rank": [2, 3, 10, 39, 43, 45, 46, 51, 64, 65, 66, 68, 69, 71, 72, 74, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 98, 99, 104, 105, 106, 109, 110], "get_label_quality_scor": [2, 42, 43, 45, 46, 47, 51, 63, 65, 66, 67, 68, 69, 70, 73, 74, 76, 78, 79, 81, 82, 83, 86, 99, 101, 104, 105, 109, 110], "adjust_pred_prob": [2, 10, 67, 72, 73, 101], "control": [2, 5, 9, 10, 13, 19, 43, 46, 63, 71, 72, 75, 81, 83, 91, 92, 97, 98, 99], "how": [2, 3, 5, 10, 13, 15, 16, 17, 19, 25, 39, 40, 41, 43, 44, 49, 59, 63, 64, 67, 68, 70, 72, 73, 75, 79, 83, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 105, 106, 107, 108, 109], "much": [2, 10, 39, 43, 46, 75, 97, 99, 103], "output": [2, 3, 5, 10, 13, 19, 35, 40, 41, 44, 49, 59, 62, 63, 64, 68, 70, 71, 72, 75, 79, 80, 83, 84, 85, 86, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 106, 107, 108], "print": [2, 5, 7, 13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 59, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "suppress": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80, 109, 110], "statement": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80], "big": [2, 43, 65, 71, 75, 101], "limit": [2, 5, 13, 19, 43, 54, 65, 85, 97, 105, 109, 110], "memori": [2, 40, 43, 44, 65, 71, 77, 79, 91, 109], "experiment": [2, 40, 41, 43, 44, 45, 65, 86, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "label_issues_batch": [2, 42, 65, 99], "find_label_issues_batch": [2, 42, 43, 65, 99], "pred_prob": [2, 3, 5, 8, 10, 11, 13, 19, 26, 28, 29, 31, 34, 35, 39, 43, 45, 46, 48, 49, 50, 51, 52, 59, 60, 63, 64, 65, 67, 68, 71, 72, 73, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108], "threshold": [2, 3, 4, 7, 10, 13, 21, 22, 23, 25, 31, 33, 34, 43, 57, 70, 71, 72, 73, 79, 83, 91, 97, 105, 106, 109, 110], "inverse_noise_matrix": [2, 3, 10, 49, 59, 86, 101], "label_issu": [2, 43, 46, 65, 68, 75, 77, 86, 88, 89, 90, 93, 96, 99, 100, 101, 104, 108], "clf_kwarg": [2, 3, 10, 75], "clf_final_kwarg": [2, 75], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 39, 43, 46, 48, 54, 63, 64, 65, 67, 68, 70, 71, 73, 75, 76, 79, 83, 85, 88, 89, 90, 92, 93, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108], "result": [2, 3, 9, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 43, 44, 46, 48, 57, 59, 65, 67, 68, 71, 73, 75, 76, 77, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 108, 109, 110], "identifi": [2, 3, 5, 7, 9, 10, 13, 15, 19, 30, 36, 39, 43, 45, 46, 54, 65, 68, 71, 73, 75, 76, 77, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 101, 104, 106, 108, 109, 110], "final": [2, 10, 75, 88, 95, 97, 100, 105, 107, 108], "remain": [2, 75, 86, 88, 89, 93, 97, 100, 104, 108, 110], "datasetlik": [2, 59, 75], "beyond": [2, 5, 7, 9, 10, 12, 38, 85, 88, 89, 100, 108, 109], "pd": [2, 3, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 50, 62, 63, 64, 75, 83, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 108, 110], "datafram": [2, 3, 5, 7, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 50, 59, 60, 62, 63, 64, 75, 80, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 108, 109, 110], "scipi": [2, 4, 5, 13, 16, 55, 59, 72, 97], "spars": [2, 4, 5, 10, 13, 16, 19, 21, 34, 54, 59, 60, 95, 97], "csr_matrix": [2, 4, 5, 13, 16, 19, 21, 34, 54, 97], "torch": [2, 40, 41, 44, 89, 90, 93, 96, 98, 106], "util": [2, 5, 10, 13, 19, 36, 40, 41, 44, 47, 54, 62, 63, 68, 71, 75, 85, 86, 90, 91, 92, 93, 99, 101, 106], "tensorflow": [2, 59, 62, 85, 90, 99], "object": [2, 5, 10, 13, 15, 16, 19, 35, 36, 40, 41, 43, 44, 51, 54, 56, 59, 60, 62, 65, 68, 69, 70, 71, 72, 75, 83, 85, 89, 90, 92, 93, 95, 97, 99, 100, 101, 102, 104, 108], "list": [2, 3, 5, 10, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 45, 46, 52, 54, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 79, 80, 81, 83, 84, 86, 89, 90, 91, 92, 93, 98, 99, 100, 101, 104, 105, 108, 110], "index_list": 2, "subset": [2, 3, 5, 13, 19, 39, 43, 46, 59, 73, 80, 84, 88, 89, 90, 93, 95, 96, 97, 99, 104, 105, 106, 107, 108, 110], "wa": [2, 3, 15, 17, 43, 57, 59, 63, 64, 70, 72, 84, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 107, 109, 110], "abl": [2, 3, 10, 75, 90, 99, 100, 101, 103, 104], "format": [2, 3, 5, 10, 15, 35, 40, 43, 44, 46, 49, 50, 51, 52, 54, 59, 60, 62, 63, 64, 65, 68, 71, 72, 73, 75, 77, 79, 80, 83, 84, 88, 91, 92, 93, 95, 97, 98, 100, 103, 108, 109, 110], "make": [2, 3, 5, 21, 40, 43, 44, 51, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 108], "sure": [2, 5, 43, 46, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 103, 104, 105, 106, 108], "shuffl": [2, 10, 59, 90, 93, 96, 97, 104, 106], "ha": [2, 3, 5, 6, 10, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 45, 49, 51, 54, 58, 59, 63, 68, 70, 75, 81, 83, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 110], "batch": [2, 43, 59, 62, 63, 77, 79, 93, 99, 106], "order": [2, 5, 10, 37, 39, 40, 44, 45, 46, 49, 50, 51, 57, 59, 63, 64, 65, 68, 71, 72, 73, 77, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 108, 109, 110], "destroi": [2, 59], "oper": [2, 40, 43, 44, 54, 59, 62, 73, 85, 88, 89, 96, 99, 106], "eg": [2, 5, 10, 59, 68, 71, 91, 92, 99, 100], "repeat": [2, 59, 63, 103, 106], "appli": [2, 10, 37, 40, 42, 44, 46, 51, 52, 54, 58, 59, 67, 72, 81, 85, 88, 89, 90, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 109], "array_lik": [2, 3, 39, 46, 59, 65, 72, 76], "some": [2, 3, 5, 10, 17, 25, 39, 40, 42, 44, 46, 49, 54, 58, 59, 61, 63, 64, 65, 67, 68, 71, 72, 73, 75, 77, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "seri": [2, 3, 43, 59, 60, 75, 83, 99, 100], "row": [2, 3, 5, 10, 13, 16, 30, 35, 39, 43, 46, 48, 49, 54, 55, 59, 63, 64, 65, 67, 72, 73, 75, 80, 81, 83, 84, 88, 90, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 110], "rather": [2, 3, 5, 10, 29, 39, 59, 62, 63, 70, 79, 83, 89, 98, 100, 103, 107, 108, 109, 110], "leav": [2, 46], "per": [2, 3, 5, 7, 10, 13, 16, 39, 43, 46, 51, 58, 63, 64, 65, 67, 70, 71, 73, 76, 77, 79, 83, 92, 99, 105, 110], "determin": [2, 3, 10, 15, 19, 25, 29, 33, 39, 43, 46, 51, 54, 59, 63, 65, 68, 70, 73, 79, 83, 91, 97, 99, 100, 103, 105, 106, 108], "cutoff": [2, 3, 55, 106], "consid": [2, 3, 4, 5, 10, 13, 16, 19, 26, 29, 31, 34, 39, 40, 44, 46, 54, 56, 59, 63, 70, 72, 73, 76, 79, 83, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 105, 106, 107, 108, 109], "section": [2, 3, 7, 10, 86, 93, 95, 97, 99, 100, 105], "3": [2, 3, 4, 5, 7, 10, 11, 37, 39, 40, 44, 46, 49, 50, 51, 52, 55, 57, 58, 59, 62, 65, 72, 73, 75, 76, 81, 83, 98, 99, 107], "equat": [2, 3, 49], "advanc": [2, 3, 5, 9, 10, 13, 19, 70, 72, 83, 86, 92, 94, 97, 99, 100, 101], "user": [2, 3, 5, 9, 10, 13, 17, 19, 30, 35, 36, 37, 40, 44, 46, 54, 62, 70, 72, 73, 75, 79, 83, 100, 101], "specifi": [2, 3, 4, 5, 8, 10, 13, 16, 17, 19, 21, 34, 36, 40, 43, 44, 46, 51, 54, 56, 58, 62, 63, 64, 65, 68, 70, 72, 73, 75, 76, 84, 86, 89, 90, 92, 93, 96, 97, 100, 103, 105, 108], "automat": [2, 3, 5, 29, 39, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "greater": [2, 3, 4, 5, 7, 9, 10, 31, 43, 55, 59, 70, 92, 98, 99, 110], "count": [2, 25, 29, 39, 43, 46, 49, 59, 64, 65, 71, 86, 93, 97, 99, 105], "observ": [2, 3, 49, 56, 90, 91, 92, 103, 106, 108], "mislabel": [2, 10, 39, 43, 45, 46, 49, 63, 64, 65, 68, 70, 73, 79, 81, 83, 84, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 105, 108], "one": [2, 3, 5, 7, 10, 29, 39, 40, 43, 44, 45, 46, 51, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 106, 107, 108, 110], "get_label_issu": [2, 42, 43, 74, 75, 88, 89, 101, 108], "either": [2, 3, 4, 7, 10, 40, 43, 44, 46, 55, 63, 65, 70, 72, 73, 77, 79, 92, 97, 99, 104, 105], "boolean": [2, 7, 10, 25, 43, 46, 56, 58, 63, 65, 68, 73, 75, 77, 79, 80, 85, 89, 90, 92, 93, 96, 99, 105, 108, 109], "label_issues_mask": [2, 46, 73, 75, 86], "indic": [2, 3, 4, 5, 7, 10, 13, 16, 25, 39, 43, 44, 45, 46, 48, 51, 54, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "its": [2, 5, 7, 9, 10, 13, 19, 40, 43, 44, 46, 54, 56, 57, 58, 65, 68, 71, 72, 73, 75, 77, 81, 83, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 107, 108, 109, 110], "return_indices_ranked_bi": [2, 43, 46, 65, 81, 86, 88, 89, 99, 101], "significantli": [2, 10, 93, 97, 101, 103, 107], "reduc": [2, 43, 46, 59, 90, 99], "time": [2, 10, 40, 43, 44, 59, 63, 84, 86, 91, 93, 99, 100, 105, 109, 110], "take": [2, 5, 10, 39, 40, 44, 50, 51, 54, 56, 59, 62, 73, 88, 93, 95, 103, 104, 105, 110], "run": [2, 5, 6, 7, 9, 10, 11, 12, 13, 17, 19, 29, 30, 35, 38, 40, 43, 44, 56, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 110], "skip": [2, 10, 40, 44, 75, 90, 97, 99, 100, 104, 110], "slow": [2, 3], "step": [2, 7, 29, 51, 71, 93, 97, 100, 101, 103, 107], "caution": [2, 5, 99, 100], "previous": [2, 5, 13, 16, 59, 72, 75, 86, 88, 90, 91, 95, 96, 100, 103, 107], "assign": [2, 7, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 40, 44, 50, 51, 59, 75, 88, 91, 93, 95, 97, 99, 108, 109, 110], "individu": [2, 4, 7, 10, 13, 16, 29, 40, 44, 45, 63, 67, 70, 73, 75, 81, 83, 86, 88, 92, 95, 97, 98, 99, 103, 104, 105, 110], "still": [2, 43, 44, 59, 72, 88, 93, 99, 106], "extra": [2, 40, 44, 59, 62, 63, 64, 75, 93, 96, 99, 100, 103, 106], "receiv": [2, 10, 40, 44, 45, 64, 67, 68, 75, 77, 81, 92, 105], "overwritten": [2, 75], "callabl": [2, 3, 4, 10, 29, 40, 44, 51, 54, 55, 56, 58, 62, 67, 99], "x_val": 2, "y_val": 2, "map": [2, 3, 15, 43, 44, 47, 50, 58, 59, 71, 73, 75, 80, 90, 91, 92, 93, 97, 99, 101, 104, 110], "appropri": [2, 10, 19, 37, 55, 65, 73, 91, 95, 100, 104, 105], "earli": [2, 93], "stop": [2, 93], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 25, 39, 59, 72, 88, 91, 93, 95, 97, 100, 104, 108, 110], "f": [2, 7, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108], "ignor": [2, 40, 44, 58, 62, 75, 80, 84, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "allow": [2, 13, 39, 40, 43, 44, 48, 56, 59, 63, 71, 72, 75, 77, 79, 89, 90, 93, 97, 99, 107, 109], "access": [2, 10, 16, 40, 44, 75, 92, 93, 98, 104], "hyperparamet": [2, 67, 72, 93], "purpos": [2, 54, 91, 92, 97, 99, 104, 108], "want": [2, 5, 10, 39, 43, 54, 60, 63, 65, 75, 89, 91, 93, 96, 98, 100, 103, 105, 106, 107, 109, 110], "explicitli": [2, 8, 10, 44, 54, 75], "yourself": [2, 5, 43, 92, 97], "altern": [2, 7, 10, 51, 56, 59, 62, 63, 73, 86, 89, 90, 93, 95, 96, 98, 99, 100, 101, 103, 104, 106, 108], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 19, 29, 33, 40, 43, 44, 46, 54, 59, 62, 63, 65, 72, 73, 75, 79, 80, 83, 84, 85, 88, 89, 91, 92, 93, 95, 96, 97, 99, 100, 104, 105, 106, 107, 108, 109], "effect": [2, 10, 30, 40, 44, 63, 72, 75, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 108], "offer": [2, 5, 9, 10, 89, 90, 91, 92, 96, 99, 100, 101, 104], "after": [2, 3, 5, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 63, 75, 89, 91, 93, 96, 97, 99, 100, 101, 103, 105, 106, 107, 108, 109], "attribut": [2, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 51, 56, 72, 75, 88, 91, 97], "label_issues_df": [2, 75, 93], "similar": [2, 10, 39, 40, 44, 56, 59, 63, 67, 68, 70, 72, 75, 79, 83, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105, 106, 109], "document": [2, 3, 5, 13, 17, 19, 39, 40, 43, 44, 45, 46, 51, 58, 62, 64, 65, 67, 70, 71, 72, 75, 79, 80, 81, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "descript": [2, 5, 7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 39, 45, 59, 68, 75, 91, 92], "were": [2, 3, 5, 10, 39, 44, 54, 64, 70, 83, 88, 90, 95, 99, 101, 103, 105, 107, 109], "present": [2, 3, 5, 10, 13, 15, 16, 23, 39, 59, 72, 80, 85, 93, 97, 99, 100, 106], "actual": [2, 3, 5, 10, 39, 54, 63, 64, 73, 92, 99, 101, 107, 110], "num_class": [2, 39, 43, 59, 62, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 106], "uniqu": [2, 34, 59, 80, 91, 97, 99, 100, 104, 106], "given_label": [2, 5, 11, 28, 33, 39, 49, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109, 110], "normal": [2, 3, 10, 21, 29, 34, 46, 48, 51, 57, 58, 59, 73, 97, 99, 101, 106], "trick": [2, 99], "distribut": [2, 3, 5, 10, 29, 31, 39, 44, 46, 50, 57, 63, 71, 72, 73, 85, 91, 92, 93, 95, 96, 97, 100, 105, 106], "account": [2, 39, 63, 67, 72, 73, 89, 96, 99, 101, 103, 104, 106, 108], "word": [2, 3, 58, 83, 84, 99], "remov": [2, 10, 34, 39, 40, 44, 46, 75, 85, 88, 89, 93, 96, 97, 98, 99, 100, 104, 106, 108], "so": [2, 3, 5, 6, 7, 10, 17, 29, 37, 39, 40, 43, 44, 46, 54, 59, 63, 64, 70, 73, 75, 79, 83, 90, 91, 92, 93, 96, 97, 100, 101, 104, 106, 109], "proportion": [2, 10, 46], "just": [2, 3, 5, 10, 13, 16, 35, 39, 41, 43, 59, 62, 73, 75, 77, 85, 86, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 104, 105, 106, 107, 108, 109], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 16, 34, 40, 41, 44, 46, 51, 57, 58, 59, 63, 65, 67, 72, 73, 75, 76, 77, 85, 88, 89, 90, 93, 96, 97, 98, 99, 100, 101, 106, 107, 108], "detect": [2, 5, 7, 9, 13, 16, 17, 19, 21, 25, 31, 45, 54, 57, 66, 68, 69, 70, 71, 72, 73, 74, 75, 78, 82, 85, 88, 89, 91, 94, 98, 100, 102, 104, 108, 109, 110], "arg": [2, 15, 25, 30, 34, 40, 41, 44, 51, 59, 73, 75, 100], "kwarg": [2, 7, 10, 13, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 45, 51, 54, 62, 71, 75, 77, 79, 80, 81, 99], "test": [2, 5, 10, 29, 44, 51, 54, 62, 75, 85, 88, 89, 91, 92, 93, 95, 96, 102, 107, 108, 110], "expect": [2, 3, 10, 40, 44, 46, 51, 54, 63, 72, 73, 75, 88, 89, 99, 100, 101, 103, 104, 105, 108, 110], "class_predict": 2, "evalu": [2, 10, 40, 41, 42, 43, 44, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 107, 108, 109], "simpli": [2, 10, 39, 73, 85, 89, 91, 92, 95, 96, 99, 101, 104, 108, 109, 110], "quantifi": [2, 4, 5, 7, 10, 13, 16, 46, 67, 72, 75, 85, 92, 93, 95, 96, 97, 100, 101, 105], "save_spac": [2, 10, 74, 75], "potenti": [2, 10, 39, 46, 58, 65, 68, 71, 73, 75, 77, 79, 84, 86, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "cach": [2, 89, 96], "panda": [2, 5, 7, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 59, 60, 62, 63, 64, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 108, 109], "unlik": [2, 10, 46, 48, 51, 62, 64, 65, 67, 83, 91, 100, 103, 104, 106, 108], "both": [2, 5, 10, 13, 19, 29, 39, 40, 44, 46, 54, 59, 63, 65, 73, 77, 79, 84, 85, 91, 93, 99, 100, 101, 103, 110], "mask": [2, 43, 46, 58, 59, 65, 68, 73, 75, 77, 79, 80, 85, 98, 99, 103, 105, 109, 110], "prefer": [2, 73, 81, 104], "plan": 2, "subsequ": [2, 3, 40, 44, 56, 89, 96, 99, 101, 105], "invok": [2, 40, 44, 101, 107], "scratch": [2, 54, 75], "To": [2, 5, 7, 9, 10, 12, 13, 16, 19, 29, 38, 40, 43, 44, 45, 46, 62, 63, 65, 67, 71, 72, 73, 75, 76, 77, 79, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "share": [2, 10, 73, 75], "mostli": [2, 59, 70, 75, 100, 104, 108], "longer": [2, 37, 50, 51, 58, 75, 86, 89, 96, 99, 100, 105], "info": [2, 5, 7, 10, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 75, 83, 92, 97, 98, 110], "about": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 41, 43, 48, 63, 64, 67, 71, 75, 80, 83, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106], "docstr": [2, 39, 40, 44, 59, 75, 98, 101], "unless": [2, 40, 44, 54, 75, 99], "our": [2, 3, 10, 62, 63, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "is_label_issu": [2, 11, 33, 75, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "entir": [2, 10, 29, 43, 46, 49, 64, 65, 70, 73, 75, 77, 79, 80, 85, 91, 92, 97, 99, 100, 105, 106, 107, 109, 110], "accur": [2, 3, 5, 9, 10, 13, 19, 39, 43, 46, 55, 63, 64, 65, 68, 71, 73, 75, 76, 77, 79, 80, 86, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 108], "label_qu": [2, 63, 75, 89, 101, 103, 108], "measur": [2, 5, 39, 63, 64, 75, 85, 88, 97, 98, 99, 100, 101, 103, 104, 108, 109, 110], "qualiti": [2, 3, 5, 7, 9, 10, 13, 16, 33, 34, 39, 43, 45, 46, 48, 51, 63, 64, 65, 67, 68, 70, 73, 75, 76, 79, 81, 83, 85, 86, 90, 91, 93, 99, 100, 102], "lower": [2, 4, 5, 7, 10, 13, 16, 31, 43, 51, 57, 63, 64, 67, 70, 71, 73, 75, 76, 79, 83, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "eas": 2, "comparison": [2, 40, 44, 71, 100, 101, 103], "against": [2, 40, 44, 91, 95, 97, 99, 100, 103, 104], "predicted_label": [2, 5, 11, 28, 33, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109], "ad": [2, 40, 44, 92, 103, 108], "precis": [2, 55, 57, 65, 68, 71, 97, 98, 99, 101, 109, 110], "definit": [2, 7, 37, 51, 75, 88, 95], "accessor": [2, 75], "describ": [2, 10, 21, 63, 72, 73, 75, 81, 83, 101, 103, 104, 105, 107, 110], "precomput": [2, 4, 5, 49, 54, 75, 98], "clear": [2, 40, 44, 56, 75, 89, 96, 97, 108], "save": [2, 5, 13, 19, 40, 43, 44, 71, 75, 97, 99, 105, 109, 110], "space": [2, 5, 10, 72, 75, 93, 95, 97, 98], "place": [2, 40, 44, 54, 59, 75, 88, 103], "larg": [2, 9, 10, 43, 54, 75, 93, 99, 105, 106, 109, 110], "deploi": [2, 9, 10, 75, 93, 99, 100], "care": [2, 10, 40, 44, 54, 75, 96, 97, 99, 101], "avail": [2, 4, 5, 7, 10, 15, 17, 36, 44, 56, 75, 99, 100, 101, 103, 105, 108], "cannot": [2, 5, 15, 17, 59, 100, 107, 110], "anymor": 2, "classmethod": [2, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 44, 51, 75], "__init_subclass__": [2, 42, 44, 74, 75], "set_": [2, 44, 75], "_request": [2, 44, 75], "pep": [2, 44, 75], "487": [2, 44, 75], "look": [2, 5, 7, 10, 19, 40, 44, 59, 75, 80, 88, 91, 92, 95, 96, 99, 100, 101, 103, 104, 105, 106, 109, 110], "inform": [2, 5, 7, 10, 13, 16, 19, 36, 40, 44, 56, 59, 63, 64, 68, 71, 75, 80, 83, 84, 85, 90, 91, 95, 96, 97, 98, 100, 101, 103, 106, 109, 110], "__metadata_request__": [2, 44, 75], "infer": [2, 44, 59, 75, 80, 84, 88, 89, 93, 103, 104], "signatur": [2, 40, 44, 75], "accept": [2, 40, 44, 56, 57, 73, 75, 91, 92, 99], "metadata": [2, 10, 44, 75, 93, 110], "through": [2, 5, 7, 44, 75, 89, 90, 92, 96, 97, 98, 99, 100, 103, 105, 106], "develop": [2, 9, 44, 56, 75, 99, 101, 110], "request": [2, 44, 75, 88, 89, 92, 96, 97, 98, 104, 110], "those": [2, 3, 4, 10, 43, 44, 46, 53, 62, 63, 65, 71, 75, 79, 83, 84, 85, 90, 93, 97, 99, 100, 105, 109], "http": [2, 4, 5, 7, 9, 10, 12, 21, 38, 40, 41, 43, 44, 48, 56, 59, 68, 71, 72, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "www": [2, 44, 75, 106], "org": [2, 4, 21, 40, 41, 44, 56, 59, 72, 75, 99, 100, 101, 110], "dev": [2, 44, 75], "0487": [2, 44, 75], "get_metadata_rout": [2, 42, 44, 74, 75], "rout": [2, 44, 75], "pleas": [2, 40, 44, 62, 75, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "guid": [2, 7, 10, 44, 75, 86, 90, 91, 92, 93, 94, 95, 96, 97, 100, 101], "mechan": [2, 40, 44, 75], "metadatarequest": [2, 44, 75], "encapsul": [2, 19, 44, 70, 75], "get_param": [2, 42, 44, 61, 62, 74, 75], "subobject": [2, 44, 75], "param": [2, 10, 40, 44, 62, 72, 75, 99], "name": [2, 5, 6, 7, 10, 11, 13, 15, 16, 35, 37, 39, 40, 44, 50, 51, 55, 59, 62, 63, 64, 71, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "set_fit_request": [2, 42, 44, 74, 75], "str": [2, 3, 4, 5, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 49, 51, 54, 55, 56, 57, 58, 59, 62, 63, 64, 68, 70, 71, 73, 75, 80, 84, 90, 91, 97, 99, 103, 104, 105, 110], "unchang": [2, 40, 44, 75, 97, 110], "relev": [2, 10, 19, 29, 44, 75, 93, 95, 97], "enable_metadata_rout": [2, 44, 75], "set_config": [2, 44, 75], "meta": [2, 44, 75], "rais": [2, 4, 5, 13, 15, 16, 37, 40, 44, 48, 51, 54, 57, 75, 99], "alia": [2, 40, 44, 75], "metadata_rout": [2, 44, 75], "retain": [2, 44, 59, 75], "chang": [2, 35, 37, 40, 43, 44, 48, 75, 83, 88, 89, 90, 91, 96, 99, 100, 105, 106, 110], "version": [2, 4, 5, 7, 9, 10, 12, 14, 18, 24, 27, 32, 38, 40, 42, 44, 47, 48, 59, 61, 62, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "sub": [2, 44, 70, 75], "pipelin": [2, 44, 75, 108], "otherwis": [2, 4, 7, 10, 37, 39, 40, 43, 44, 46, 52, 55, 57, 58, 59, 65, 75, 77, 79, 80, 84, 85, 89, 96, 99, 100], "updat": [2, 13, 16, 40, 43, 44, 54, 62, 75, 86, 91, 93, 100], "set_param": [2, 42, 44, 61, 62, 74, 75], "simpl": [2, 40, 44, 46, 63, 73, 75, 88, 89, 91, 92, 93, 95, 96, 100, 103, 106, 108], "well": [2, 3, 9, 10, 40, 44, 48, 49, 63, 65, 71, 73, 75, 80, 83, 84, 86, 91, 92, 93, 95, 96, 99, 100, 101, 103, 105, 106], "nest": [2, 40, 44, 45, 60, 75, 81, 83, 84, 110], "latter": [2, 40, 44, 75, 106], "compon": [2, 44, 75], "__": [2, 44, 75], "set_score_request": [2, 74, 75], "structur": [3, 72, 95, 97, 99, 100], "unobserv": 3, "less": [3, 4, 5, 10, 34, 43, 51, 63, 72, 73, 77, 79, 83, 93, 95, 97, 98, 99, 100, 101, 105, 110], "channel": [3, 90, 101], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 39, 49, 59, 64, 89, 92, 98], "inv": 3, "confident_joint": [3, 25, 39, 46, 59, 64, 65, 86, 99, 101], "un": 3, "under": [3, 10, 40, 44, 64, 71, 72, 92, 97, 100, 106], "joint": [3, 39, 46, 49, 59, 64, 65, 98], "num_label_issu": [3, 43, 46, 65, 80, 84, 86], "estimation_method": [3, 43], "off_diagon": 3, "multi_label": [3, 39, 46, 59, 60, 65, 104], "don": [3, 10, 85, 92, 93, 96, 101, 105, 108], "statis": 3, "compute_confident_joint": [3, 39, 46, 59, 65, 101], "off": [3, 46, 59, 70, 93, 101, 105, 106], "j": [3, 5, 39, 40, 44, 45, 46, 65, 68, 71, 72, 81, 83, 84, 91, 92, 101, 109, 110], "confident_learn": [3, 46, 65, 101], "off_diagonal_calibr": 3, "calibr": [3, 4, 46, 59, 63, 103], "cj": [3, 49, 59], "axi": [3, 34, 49, 51, 57, 77, 80, 90, 91, 92, 93, 97, 99, 100, 101, 103, 104, 106, 108, 109], "bincount": [3, 91, 92, 101, 103, 104], "alwai": [3, 10, 40, 44, 59, 88, 89, 90, 101, 108], "estimate_issu": 3, "over": [3, 5, 10, 40, 43, 44, 70, 71, 77, 79, 88, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108], "As": [3, 7, 85, 91, 92, 96, 100, 101, 108, 110], "add": [3, 5, 7, 13, 15, 16, 40, 44, 62, 71, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 104], "approach": [3, 39, 43, 46, 62, 88, 95, 97, 100, 101, 104, 106, 108], "custom": [3, 7, 10, 12, 33, 40, 43, 44, 51, 58, 73, 89, 92, 96, 97, 101, 108], "know": [3, 10, 91, 92, 93, 96, 99, 101, 103, 108], "cut": [3, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 35, 105, 106, 110], "underestim": 3, "few": [3, 9, 10, 71, 85, 97, 99, 103, 104, 105, 106, 110], "4": [3, 4, 5, 10, 11, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 50, 51, 58, 67, 68, 70, 71, 73, 76, 83, 98, 99, 104, 109, 110], "detail": [3, 4, 5, 10, 13, 17, 19, 36, 39, 40, 44, 45, 51, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 79, 80, 81, 85, 86, 90, 97, 99, 100, 104, 106, 110], "num_issu": [3, 7, 43, 90, 91, 92, 93, 95, 96, 97, 100, 101], "calibrate_confident_joint": 3, "up": [3, 7, 10, 20, 29, 30, 33, 46, 51, 53, 62, 63, 89, 98, 99, 105, 108, 110], "p_": [3, 39, 46], "pair": [3, 5, 10, 39, 46, 101], "v": [3, 10, 43, 64, 65, 67, 73, 91, 92, 102, 104, 105, 106, 107], "rest": [3, 5, 7, 9, 10, 12, 38, 64, 65, 67, 75, 88, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 108], "fashion": [3, 5, 77, 88], "2x2": 3, "incorrectli": [3, 39, 64, 65, 68, 95, 100, 110], "calibrated_cj": 3, "c": [3, 10, 57, 58, 65, 73, 85, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 105, 106, 107, 108], "whose": [3, 4, 5, 10, 31, 40, 44, 49, 54, 58, 63, 67, 70, 76, 79, 83, 84, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 106, 109, 110], "truli": [3, 106, 109], "estimate_joint": [3, 39, 101], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 65, 71, 101, 105, 107, 109, 110], "return_indices_of_off_diagon": 3, "frequenc": [3, 29, 63, 64, 71, 80, 105, 106], "done": [3, 10, 62, 75, 91, 99, 101, 104, 106, 107], "overfit": [3, 10, 68, 71, 88, 90, 91, 92, 93, 95, 96, 107], "classifict": 3, "singl": [3, 5, 9, 10, 15, 29, 39, 40, 44, 45, 51, 52, 59, 63, 64, 70, 71, 72, 73, 83, 88, 90, 91, 97, 99, 101, 104, 105], "baselin": [3, 40, 46, 89, 106, 108], "proxi": 3, "union": [3, 5, 15, 29, 51, 54, 55, 56, 59, 60, 65, 71, 75, 83, 99], "tupl": [3, 34, 40, 44, 45, 49, 50, 52, 54, 58, 59, 63, 65, 71, 79, 81, 83, 84, 90, 110], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 43, 49, 54, 55, 63, 72, 77, 79, 85, 89, 93, 97, 99, 100, 109], "practic": [3, 88, 89, 92, 93, 100, 101, 106, 108], "complet": [3, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "gist": 3, "cj_ish": 3, "guess": [3, 49, 101, 103], "8": [3, 5, 7, 8, 50, 51, 52, 58, 67, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 103, 104, 105, 106, 108, 109, 110], "parallel": [3, 46, 71, 81, 98], "again": [3, 62, 88, 99, 106], "simplifi": [3, 17, 99], "understand": [3, 9, 10, 39, 64, 71, 92, 97, 101, 102, 108, 109, 110], "100": [3, 4, 40, 44, 54, 55, 57, 72, 73, 88, 89, 91, 92, 93, 95, 97, 98, 99, 100, 101, 104, 105, 106, 110], "optim": [3, 40, 41, 44, 62, 88, 89, 92, 93, 95, 96, 97, 98, 101, 103, 104, 106, 108], "speed": [3, 46, 89, 98, 99, 108], "dtype": [3, 26, 28, 29, 34, 40, 44, 58, 59, 67, 83, 90, 97, 100, 105], "enumer": [3, 40, 44, 90, 91, 92, 93, 97, 110], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 44, 51, 59, 83, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "num_confident_bin": 3, "argmax": [3, 46, 73, 77, 80, 90, 97, 99, 101, 105, 106, 109], "elif": 3, "estimate_lat": 3, "py_method": [3, 49], "cnt": [3, 49], "1d": [3, 5, 13, 15, 19, 35, 43, 46, 51, 52, 54, 59, 60, 67, 76, 88, 90, 97], "eqn": [3, 49], "margin": [3, 46, 49, 51, 73], "marginal_p": [3, 49], "shorthand": [3, 13, 16], "proport": [3, 10, 39, 64, 101, 107], "poorli": [3, 49, 88, 97], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 101], "variabl": [3, 7, 17, 30, 59, 75, 76, 90, 91, 95, 101, 104, 108], "exact": [3, 10, 49, 54, 88, 91, 92, 93, 95, 97, 100], "within": [3, 4, 5, 10, 14, 18, 35, 40, 41, 44, 45, 47, 65, 70, 79, 81, 83, 91, 92, 93, 99, 105, 109], "percent": 3, "often": [3, 39, 49, 64, 99, 101, 107, 109], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 59, 60, 71, 88, 89, 90, 91, 93, 95, 96, 99, 100, 104, 105, 106, 108], "wai": [3, 5, 10, 54, 62, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107], "pro": 3, "con": 3, "pred_proba": [3, 107], "combin": [3, 39, 91, 93, 97, 98, 99, 100, 101, 107, 108], "becaus": [3, 10, 49, 55, 59, 70, 96, 97, 99, 100, 101, 103, 105, 107], "littl": [3, 43, 98, 105, 110], "uniform": [3, 73, 98, 99, 101], "20": [3, 7, 45, 84, 90, 93, 96, 97, 98, 99, 100, 101, 105, 108, 109, 110], "Such": [3, 93, 106], "bound": [3, 26, 28, 40, 44, 58, 67, 68, 70, 71, 105], "reason": [3, 10, 25, 40, 44, 55, 72], "comment": [3, 58, 97, 110], "end": [3, 5, 40, 44, 56, 71], "file": [3, 5, 15, 42, 43, 61, 71, 88, 90, 91, 95, 96, 98, 99, 105, 106, 109, 110], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 101], "handl": [3, 5, 7, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 54, 55, 56, 86, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 101, 104, 106, 108, 109, 110], "five": [3, 68, 71, 101, 105], "estimate_cv_predicted_prob": [3, 101], "estimate_noise_matric": 3, "get_confident_threshold": [3, 42, 43], "amongst": [3, 10, 100, 105], "confident_threshold": [3, 10, 25, 26, 43, 72], "point": [4, 5, 7, 9, 10, 21, 29, 40, 44, 54, 56, 85, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103], "valuat": [4, 9, 21], "help": [4, 39, 40, 44, 71, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 109, 110], "u": [4, 88, 89, 90, 91, 93, 95, 97, 99, 101, 103, 104, 107, 108, 109, 110], "assess": [4, 10, 97, 100, 105], "contribut": [4, 10, 21, 97, 105], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 13, 19, 21, 22, 29, 31, 34, 47, 53, 95, 97], "metric": [4, 5, 10, 21, 22, 24, 29, 31, 34, 47, 53, 54, 56, 57, 59, 62, 71, 72, 88, 89, 90, 93, 95, 96, 97, 100, 101, 108], "10": [4, 10, 21, 22, 26, 29, 31, 34, 40, 41, 54, 71, 72, 73, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "shaplei": [4, 10, 21], "nearest": [4, 5, 10, 13, 19, 26, 29, 31, 53, 54, 55, 56, 57, 72, 92, 96, 97, 106], "neighbor": [4, 5, 10, 13, 19, 21, 26, 29, 31, 47, 54, 55, 56, 57, 72, 91, 92, 93, 95, 96, 97, 99, 106], "knn": [4, 10, 13, 16, 21, 29, 31, 34, 53, 54, 55, 56, 57, 72, 95, 106], "graph": [4, 5, 10, 13, 16, 19, 21, 29, 34, 53, 54], "calcul": [4, 10, 21, 29, 43, 51, 53, 54, 57, 63, 67, 68, 70, 71, 72, 75, 79, 93, 98, 100], "directli": [4, 5, 10, 13, 17, 19, 36, 37, 43, 56, 62, 63, 89, 92, 96, 97, 99, 100, 104, 105, 108], "lowest": [4, 10, 63, 71, 92, 93, 95, 97, 99, 100, 103, 104, 105, 109], "fall": [4, 10, 70, 79, 83, 101, 106], "flag": [4, 10, 25, 29, 46, 51, 64, 65, 68, 75, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 106, 108, 109], "approxim": [4, 10, 21, 43, 56, 72, 97, 103], "top": [4, 5, 10, 39, 43, 45, 46, 59, 65, 68, 71, 73, 80, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 108, 110], "found": [4, 5, 7, 10, 13, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 104, 106, 108, 110], "arxiv": [4, 21, 101], "ab": [4, 21, 101, 105], "1908": 4, "08619": 4, "1911": [4, 21], "07128": [4, 21], "embed": [4, 5, 10, 13, 19, 72, 85, 89, 90, 91, 92, 95, 96, 97, 100, 101, 104, 108], "represent": [4, 5, 10, 13, 19, 37, 40, 44, 52, 54, 65, 85, 89, 90, 91, 92, 93, 96, 99, 100, 101, 106], "suppli": [4, 104, 105, 108], "2d": [4, 5, 13, 19, 35, 43, 51, 52, 54, 58, 59, 63, 88, 90, 97, 104], "num_exampl": [4, 5, 13, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 64, 90, 91, 92, 93, 95, 96, 100, 101], "num_featur": [4, 5, 13, 19, 40, 44, 62], "distanc": [4, 5, 10, 13, 19, 21, 29, 31, 34, 53, 54, 55, 56, 57, 70, 72, 95, 97, 106], "construct": [4, 5, 7, 10, 13, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 53, 54, 56, 62, 97, 100], "nearestneighbor": [4, 5, 10, 21, 54, 56, 72, 95, 106], "cosin": [4, 10, 54, 55, 57, 72, 97, 106], "dim": [4, 72, 93, 109], "euclidean": [4, 5, 10, 54, 55, 57, 70, 72, 95], "dimension": [4, 29, 55, 59, 90, 101, 106], "scikit": [4, 44, 55, 56, 59, 72, 85, 88, 89, 90, 91, 92, 95, 96, 97, 99, 108], "fewer": [4, 10, 46, 59, 72, 97, 105], "stabl": [4, 14, 18, 24, 27, 32, 42, 47, 56, 59, 61, 72, 86, 90, 91, 92, 93, 95, 96, 100, 101], "exce": [4, 54, 93, 97], "transform": [4, 10, 35, 51, 54, 57, 59, 72, 73, 88, 89, 92, 93, 96, 97, 100, 106, 110], "rel": [4, 10, 39, 54, 63, 64, 72, 91, 92, 93, 95, 96, 100, 101, 106], "adjust": [4, 41, 46, 54, 67, 72, 73, 85, 97, 100, 101], "closer": [4, 10, 70, 97, 105], "highli": [4, 92, 93], "influenti": 4, "posit": [4, 5, 10, 40, 44, 57, 59, 71, 97, 98, 106], "convers": 4, "neg": [4, 10, 70, 71, 91, 92, 97, 98], "valueerror": [4, 5, 13, 15, 16, 37, 48, 51, 54, 57, 99], "neither": [4, 5, 10, 17, 55, 105], "nor": [4, 5, 10, 17], "larger": [4, 21, 55, 75, 77, 79, 93, 96, 98, 99], "55": [4, 58, 97, 98, 105, 108], "525": 4, "unifi": 5, "audit": [5, 9, 13, 15, 16, 19, 90, 93, 94, 95, 96, 97, 99, 100, 101, 104, 105, 108], "kind": [5, 6, 7, 10, 97, 98], "addit": [5, 7, 9, 12, 13, 16, 36, 38, 40, 44, 51, 54, 56, 60, 63, 71, 80, 81, 88, 89, 90, 91, 95, 96, 97, 100, 101, 103, 106, 107], "depend": [5, 7, 9, 12, 13, 15, 16, 38, 42, 46, 48, 59, 61, 65, 72, 75, 76, 85, 97, 107], "instal": [5, 7, 9, 12, 38, 40, 42, 43, 44, 46, 61, 62, 77, 79, 97], "pip": [5, 7, 9, 12, 38, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "development": [5, 7, 9, 12, 38], "git": [5, 7, 9, 12, 38, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "github": [5, 7, 9, 12, 38, 40, 41, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108], "com": [5, 7, 9, 12, 38, 40, 41, 43, 48, 59, 72, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "egg": [5, 7, 9, 12, 38, 85, 98], "label_nam": [5, 7, 8, 10, 11, 15, 21, 34, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "image_kei": [5, 10, 13, 93, 97], "interfac": [5, 9, 10, 56, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "librari": [5, 10, 44, 56, 68, 71, 72, 85, 89, 91, 96, 97, 98, 99], "goal": [5, 108], "track": [5, 7, 16, 17, 85, 91, 98, 99, 101], "intermedi": [5, 9, 92], "statist": [5, 10, 13, 16, 25, 29, 39, 63, 64, 71, 92, 95, 96, 97, 100, 101], "convert": [5, 10, 15, 37, 40, 44, 52, 57, 60, 63, 70, 79, 83, 86, 89, 90, 93, 96, 97, 98, 99, 100, 103, 104, 105], "hug": [5, 10, 15, 93], "face": [5, 10, 15, 19, 93, 98, 104], "kei": [5, 7, 10, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 51, 63, 64, 70, 72, 91, 92, 93, 96, 99, 101, 103, 105], "string": [5, 10, 13, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 44, 55, 59, 63, 64, 76, 80, 83, 84, 89, 95, 96, 97, 99, 103, 104, 110], "dictionari": [5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 50, 59, 63, 64, 67, 68, 70, 71, 91, 92, 95, 96, 101, 103, 104, 105], "path": [5, 15, 40, 43, 44, 71, 90, 91, 97, 99, 105], "local": [5, 7, 10, 15, 40, 41, 44, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "text": [5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 45, 51, 72, 81, 83, 84, 85, 87, 91, 92, 94, 98, 99, 100, 101, 102, 103, 106], "txt": [5, 15, 110], "csv": [5, 15, 88, 89, 95, 96, 100, 108], "json": [5, 15], "hub": [5, 15], "multiclass": [5, 15, 18, 51, 59, 63, 104], "regress": [5, 7, 10, 11, 13, 15, 17, 19, 24, 33, 35, 37, 89, 91, 92, 96, 102, 103, 106], "multilabel": [5, 10, 11, 15, 17, 18, 24, 28, 35, 37, 52, 104], "imag": [5, 9, 13, 39, 44, 68, 70, 71, 72, 77, 79, 80, 85, 91, 92, 94, 98, 99, 100, 102, 103, 104, 105, 107, 109], "field": [5, 10, 40, 44], "themselv": [5, 88, 89, 97, 108], "pil": [5, 93], "cleanvis": [5, 10, 13, 97], "level": [5, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 54, 58, 81, 83, 92, 93, 99, 102, 104, 109], "load_dataset": [5, 15, 93], "glue": 5, "sst2": 5, "properti": [5, 9, 13, 15, 16, 37, 40, 44, 97], "has_label": [5, 15], "class_nam": [5, 15, 23, 39, 45, 64, 71, 80, 84, 85, 98, 101, 105, 109, 110], "empti": [5, 15, 49, 63, 92, 97, 99, 104], "find_issu": [5, 6, 7, 8, 10, 11, 13, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_typ": [5, 6, 7, 8, 10, 11, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "sort": [5, 13, 19, 43, 46, 51, 63, 65, 68, 70, 71, 73, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 108, 109, 110], "common": [5, 10, 13, 16, 19, 85, 92, 94, 97, 98, 99, 100, 101, 104, 105, 109], "real": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "world": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "interact": [5, 13, 19, 96, 99], "thereof": [5, 13, 19], "insight": [5, 13, 19, 71, 103], "best": [5, 9, 10, 13, 19, 50, 63, 73, 88, 89, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 110], "properli": [5, 10, 43, 50, 54, 59, 60, 77, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 106, 108, 109], "respect": [5, 40, 44, 68, 71, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105], "lexicograph": [5, 50, 59, 90, 91, 92, 93, 95, 96, 100, 101, 104], "squar": [5, 59, 75, 98, 108], "csr": [5, 54, 97], "evenli": 5, "omit": [5, 70, 71, 93, 97, 105], "itself": [5, 35, 40, 44, 54, 97, 105], "three": [5, 10, 39, 63, 64, 75, 80, 88, 90, 91, 92, 95, 98, 101, 103, 107, 108, 109, 110], "indptr": [5, 97], "wise": 5, "start": [5, 7, 10, 37, 40, 41, 44, 51, 85, 104, 110], "th": [5, 10, 45, 50, 58, 59, 63, 65, 68, 70, 71, 72, 81, 83, 84, 96, 104, 105, 110], "ascend": [5, 39, 64, 93, 101], "segment": [5, 77, 79, 80, 102], "reflect": [5, 10, 54, 88, 89, 95, 96, 100, 103, 105, 106, 108], "maintain": [5, 62], "kneighbors_graph": [5, 21, 56, 95], "illustr": [5, 97], "todens": 5, "second": [5, 51, 59, 71, 73, 91, 95, 99, 101, 110], "duplic": [5, 9, 24, 25, 40, 44, 54, 85, 91, 97, 100, 101, 108], "explicit": 5, "precend": 5, "collect": [5, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 63, 97, 99, 103, 110], "unspecifi": [5, 13, 19, 46, 65], "interest": [5, 13, 19, 25, 80, 84, 88, 89, 96, 97, 100, 101, 108, 109, 110], "constructor": [5, 10, 11, 13, 19, 26, 33, 54, 56], "issuemanag": [5, 9, 13, 16, 17, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 36], "respons": [5, 13, 19, 25, 56, 75, 76, 97, 98, 108, 110], "random_st": [5, 88, 90, 91, 92, 93, 97, 100, 101, 104, 106], "lab": [5, 6, 8, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 43, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108], "comprehens": [5, 85, 93, 97, 100, 104, 108], "nbr": 5, "n_neighbor": [5, 10, 21, 54, 56, 72, 97], "mode": [5, 12, 21, 40, 43, 44, 95, 106], "4x4": 5, "float64": [5, 29, 40, 44, 83], "compress": [5, 10, 54, 59, 77, 79, 97], "toarrai": [5, 54, 97], "NOT": [5, 43, 96], "23606798": 5, "41421356": [5, 54], "configur": [5, 19, 51, 92], "suppos": [5, 10, 68, 88, 89, 106, 108], "who": [5, 70, 88, 95, 97, 101, 110], "manag": [5, 8, 9, 10, 13, 16, 17, 18, 19, 20, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 62, 91, 99], "clean_learning_kwarg": [5, 10, 11, 26, 33, 99, 108], "labelissuemanag": [5, 10, 17, 24, 26], "prune_method": [5, 86], "prune_by_noise_r": [5, 46, 65, 101], "report": [5, 7, 10, 12, 13, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 84, 85, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108, 110], "include_descript": [5, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36], "show_summary_scor": [5, 13, 36, 97, 100], "show_all_issu": [5, 13, 36, 97, 100], "summari": [5, 7, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 45, 61, 62, 64, 69, 78, 79, 81, 82, 83, 86, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 108, 109, 110], "show": [5, 7, 29, 40, 44, 50, 59, 71, 80, 84, 88, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106, 108, 109, 110], "suffer": [5, 10, 13, 16, 25, 65, 73, 84, 97, 110], "onc": [5, 10, 25, 39, 40, 44, 88, 91, 99, 100, 101, 104, 105], "familiar": [5, 97], "overal": [5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 45, 51, 63, 64, 67, 70, 71, 75, 79, 80, 81, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 105, 110], "sever": [5, 7, 10, 13, 15, 16, 25, 40, 43, 44, 46, 67, 70, 72, 73, 79, 83, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 105, 106, 110], "compar": [5, 63, 72, 83, 91, 92, 95, 97, 100, 101, 105], "issue_summari": [5, 7, 10, 13, 16, 97], "With": [5, 9, 10, 43, 89, 96, 99, 101, 103, 108, 109, 110], "usag": [5, 43, 62], "usual": [5, 15, 35, 36, 93, 103, 108], "ti": [5, 63], "exhibit": [5, 7, 10, 13, 16, 80, 90, 91, 92, 93, 95, 96, 100, 101, 105], "ie": [5, 75], "likelihood": [5, 10, 43, 45, 46, 65, 70, 72, 73, 77, 81, 97], "wherea": [5, 10, 59, 65, 88, 89, 97, 107], "outlier": [5, 9, 11, 17, 24, 25, 34, 47, 54, 73, 85, 91, 92, 97, 100, 101, 102, 108], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 101, 108], "global": [5, 7, 10, 25, 40, 44, 98], "non_iid": [5, 10, 11, 17, 29, 92, 93, 95, 96, 97, 100, 101], "hypothesi": [5, 97], "iid": [5, 7, 9, 29, 85, 95, 100, 101], "never": [5, 90, 100, 101, 104, 106, 107], "someth": [5, 7, 10, 40, 44, 73, 105], "123": [5, 91, 92], "456": [5, 88, 89, 90], "nearest_neighbor": 5, "7": [5, 10, 51, 52, 62, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 103, 104, 105, 106, 108, 109, 110], "9": [5, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 45, 51, 52, 67, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 101, 103, 104, 105, 106, 108, 109, 110], "distance_to_nearest_neighbor": [5, 11, 91, 92, 93, 95, 96, 100, 101], "789": 5, "get_issu": [5, 10, 13, 16, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_nam": [5, 6, 7, 10, 13, 16, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 90, 91, 92, 93, 95, 96, 97, 100, 101], "focu": [5, 10, 13, 16, 96, 97, 100, 109, 110], "full": [5, 10, 13, 16, 43, 62, 71, 93, 100, 110], "summar": [5, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 64, 80, 84, 85, 109], "specific_issu": [5, 13, 16], "lie": [5, 10, 72, 73, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101], "get_issue_summari": [5, 10, 13, 16, 92, 97], "get_info": [5, 10, 13, 16, 92, 96, 97, 98], "yet": [5, 20, 30, 62, 98, 100, 103], "list_possible_issue_typ": [5, 17, 18], "regist": [5, 7, 17, 18, 20, 30, 40, 44, 91], "rtype": [5, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44], "registri": [5, 17, 18], "list_default_issue_typ": [5, 17, 18], "folder": [5, 90, 91, 93], "load": [5, 15, 43, 71, 93, 98, 99, 100, 101, 105, 106, 109, 110], "futur": [5, 10, 25, 40, 44, 63, 85, 91, 96], "overwrit": [5, 91], "separ": [5, 39, 51, 67, 91, 92, 93, 97, 99, 100, 105, 107], "static": 5, "rememb": [5, 96, 99, 100, 101], "part": [5, 10, 40, 44, 46, 68, 70, 71, 90, 91, 97, 98, 100, 109, 110], "ident": [5, 10, 25, 59, 96, 97], "datalab": [6, 8, 11, 13, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 85, 88, 89, 98, 100, 103, 108], "walk": [7, 100], "alongsid": [7, 13, 40, 44, 91, 99], "pre": [7, 8, 10, 40, 44, 85, 91, 92, 108], "runtim": [7, 40, 43, 44, 75, 77, 79, 90, 93, 99, 100], "issue_manager_factori": [7, 17, 91], "myissuemanag": [7, 17], "myissuemanagerforregress": 7, "decor": [7, 17], "ll": [7, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "thing": [7, 44, 89, 97, 101, 108], "next": [7, 63, 85, 88, 89, 90, 95, 96, 97, 99, 103, 105, 108, 110], "dummi": 7, "randint": [7, 34, 51, 91, 92, 97], "mark": [7, 10, 86, 105, 106, 108], "regard": [7, 92, 100, 101], "rand": [7, 51, 54, 91, 92, 97], "is_": [7, 10, 91], "_issu": [7, 10, 91], "issue_score_kei": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "whole": [7, 10, 29, 40, 44, 92, 97], "make_summari": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "popul": [7, 96, 100], "verbosity_level": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "std": [7, 105], "raw_scor": 7, "bit": 7, "involv": [7, 43, 80, 84, 97, 99, 104], "intermediate_arg": 7, "min": [7, 51, 70, 83, 91, 99, 106], "sin_filt": 7, "sin": 7, "arang": [7, 97], "kernel": [7, 97], "affect": [7, 10, 40, 44, 55, 77, 83, 96, 97, 99], "easili": [7, 10, 49, 86, 88, 89, 90, 92, 95, 96, 100, 101, 103, 104, 106, 107, 108, 109], "hard": [7, 44, 85, 98, 106], "sai": [7, 10, 40, 44, 97, 104, 109], "anoth": [7, 10, 25, 39, 43, 55, 58, 70, 73, 89, 95, 96, 97, 99, 101, 103, 106], "try": [7, 9, 10, 43, 46, 62, 63, 77, 79, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 107, 108, 109], "won": [7, 40, 44, 91, 92, 99, 104], "issue_manag": [7, 10, 12, 13, 16, 18, 21, 22, 23, 26, 28, 29, 30, 31, 33, 34, 91], "instanti": [7, 19, 43, 62, 72, 89, 90, 92, 95], "477762": 7, "286455": 7, "term": [7, 10, 49, 59, 71, 90, 91, 92, 93, 95, 96, 100, 101], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 22, 31, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 105, 106, 108, 109, 110], "003042": 7, "058117": 7, "11": [7, 10, 62, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "121908": 7, "15": [7, 57, 62, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "169312": 7, "17": [7, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 91, 92, 97, 98, 100, 101], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 34, 85, 100], "group": [8, 9, 29, 34, 85, 98, 100, 105, 110], "dbscan": [8, 10, 34], "hdbscan": 8, "etc": [8, 10, 25, 35, 40, 44, 49, 62, 63, 81, 85, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108], "sensit": [8, 10, 57, 97, 100], "ep": [8, 34, 71], "radiu": 8, "min_sampl": [8, 34], "kmean": [8, 97], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 34, 97], "cluster_id": [8, 10, 11, 34, 97], "labels_": 8, "underperforming_group": [8, 10, 11, 17, 24, 92, 93, 95, 96, 97, 100, 101], "search": [9, 10, 23, 29, 30, 47, 53, 54, 55, 58, 75, 97, 99, 100, 107], "nondefault": 9, "Near": [9, 99], "imbal": [9, 24, 67, 72, 73, 92], "spuriou": [9, 13, 93], "correl": [9, 13, 93], "null": [9, 11, 17, 24, 92, 93, 96, 100, 101], "togeth": [9, 10, 49, 89, 91, 92, 93, 95, 96, 100, 101, 108, 110], "built": [9, 51, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "own": [9, 40, 42, 44, 56, 61, 67, 68, 71, 77, 81, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 108, 109, 110], "prerequisit": 9, "basic": [9, 44, 62, 97, 100, 106], "fulli": [9, 10, 40, 44, 62, 99], "platform": [9, 10, 85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 107, 108], "write": [9, 10], "code": [9, 10, 40, 44, 49, 59, 62, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "being": [9, 10, 13, 16, 39, 40, 44, 46, 51, 58, 59, 73, 88, 95, 99, 100, 101, 108, 109], "100x": [9, 10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "faster": [9, 10, 43, 72, 75, 77, 79, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "intellig": [9, 10, 100], "quickli": [9, 10, 41, 88, 90, 93, 95, 96, 99, 100, 104, 106, 107, 109, 110], "fix": [9, 10, 63, 88, 89, 92, 95, 96, 97, 98, 100, 101, 104, 106, 107, 108], "scientist": [9, 10], "million": [9, 10, 110], "thank": [9, 10], "ai": [9, 10, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 106, 108, 110], "suggest": [9, 10, 39, 63, 64, 70, 89, 93, 96, 97, 99, 108], "power": [9, 10, 93, 98, 101, 110], "automl": [9, 10, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 107, 108], "system": [9, 10, 90, 93, 109], "foundat": [9, 10, 85, 88, 89, 92, 95, 96, 97, 98, 101, 104, 106, 107, 108], "improv": [9, 10, 63, 88, 89, 92, 93, 98, 99, 101, 102, 108, 109], "click": [9, 10, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "tune": [9, 10, 89, 90, 96, 98, 100, 106], "serv": [9, 10, 16, 19, 103], "auto": [9, 10, 88, 89, 92, 98, 99, 100, 108], "free": [9, 10, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "page": [10, 92, 99, 100, 101], "variou": [10, 16, 33, 42, 60, 61, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105], "why": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "matter": [10, 39, 64], "didn": [10, 97, 100], "plu": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "ye": [10, 11], "near_dupl": [10, 11, 17, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "class_imbal": [10, 11, 17, 23, 92, 93, 95, 96, 97, 100, 101], "data_valu": [10, 11, 17, 24, 97], "No": [10, 11, 88, 89, 96, 97, 99], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 70, 88, 89, 110], "issue_scor": 10, "atyp": [10, 72, 91, 92, 93, 95, 96, 100, 101, 106], "datapoint": [10, 34, 46, 51, 59, 73, 76, 85, 88, 89, 90, 91, 92, 95, 96, 99, 100, 107, 108], "is_issu": [10, 25], "primarili": 10, "former": [10, 40, 44], "investig": [10, 90, 97], "expertis": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "interpret": [10, 98, 99, 101, 104, 108], "annot": [10, 39, 50, 63, 64, 65, 67, 68, 70, 71, 80, 83, 84, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 105, 109], "dissimilar": [10, 95, 96], "preced": 10, "incorrect": [10, 70, 73, 76, 88, 90, 91, 92, 93, 95, 96, 97, 100, 101, 105, 108], "due": [10, 43, 46, 73, 77, 79, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108], "appear": [10, 39, 50, 64, 65, 68, 76, 92, 93, 95, 96, 97, 100, 108, 109], "now": [10, 13, 43, 86, 88, 89, 90, 92, 97, 99, 100, 103, 105, 106, 108, 110], "token": [10, 45, 58, 79, 80, 81, 82, 83, 84, 99, 101, 102], "hamper": [10, 93, 98], "analyt": [10, 85, 97, 99, 103], "lead": [10, 70, 73, 93, 97, 100, 105], "draw": [10, 91, 92], "conclus": [10, 96], "let": [10, 40, 44, 72, 73, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "sort_valu": [10, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 108], "head": [10, 88, 89, 90, 92, 93, 95, 96, 97, 98, 100, 101, 103, 108], "97": [10, 88, 98, 99, 100, 101, 105, 108, 110], "064045": 10, "58": [10, 88, 92, 97, 98, 101, 105], "680894": 10, "41": [10, 97, 98, 100, 105, 108, 110], "746043": 10, "794894": 10, "98": [10, 98, 99, 100, 108], "802911": 10, "give": [10, 51, 73, 101, 103, 109], "li": [10, 72], "especi": [10, 88, 89, 93, 97, 99, 108], "veri": [10, 39, 64, 68, 70, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108], "rare": [10, 46, 71, 91, 92, 93, 95, 96, 99, 100, 101], "anomal": [10, 73, 91, 92, 93, 95, 96, 100, 101], "articl": [10, 43, 99], "blog": 10, "unexpect": [10, 40, 44, 96], "consequ": 10, "inspect": [10, 89, 90, 92, 93, 100, 101, 105, 108], "011562": 10, "62": [10, 97, 100, 101, 105, 108], "019657": 10, "22": [10, 90, 91, 93, 97, 98, 100, 101, 104, 105, 110], "035243": 10, "040907": 10, "42": [10, 51, 96, 97, 98, 105, 110], "056865": 10, "smaller": [10, 72, 104, 105], "extrem": [10, 13, 91, 92, 93, 95, 96, 97, 99, 100, 101], "record": [10, 40, 44, 90, 95, 108], "abbrevi": 10, "misspel": 10, "typo": [10, 84], "resolut": 10, "video": [10, 98], "audio": [10, 91, 92, 94, 99], "minor": [10, 58], "variat": 10, "translat": [10, 100], "d": [10, 57, 88, 95, 96, 97, 99, 100, 101, 104, 108, 110], "constant": [10, 34, 75], "median": [10, 33, 57], "question": [10, 25, 85, 101], "nearli": [10, 25, 92, 93, 95, 96], "awar": [10, 86, 101], "presenc": [10, 54, 56, 101], "36": [10, 97, 98, 100, 110], "066009": 10, "80": [10, 41, 88, 95, 100, 104, 108], "003906": 10, "093245": 10, "005599": 10, "27": [10, 95, 97, 98, 100, 101, 105, 110], "156720": 10, "009751": 10, "72": [10, 97, 98, 100, 101, 104, 108], "signific": [10, 88, 89, 92, 95, 96, 98, 100, 101, 104, 106, 108], "violat": [10, 85, 95, 96, 97, 100, 101], "assumpt": [10, 95, 96, 97, 100, 101], "changepoint": [10, 95, 96, 100, 101], "shift": [10, 54, 56, 95, 96, 100, 101], "drift": [10, 92, 95, 97, 100, 101], "autocorrel": [10, 95, 96, 100, 101], "almost": [10, 95, 96, 100, 101], "adjac": [10, 54, 95, 96, 100, 101], "tend": [10, 39, 49, 95, 96, 100, 101, 109, 110], "sequenti": [10, 40, 44, 62, 93], "pai": [10, 96, 97], "attent": [10, 97], "realli": [10, 89, 96, 100, 103, 109], "mere": 10, "highlight": [10, 80, 84, 91, 92, 95, 97, 109], "necessarili": [10, 63, 71, 96, 100, 101], "wrong": [10, 63, 68, 70, 86, 89, 91, 92, 96, 99, 100, 101, 105], "gap": 10, "b": [10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 58, 59, 83, 88, 95, 96, 97, 98, 99, 100, 101, 107, 110], "x1": [10, 68, 71, 105], "x2": [10, 68, 71, 105], "10th": 10, "100th": 10, "90": [10, 83, 88, 95, 100, 101, 107, 108], "similarli": [10, 40, 44, 91, 93, 95, 99, 100, 105], "associ": [10, 15, 19, 35, 37, 40, 44, 71, 103], "blogpost": 10, "proper": [10, 59, 63, 68, 71, 88, 93, 96, 99, 103, 105], "scenario": [10, 54, 56, 73, 91, 92], "underli": [10, 45, 56, 72, 81, 83, 110], "stem": [10, 72, 106], "evolv": 10, "influenc": 10, "act": [10, 70, 91], "accordingli": [10, 35, 54], "emploi": [10, 104, 106], "partit": [10, 107], "ahead": 10, "good": [10, 40, 44, 57, 62, 64, 70, 73, 77, 79, 80, 85, 93, 97, 100], "problem": [10, 35, 43, 51, 80, 85, 91, 92, 93, 96, 97, 99], "deploy": [10, 88, 89, 101, 108], "overlook": [10, 70, 105], "fact": 10, "thu": [10, 39, 44, 64, 88, 90, 95, 96, 100, 101, 107, 110], "diagnos": [10, 92, 99], "24": [10, 90, 97, 98, 100, 101, 103, 105, 108], "681458": 10, "37": [10, 91, 97, 98, 100], "804582": 10, "64": [10, 44, 88, 93, 95, 97, 101, 105], "810646": 10, "815691": 10, "78": [10, 88, 95, 98, 100, 101, 105, 108], "834293": 10, "Be": [10, 44], "cautiou": 10, "behavior": [10, 19, 39, 40, 44, 71, 99], "rarest": [10, 92, 100], "q": [10, 97, 105], "subpar": 10, "special": [10, 54, 58], "techniqu": [10, 105], "smote": 10, "asymmetr": [10, 39], "28": [10, 93, 96, 97, 98, 100, 101, 103, 110], "75": [10, 51, 91, 92, 97, 98, 100, 103, 104, 105, 108, 110], "33": [10, 40, 44, 97, 98, 100, 105], "68": [10, 88, 98, 100, 101, 105], "excess": [10, 93], "dark": [10, 97, 109], "bright": [10, 110], "blurri": [10, 93, 97], "lack": [10, 62, 97, 100], "unusu": [10, 105, 106], "discuss": [10, 99], "earlier": [10, 89, 110], "unintend": [10, 95, 96, 97], "relationship": [10, 39], "irrelev": 10, "exploit": 10, "fail": [10, 15], "unseen": 10, "hold": [10, 15], "aris": 10, "captur": [10, 39, 90, 105, 106, 109], "environment": 10, "preprocess": [10, 88, 89, 92, 95, 97, 106, 108], "systemat": [10, 80, 84, 103], "photograph": 10, "uncorrelated": [10, 97], "strongli": [10, 96, 97], "minu": [10, 73], "sole": [10, 75, 88, 91, 100, 103, 106], "review": [10, 88, 89, 92, 95, 96, 98, 99, 100, 101, 105, 108, 109, 110], "latch": 10, "onto": 10, "troublesom": 10, "spurious_correl": [10, 97], "correlations_df": [10, 97], "blurry_scor": [10, 97], "559": [10, 100], "dark_scor": [10, 93, 97], "808": 10, "light_scor": [10, 97], "723": [10, 95, 100], "odd_size_scor": [10, 97], "957": 10, "odd_aspect_ratio_scor": [10, 97], "835": 10, "grayscale_scor": [10, 97], "003": 10, "spurious": 10, "low_information_scor": [10, 93, 97], "688": [10, 100, 108], "categor": [10, 72, 87, 88, 91, 92, 94, 99, 100, 108], "characterist": [10, 39, 97], "grayscal": [10, 93, 97], "cluster": [10, 21, 34, 100], "slice": [10, 100], "poor": [10, 97, 100], "subpopul": [10, 100], "faq": [10, 85, 92, 93, 95, 96, 102], "get_self_confidence_for_each_label": [10, 51, 73], "r": [10, 43, 75, 91, 92, 97, 108, 109], "tabular": [10, 85, 87, 91, 92, 94, 97, 99, 100, 103], "encod": [10, 52, 71, 77, 80, 88, 89, 95, 96, 99, 100, 108, 109], "71": [10, 97, 98, 100, 101, 105, 108], "70": [10, 83, 95, 97, 100], "69": [10, 100, 101, 108], "subgroup": [10, 97], "wors": [10, 97, 103], "ratio": [10, 97], "miss": [10, 30, 40, 44, 59, 68, 70, 99, 100, 105, 108], "pattern": [10, 97], "isn": [10, 20, 30], "scalabl": 10, "sacrific": 10, "One": [10, 59, 72, 99], "quantif": 10, "39": [10, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 108, 109, 110], "32": [10, 90, 91, 97, 98, 100, 103, 105], "valuabl": [10, 21, 97], "exert": [10, 92], "possible_issue_typ": 10, "label_kwarg": 10, "outlier_kwarg": 10, "near_duplicate_kwarg": 10, "non_iid_kwarg": 10, "class_imbalance_kwarg": 10, "underperforming_group_kwarg": 10, "null_kwarg": 10, "data_valuation_kwarg": 10, "health_summary_paramet": [10, 24, 26, 33], "health_summari": [10, 26, 39, 85, 98], "health_summary_kwarg": 10, "tandem": [10, 98], "view": [10, 40, 44, 45, 46, 79, 81, 83, 85, 88, 89, 90, 91, 92, 95, 96, 98, 100, 101, 103, 104, 105, 106, 107, 108, 110], "strength": [10, 57, 71, 97], "scaling_factor": [10, 31, 57], "ood_kwarg": 10, "outofdistribut": [10, 31, 72, 106], "outsid": [10, 99, 104], "outlierissuemanag": [10, 17, 24, 31], "nearduplicateissuemanag": [10, 17, 22, 24], "noniidissuemanag": [10, 17, 24, 29], "num_permut": [10, 29], "permut": [10, 29], "significance_threshold": [10, 29], "signic": 10, "noniid": [10, 24], "classimbalanceissuemanag": [10, 17, 23, 24], "underperforminggroupissuemanag": [10, 17, 24, 34], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 34], "filter_cluster_id": [10, 24, 34], "clustering_kwarg": [10, 34], "nullissuemanag": [10, 17, 24, 30], "datavaluationissuemanag": [10, 17, 21, 24], "codeblock": 10, "demonstr": [10, 43, 54, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109], "howev": [10, 40, 44, 54, 59, 88, 89, 90, 93, 95, 96, 97, 100, 103, 107, 109], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 64], "versa": [10, 64], "light": [10, 93, 97, 98, 105, 109], "29": [10, 93, 97, 98, 100, 103, 104, 105, 109, 110], "low_inform": [10, 93, 97], "odd_aspect_ratio": [10, 93, 97], "35": [10, 91, 97, 98, 100, 103, 104, 105], "odd_siz": [10, 93, 97], "doc": [10, 40, 44, 72, 85, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 106, 108, 110], "spurious_correlations_kwarg": 10, "enough": [10, 43, 59, 97, 99], "label_scor": [11, 26, 28, 33, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "is_outlier_issu": [11, 91, 92, 93, 95, 96, 97, 100, 101], "outlier_scor": [11, 31, 91, 92, 93, 95, 96, 97, 100, 101, 106], "is_near_duplicate_issu": [11, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_scor": [11, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_set": [11, 22, 24, 91, 92, 93, 95, 96, 99, 100, 101], "is_non_iid_issu": [11, 92, 95, 96, 97, 100, 101], "non_iid_scor": [11, 29, 92, 95, 96, 97, 100, 101], "is_class_imbalance_issu": [11, 92, 97, 100], "class_imbalance_scor": [11, 23, 92, 97, 100], "is_underperforming_group_issu": [11, 92, 97, 100], "underperforming_group_scor": [11, 34, 92, 97, 100], "is_null_issu": [11, 92, 97, 100], "null_scor": [11, 30, 92, 97, 100], "is_data_valuation_issu": [11, 97], "data_valuation_scor": [11, 21, 97], "studio": [12, 85, 88, 89, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "data_issu": [12, 13, 18, 19, 36], "issue_find": [12, 18], "factori": [12, 18, 19], "model_output": [12, 18], "incorpor": [13, 86, 101], "vision": [13, 93], "create_imagelab": [13, 14], "huggingfac": [13, 90, 91, 92, 93, 99], "imagelabdataissuesadapt": [13, 14], "strategi": [13, 16, 51, 97, 99], "dataissu": [13, 16, 18, 19, 36], "_infostrategi": [13, 16], "basi": [13, 16], "filter_based_on_max_preval": 13, "max_num": 13, "collect_issues_from_imagelab": [13, 16], "collect_issues_from_issue_manag": [13, 16], "collect_statist": [13, 16], "reus": [13, 16, 25], "avoid": [13, 16, 40, 43, 44, 46, 54, 59, 65, 68, 71, 75, 77, 79, 91, 92, 99, 100], "recomput": [13, 16, 89], "weighted_knn_graph": [13, 16], "issue_manager_that_computes_knn_graph": [13, 16], "set_health_scor": [13, 16], "health": [13, 16, 26, 39, 64, 85], "correlationvisu": [13, 14], "visual": [13, 68, 69, 71, 88, 91, 92, 93, 108, 110], "title_info": 13, "ncol": [13, 93, 106], "cell_siz": 13, "correlationreport": [13, 14], "anyth": [13, 101], "imagelabreporteradapt": [13, 14], "get_report": [13, 36], "report_str": [13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36], "imagelabissuefinderadapt": [13, 14], "issuefind": [13, 18, 19, 36], "get_available_issue_typ": [13, 19], "handle_spurious_correl": [13, 14], "imagelab_issu": 13, "_": [13, 22, 23, 25, 26, 28, 29, 30, 33, 34, 51, 58, 59, 88, 90, 91, 93, 97, 98, 101, 104], "imagelab": [14, 16, 18], "except": [15, 40, 44, 62, 73, 91, 92, 93, 100, 103], "dataformaterror": [15, 18], "add_not": 15, "with_traceback": 15, "tb": 15, "__traceback__": 15, "datasetdicterror": [15, 18], "datasetdict": 15, "datasetloaderror": [15, 18], "dataset_typ": 15, "sublist": 15, "map_to_int": 15, "abc": [15, 25, 35], "is_avail": [15, 93], "central": [16, 110], "repositori": 16, "get_data_statist": [16, 18], "concret": 17, "subclass": [17, 40, 44, 72, 91], "regressionlabelissuemanag": [17, 24, 32, 33], "multilabelissuemanag": [17, 24, 27, 28], "from_str": [17, 37, 47, 51], "my_issu": 17, "logic": [17, 37, 43, 46, 77, 79, 100], "modeloutput": [18, 35], "multiclasspredprob": [18, 35], "regressionpredict": [18, 35], "multilabelpredprob": [18, 35], "instati": 19, "public": [19, 97, 100, 101, 105, 109, 110], "creation": [19, 44, 97], "execut": [19, 40, 44, 91, 99, 105], "coordin": [19, 68, 70, 71, 105, 110], "At": [19, 71, 99], "direct": [20, 30, 40, 44, 56, 62], "vstack": [21, 59, 93, 98, 99, 101, 103, 104], "25": [21, 29, 40, 51, 57, 92, 93, 97, 98, 100, 101, 103, 104, 105, 110], "classvar": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "short": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 58, 59], "item": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 91, 92, 93, 99, 101, 103, 104], "some_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "additional_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "default_threshold": [21, 24, 31], "collect_info": [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "info_to_omit": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "compos": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 40, 44, 89, 96, 106], "is_x_issu": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "x_score": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_a": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b1": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b2": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "occurr": [22, 23, 25, 29, 30, 31, 34, 58], "median_nn_dist": 22, "bleed": [24, 27, 32, 42], "edg": [24, 27, 32, 42, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108, 110], "sharp": [24, 27, 32, 42], "get_health_summari": [24, 26], "ood": [24, 31, 72, 73, 106], "simplified_kolmogorov_smirnov_test": [24, 29], "outlier_cluster_label": [24, 34], "no_underperforming_cluster_id": [24, 34], "perform_clust": [24, 34], "get_underperforming_clust": [24, 34], "find_issues_with_predict": [24, 32, 33], "find_issues_with_featur": [24, 32, 33], "believ": [25, 109], "priori": [25, 101], "abstract": [25, 35], "applic": [26, 63, 97, 99, 101, 103, 110], "typevar": [26, 28, 40, 44, 58, 67, 70, 71], "scalartyp": [26, 28], "covari": [26, 28, 75, 108], "summary_dict": 26, "neighbor_histogram": 29, "non_neighbor_histogram": 29, "kolmogorov": 29, "smirnov": 29, "largest": [29, 43, 51, 54, 73, 77, 79, 105, 109], "empir": [29, 50, 63], "cumul": 29, "ecdf": 29, "histogram": [29, 95, 97, 108], "absolut": [29, 33], "trial": 29, "null_track": 30, "extend": [30, 52, 62, 93, 97, 100, 105, 106, 110], "superclass": 30, "arbitrari": [30, 39, 79, 83, 91, 106, 108], "prompt": 30, "address": [30, 89, 91, 92, 96, 99], "enabl": [30, 44, 56, 100], "37037": 31, "q3_avg_dist": 31, "iqr_avg_dist": 31, "median_outlier_scor": 31, "issue_threshold": 31, "multipli": [33, 57], "deleg": 33, "confus": [34, 35, 39, 40, 44, 46, 59, 71, 89, 110], "50": [34, 44, 97, 99, 100, 101, 103, 105, 106, 108], "keepdim": [34, 99], "signifi": 34, "absenc": 34, "int64": [34, 90, 100, 103], "npt": 34, "int_": 34, "id": [34, 63, 91, 93, 97, 99, 103], "unique_cluster_id": 34, "exclud": [34, 36, 45, 80, 84, 91, 110], "worst": [34, 51, 103], "performed_clust": 34, "worst_cluster_id": 34, "convent": [35, 37], "subject": [35, 37, 100], "meant": [35, 37], "Not": [35, 56], "mainli": [35, 106, 110], "content": [35, 72, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "fetch": [35, 43, 90, 92, 97, 99], "datset": 36, "enum": [37, 51], "qualnam": [37, 51], "boundari": [37, 51, 91, 92], "continu": [37, 62, 88, 89, 93, 96, 99, 103, 105, 108, 110], "binari": [37, 51, 59, 65, 67, 101, 110], "simultan": [37, 108], "task_str": 37, "is_classif": 37, "__contains__": [37, 47, 51], "member": [37, 40, 44, 51, 91], "typeerror": [37, 51], "12": [37, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "__getitem__": [37, 47, 51], "match": [37, 39, 40, 44, 46, 51, 63, 64, 73, 91, 92, 93, 98, 105, 107, 109], "__iter__": [37, 47, 51], "__len__": [37, 47, 51], "alias": [37, 51], "is_regress": 37, "is_multilabel": 37, "overview": [39, 54, 88, 89, 90, 92, 93, 95, 96, 103, 105, 106, 108, 110], "modifi": [39, 40, 43, 44, 54, 56, 59, 99, 100, 101], "rank_classes_by_label_qu": [39, 92], "merg": [39, 54, 58, 85, 98, 99, 100, 110], "find_overlapping_class": [39, 99, 101], "problemat": [39, 64, 80, 84, 90, 105, 110], "unnorm": [39, 64, 101], "abov": [39, 40, 43, 44, 56, 59, 63, 70, 71, 73, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "model_select": [39, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 108], "cross_val_predict": [39, 44, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 107, 108], "get_data_labels_from_dataset": 39, "yourfavoritemodel": [39, 101], "cv": [39, 51, 88, 90, 91, 92, 95, 97, 100, 101, 103], "df": [39, 59, 84, 90, 97, 99], "overall_label_qu": [39, 64], "col": 39, "prob": [39, 58, 101, 107], "divid": [39, 64, 73], "label_nois": [39, 64], "human": [39, 98, 109, 110], "clearli": [39, 73, 93, 105, 109], "num": [39, 64, 98, 101], "overlap": [39, 85, 97, 98, 99, 101], "ontolog": 39, "publish": [39, 110], "therefor": [39, 73, 97, 100], "vehicl": [39, 98], "truck": [39, 97, 98, 106, 109], "intuit": [39, 64], "car": [39, 98, 105, 109], "frequent": [39, 63, 97, 99, 100, 108], "l": [39, 40, 44, 68, 70, 71], "class1": 39, "class2": 39, "dog": [39, 59, 64, 66, 80, 98, 99, 106, 107, 110], "cat": [39, 59, 64, 66, 98, 99, 106, 107], "co": [39, 40, 41], "noisy_label": [39, 91, 92, 104], "overlapping_class": 39, "descend": [39, 40, 44, 51, 64, 71], "overall_label_health_scor": [39, 64, 101], "half": [39, 40, 42, 44, 64, 98, 110], "health_scor": [39, 64], "classes_by_label_qu": [39, 92], "cnn": [40, 42, 44, 93], "cifar": [40, 41, 97, 98, 106], "teach": [40, 41], "bhanml": 40, "blob": [40, 97], "master": [40, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108], "call_bn": [40, 42], "bn": 40, "input_channel": 40, "n_output": 40, "dropout_r": 40, "top_bn": 40, "architectur": [40, 44], "shown": [40, 71, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 107, 109, 110], "forward": [40, 41, 42, 44, 93, 103], "overridden": [40, 44], "although": [40, 44, 72, 88, 95, 100], "recip": [40, 44], "afterward": [40, 44], "sinc": [40, 44, 48, 60, 64, 71, 79, 83, 99, 100, 103, 104, 105, 107, 110], "hook": [40, 44, 98], "silent": [40, 43, 44], "t_destin": [40, 42, 44], "__call__": [40, 42, 44, 47, 51], "add_modul": [40, 42, 44], "child": [40, 44], "fn": [40, 44, 71], "recurs": [40, 44, 51], "submodul": [40, 44, 53], "children": [40, 42, 44, 110], "nn": [40, 41, 44, 54, 93], "init": [40, 44, 101], "no_grad": [40, 44, 93, 106], "init_weight": [40, 44], "linear": [40, 44, 89, 93, 96], "fill_": [40, 44], "net": [40, 44, 90, 93, 98], "in_featur": [40, 44], "out_featur": [40, 44], "bia": [40, 44, 93], "tensor": [40, 41, 44, 90, 93, 106], "requires_grad": [40, 44], "bfloat16": [40, 42, 44], "cast": [40, 44, 90], "buffer": [40, 42, 44], "datatyp": [40, 44], "xdoctest": [40, 44], "undefin": [40, 44], "var": [40, 44], "buf": [40, 44], "20l": [40, 44], "1l": [40, 44], "5l": [40, 44], "call_super_init": [40, 42, 44], "immedi": [40, 44, 106], "compil": [40, 42, 44, 62], "cpu": [40, 42, 44, 46, 90, 93], "move": [40, 44, 51, 86, 98], "cuda": [40, 42, 44, 90, 93], "devic": [40, 44, 90, 93, 100], "gpu": [40, 44, 89, 90, 96], "live": [40, 44], "copi": [40, 44, 75, 88, 90, 91, 92, 95, 97, 99, 100, 104, 107, 108], "doubl": [40, 42, 44], "dump_patch": [40, 42, 44], "eval": [40, 42, 44, 93, 104, 106], "dropout": [40, 44], "batchnorm": [40, 44], "grad": [40, 44], "extra_repr": [40, 42, 44], "line": [40, 44, 85, 91, 97, 98, 103, 106, 110], "get_buff": [40, 42, 44], "target": [40, 41, 44, 75, 76, 97, 106, 108], "throw": [40, 44], "get_submodul": [40, 42, 44], "explan": [40, 44], "qualifi": [40, 44], "referenc": [40, 44], "attributeerror": [40, 44], "invalid": [40, 44, 96], "resolv": [40, 44, 97, 110], "get_extra_st": [40, 42, 44], "state_dict": [40, 42, 44], "set_extra_st": [40, 42, 44], "build": [40, 44, 54, 93, 97, 109], "picklabl": [40, 44], "serial": [40, 44], "backward": [40, 44, 93], "break": [40, 44, 93, 105], "pickl": [40, 44, 105], "get_paramet": [40, 42, 44], "net_b": [40, 44], "net_c": [40, 44], "conv": [40, 44], "conv2d": [40, 44, 93], "16": [40, 44, 51, 54, 62, 79, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 109, 110], "kernel_s": [40, 44], "stride": [40, 44], "200": [40, 44, 73, 97, 98, 105, 110], "diagram": [40, 44, 107], "degre": [40, 44], "queri": [40, 44, 54, 56, 92, 93, 97, 99, 100, 104], "named_modul": [40, 42, 44], "o": [40, 44, 57, 58, 90, 91, 92, 98, 99, 100, 101, 104, 105, 110], "transit": [40, 44], "ipu": [40, 42, 44], "load_state_dict": [40, 42, 44], "strict": [40, 44, 51], "persist": [40, 44], "strictli": [40, 44], "inplac": [40, 44, 97, 103], "preserv": [40, 44, 59], "namedtupl": [40, 44], "missing_kei": [40, 44], "unexpected_kei": [40, 44], "runtimeerror": [40, 44], "idx": [40, 44, 59, 60, 71, 91, 93, 97, 99, 100, 101, 103, 105, 106], "named_buff": [40, 42, 44], "prefix": [40, 44, 90, 110], "remove_dupl": [40, 44], "prepend": [40, 44], "running_var": [40, 44], "named_children": [40, 42, 44], "conv4": [40, 44], "conv5": [40, 44], "memo": [40, 44], "named_paramet": [40, 42, 44], "register_backward_hook": [40, 42, 44], "deprec": [40, 44, 48], "favor": [40, 44], "register_full_backward_hook": [40, 42, 44], "removablehandl": [40, 44], "register_buff": [40, 42, 44], "running_mean": [40, 44], "register_forward_hook": [40, 42, 44], "with_kwarg": [40, 44], "always_cal": [40, 44], "possibli": [40, 44, 88, 95], "fire": [40, 44, 98], "register_module_forward_hook": [40, 44], "regardless": [40, 44, 91, 92], "register_forward_pre_hook": [40, 42, 44], "And": [40, 44], "forward_pr": [40, 44], "register_module_forward_pre_hook": [40, 44], "gradient": [40, 44, 93, 95, 108], "grad_input": [40, 44], "grad_output": [40, 44], "technic": [40, 44], "caller": [40, 44], "register_module_full_backward_hook": [40, 44], "register_full_backward_pre_hook": [40, 42, 44], "backward_pr": [40, 44], "register_module_full_backward_pre_hook": [40, 44], "register_load_state_dict_post_hook": [40, 42, 44], "post": [40, 44, 54], "incompatible_kei": [40, 44], "modif": [40, 44, 54], "thrown": [40, 44], "register_modul": [40, 42, 44], "register_paramet": [40, 42, 44], "register_state_dict_pre_hook": [40, 42, 44], "keep_var": [40, 44], "requires_grad_": [40, 42, 44], "autograd": [40, 44], "freez": [40, 44, 89, 90, 96], "finetun": [40, 44], "gan": [40, 44], "share_memori": [40, 42, 44], "share_memory_": [40, 44], "destin": [40, 44], "shallow": [40, 44], "releas": [40, 44, 62, 86, 99], "design": [40, 44, 54], "ordereddict": [40, 44], "detach": [40, 44, 93], "non_block": [40, 44], "memory_format": [40, 44], "channels_last": [40, 44], "Its": [40, 44, 51, 64, 70], "complex": [40, 44, 100], "integr": [40, 44, 56, 85, 99], "asynchron": [40, 44], "host": [40, 44], "pin": [40, 44, 89, 96, 98], "desir": [40, 44, 54, 58, 71], "4d": [40, 44], "ignore_w": [40, 44], "determinist": [40, 44, 90], "1913": [40, 44], "3420": [40, 44], "5113": [40, 44], "2325": [40, 44], "env": [40, 44], "torch_doctest_cuda1": [40, 44], "gpu1": [40, 44], "1914": [40, 44], "5112": [40, 44], "2324": [40, 44], "float16": [40, 44], "cdoubl": [40, 44], "3741": [40, 44], "2382": [40, 44], "5593": [40, 44], "4443": [40, 44], "complex128": [40, 44], "6122": [40, 44], "1150": [40, 44], "to_empti": [40, 42, 44], "storag": [40, 44], "dst_type": [40, 44], "xpu": [40, 42, 44], "zero_grad": [40, 42, 44, 93], "set_to_non": [40, 44], "reset": [40, 44], "context": [40, 44, 105], "noisili": [41, 101], "han": 41, "2018": 41, "cifar_cnn": [41, 42], "loss_coteach": [41, 42], "y_1": 41, "y_2": 41, "forget_r": 41, "class_weight": 41, "logit": [41, 62, 93], "decim": [41, 59], "forget": [41, 51, 110], "rate_schedul": 41, "epoch": [41, 42, 44, 93, 99], "initialize_lr_schedul": [41, 42], "lr": [41, 42, 44], "001": [41, 73, 97, 99], "250": [41, 91, 92, 101, 105], "epoch_decay_start": 41, "schedul": 41, "beta": 41, "adam": 41, "adjust_learning_r": [41, 42], "alpha_plan": 41, "beta1_plan": 41, "forget_rate_schedul": [41, 42], "num_gradu": 41, "expon": 41, "tell": [41, 89, 93, 96, 101], "train_load": [41, 44], "model1": [41, 101], "optimizer1": 41, "model2": [41, 101], "optimizer2": 41, "dataload": [41, 93, 106], "parser": 41, "parse_arg": 41, "num_iter_per_epoch": 41, "print_freq": 41, "topk": 41, "top1": 41, "top5": 41, "test_load": 41, "offici": [42, 61, 97, 110], "wish": [42, 61, 100, 106, 109, 110], "adj_confident_thresholds_shar": [42, 43], "labels_shar": [42, 43], "pred_probs_shar": [42, 43], "labelinspector": [42, 43, 99], "get_num_issu": [42, 43], "get_quality_scor": [42, 43], "update_confident_threshold": [42, 43], "score_label_qu": [42, 43], "split_arr": [42, 43], "span_classif": 42, "display_issu": [42, 45, 78, 79, 80, 81, 82, 83, 84, 109, 110], "mnist_pytorch": 42, "get_mnist_dataset": [42, 44], "get_sklearn_digits_dataset": [42, 44], "simplenet": [42, 44], "batch_siz": [42, 43, 44, 77, 79, 93, 99, 106, 109], "log_interv": [42, 44], "momentum": [42, 44], "no_cuda": [42, 44], "test_batch_s": [42, 44, 93], "loader": [42, 44, 93], "set_predict_proba_request": [42, 44], "set_predict_request": [42, 44], "coteach": [42, 86], "mini": [43, 77, 79, 99], "low_self_confid": [43, 46, 65], "self_confid": [43, 46, 47, 51, 65, 67, 73, 81, 83, 88, 89, 99, 101], "conveni": [43, 56, 88, 89, 90, 96, 100], "script": 43, "labels_fil": [43, 99], "pred_probs_fil": [43, 99], "quality_score_kwarg": 43, "num_issue_kwarg": 43, "return_mask": 43, "variant": [43, 63, 109], "read": [43, 48, 92, 99, 101, 106, 110], "zarr": [43, 99], "memmap": [43, 109], "pythonspe": 43, "mmap": [43, 99], "hdf5": 43, "further": [43, 45, 64, 65, 67, 70, 71, 79, 80, 90, 97, 99, 100], "yourfil": 43, "npy": [43, 98, 99, 109], "mmap_mod": [43, 109], "tip": [43, 46, 62, 99], "save_arrai": 43, "your_arrai": 43, "disk": [43, 98, 99], "npz": [43, 110], "maxim": [43, 63, 77, 79, 100, 109], "multiprocess": [43, 46, 65, 77, 79, 93, 99], "linux": [43, 77, 79], "physic": [43, 46, 77, 79, 105], "psutil": [43, 46, 77, 79], "labels_arrai": [43, 60], "predprob": 43, "pred_probs_arrai": 43, "back": [43, 54, 71, 91, 99, 100, 105, 106], "store_result": 43, "becom": [43, 97, 106], "verifi": [43, 56, 99, 100, 103, 106], "long": [43, 63, 72, 100, 103], "chunk": [43, 107], "ram": [43, 98], "end_index": 43, "labels_batch": 43, "pred_probs_batch": 43, "batch_result": 43, "indices_of_examples_with_issu": [43, 99], "shortcut": 43, "encount": [43, 46, 77], "1000": [43, 90, 96, 99, 106], "aggreg": [43, 47, 51, 63, 67, 70, 73, 83, 99, 101, 103], "seen": [43, 99, 100, 106, 110], "far": [43, 63, 100], "label_quality_scor": [43, 67, 70, 73, 76, 101, 105], "method1": 43, "method2": 43, "normalized_margin": [43, 46, 47, 51, 65, 67, 73, 81, 83], "low_normalized_margin": [43, 46, 65], "issue_indic": [43, 70, 93], "update_num_issu": 43, "arr": [43, 99], "chunksiz": 43, "convnet": 44, "bespok": [44, 62], "download": [44, 90, 97, 99, 106], "mnist": [44, 85, 90, 98], "handwritten": 44, "digit": [44, 90, 98], "last": [44, 51, 68, 71, 91, 92, 99, 100, 103, 105, 110], "sklearn_digits_test_s": 44, "01": [44, 73, 75, 90, 97, 101, 104, 105, 106], "templat": 44, "flexibli": 44, "among": [44, 63, 101], "test_set": 44, "overrid": 44, "train_idx": [44, 59, 106], "train_label": [44, 89, 100, 106], "span": [45, 100], "sentenc": [45, 58, 81, 83, 84, 89, 96], "token_classif": [45, 58, 81, 83, 84, 99], "encourag": [46, 65, 73, 76], "multilabel_classif": [46, 64, 65, 67, 73, 99, 104], "pred_probs_by_class": 46, "prune_count_matrix_col": 46, "rank_by_kwarg": [46, 65, 73, 101], "num_to_remove_per_class": [46, 65], "bad": [46, 54, 65, 70, 73, 96, 99], "seem": [46, 101, 104], "aren": 46, "confidence_weighted_entropi": [46, 47, 51, 65, 67, 73, 81, 83], "label_issues_idx": [46, 73, 100], "entropi": [46, 48, 50, 51, 72, 73], "prune_by_class": [46, 65, 101], "predicted_neq_given": [46, 65, 101], "prune_counts_matrix": 46, "smallest": [46, 73], "unus": 46, "number_of_mislabeled_examples_in_class_k": 46, "delet": [46, 85, 89, 99], "too": [46, 51, 54, 72, 93, 99, 100, 105], "thread": [46, 65], "window": [46, 98], "shorter": [46, 68], "find_predicted_neq_given": 46, "find_label_issues_using_argmax_confusion_matrix": 46, "remove_noise_from_class": [47, 59], "clip_noise_r": [47, 59], "clip_valu": [47, 59], "value_count": [47, 59, 99], "value_counts_fill_missing_class": [47, 59], "get_missing_class": [47, 59], "round_preserving_sum": [47, 59], "round_preserving_row_tot": [47, 59], "estimate_pu_f1": [47, 59], "confusion_matrix": [47, 59], "print_square_matrix": [47, 59], "print_noise_matrix": [47, 59, 101], "print_inverse_noise_matrix": [47, 59], "print_joint_matrix": [47, 59, 101], "compress_int_arrai": [47, 59], "train_val_split": [47, 59], "subset_x_i": [47, 59], "subset_label": [47, 59], "subset_data": [47, 59], "extract_indices_tf": [47, 59], "unshuffle_tensorflow_dataset": [47, 59], "is_torch_dataset": [47, 59], "is_tensorflow_dataset": [47, 59], "csr_vstack": [47, 59], "append_extra_datapoint": [47, 59], "get_num_class": [47, 59], "num_unique_class": [47, 59], "get_unique_class": [47, 59], "format_label": [47, 59], "smart_display_datafram": [47, 59], "force_two_dimens": [47, 59], "latent_algebra": [47, 86], "compute_ps_py_inv_noise_matrix": [47, 49], "compute_py_inv_noise_matrix": [47, 49], "compute_inv_noise_matrix": [47, 49], "compute_noise_matrix_from_invers": [47, 49], "compute_pi": [47, 49], "compute_pyx": [47, 49], "label_quality_util": 47, "get_normalized_entropi": [47, 48], "multilabel_util": [47, 104], "stack_compl": [47, 52], "get_onehot_num_class": [47, 52], "int2onehot": [47, 52, 104], "onehot2int": [47, 52, 104], "multilabel_scor": [47, 67], "classlabelscor": [47, 51], "exponential_moving_averag": [47, 51, 67], "softmin": [47, 51, 67, 70, 79, 83], "possible_method": [47, 51], "multilabelscor": [47, 51], "get_class_label_quality_scor": [47, 51], "multilabel_pi": [47, 51], "get_cross_validated_multilabel_pred_prob": [47, 51], "default_k": [47, 53, 54], "features_to_knn": [47, 53, 54], "construct_knn_graph_from_index": [47, 53, 54, 56], "create_knn_graph_and_index": [47, 53, 54], "correct_knn_graph": [47, 53, 54, 97], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [47, 53, 54], "correct_knn_distances_and_indic": [47, 53, 54], "high_dimension_cutoff": [47, 53, 55], "row_count_cutoff": [47, 53, 55], "decide_euclidean_metr": [47, 53, 55], "decide_default_metr": [47, 53, 55], "construct_knn": [47, 53, 56], "transform_distances_to_scor": [47, 57], "correct_precision_error": [47, 57], "token_classification_util": [47, 110], "get_sent": [47, 58, 110], "filter_sent": [47, 58, 110], "process_token": [47, 58], "merge_prob": [47, 58], "color_sent": [47, 58], "assert_valid_input": [47, 60], "assert_valid_class_label": [47, 60], "assert_nonempty_input": [47, 60], "assert_indexing_work": [47, 60], "labels_to_arrai": [47, 60], "labels_to_list_multilabel": [47, 60], "min_allowed_prob": 48, "wikipedia": 48, "activ": [48, 50, 62, 63, 85, 103], "towardsdatasci": 48, "cheatsheet": 48, "ec57bc067c0b": 48, "clip": [48, 59, 90, 97], "behav": 48, "unnecessari": [48, 99], "slightli": [48, 88, 89], "interv": [48, 51, 106], "herein": 49, "inexact": 49, "cours": [49, 100], "propag": 49, "throughout": [49, 59, 75, 84, 90, 103, 109, 110], "increas": [49, 57, 70, 72, 73, 90, 91, 97, 99, 103, 104, 110], "dot": [49, 83, 99], "true_labels_class_count": 49, "pyx": 49, "multiannot": 50, "assert_valid_inputs_multiannot": 50, "labels_multiannot": [50, 63], "ensembl": [50, 51, 63, 73, 88, 95, 99, 104, 106, 108], "allow_single_label": 50, "annotator_id": 50, "assert_valid_pred_prob": 50, "pred_probs_unlabel": [50, 63], "format_multiannotator_label": [50, 63, 103], "formatted_label": [50, 59], "old": [50, 59, 86, 98], "check_consensus_label_class": 50, "consensus_label": [50, 63, 103], "consensus_method": [50, 63], "consensu": [50, 63, 85, 102, 110], "establish": [50, 62, 89, 108], "compute_soft_cross_entropi": 50, "soft": [50, 98], "find_best_temp_scal": 50, "coarse_search_rang": [50, 75, 99], "fine_search_s": [50, 75, 99], "temperatur": [50, 51, 70, 79, 83], "scale": [50, 57, 88, 97, 98, 99, 106, 109], "factor": [50, 51, 57, 77, 79], "minim": [50, 70, 106], "temp_scale_pred_prob": 50, "temp": 50, "sharpen": [50, 98], "smoothen": 50, "get_normalized_margin_for_each_label": [51, 73], "get_confidence_weighted_entropy_for_each_label": [51, 73], "scorer": 51, "alpha": [51, 67, 70, 91, 92, 97, 101, 104, 108], "exponenti": 51, "ema": 51, "s_1": 51, "s_k": 51, "ema_k": 51, "accord": [51, 65, 95, 96, 101, 110], "formula": [51, 57], "_t": 51, "cdot": 51, "s_t": 51, "qquad": 51, "leq": 51, "_1": 51, "recent": [51, 110], "success": 51, "previou": [51, 54, 93, 95, 99, 105], "discount": 51, "s_ema": 51, "175": [51, 93, 100, 101, 105], "underflow": 51, "nan": [51, 63, 88, 95, 97, 100, 103, 108], "aggregated_scor": 51, "base_scor": [51, 100], "base_scorer_kwarg": 51, "aggregator_kwarg": [51, 67], "n_sampl": [51, 97], "n_label": 51, "class_label_quality_scor": 51, "452": 51, "new_scor": 51, "575": [51, 100], "get_label_quality_scores_per_class": [51, 66, 67], "ml_scorer": 51, "binar": [51, 52], "reformat": [51, 90], "wider": 51, "splitter": 51, "kfold": [51, 93], "onevsrestclassifi": [51, 104], "randomforestclassifi": [51, 101, 104], "n_split": [51, 93, 104], "pred_prob_slic": 52, "onehot": 52, "hot": [52, 65, 71, 77, 80, 88, 95, 98, 99, 108, 109], "onehot_matrix": 52, "pairwis": [53, 55, 72], "reli": [54, 72, 89, 90, 91, 92, 96, 105, 106, 108], "sklearn_knn_kwarg": 54, "correction_featur": 54, "discourag": 54, "flexibl": [54, 99], "manner": [54, 67, 88, 89, 97, 103, 108], "701": 54, "900": [54, 88, 93, 95, 108], "436": [54, 100], "000": [54, 89, 93, 96, 97, 98, 110], "idea": [54, 73, 100, 105], "dens": [54, 62, 97], "33140006": 54, "76210367": 54, "correct_exact_dupl": 54, "mutual": [54, 64, 104], "vari": [54, 70, 92], "exact_duplicate_set": 54, "main": [54, 63], "front": [54, 98], "consider": 54, "capabl": [54, 85, 100], "come": [54, 59, 91, 92, 99, 109], "misidentif": 54, "corrected_dist": 54, "corrected_indic": 54, "sqrt": 54, "distant": 54, "suitabl": [55, 63, 88, 95, 97, 100], "slower": 55, "decid": [55, 63, 89, 96, 98, 103, 108, 110], "predefin": 55, "met": [55, 110], "euclidean_dist": [55, 72], "spatial": [55, 72], "decis": [55, 88, 91, 92, 100], "That": [55, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "cosine_dist": 55, "knn_kwarg": 56, "html": [56, 59, 68, 71, 72, 90, 91, 92, 93, 95, 96, 99, 100, 101], "kneighbor": 56, "metric_param": 56, "n_features_in_": 56, "effective_metric_params_": 56, "effective_metric_": 56, "n_samples_fit_": 56, "__sklearn_is_fitted__": 56, "conduct": 56, "is_fit": 56, "trail": 56, "underscor": 56, "avg_dist": 57, "exp": [57, 72, 73, 91], "dt": 57, "right": [57, 68, 71, 89, 96, 104, 105, 106], "pronounc": 57, "differenti": 57, "ly": 57, "rule": [57, 58, 85, 98], "thumb": 57, "ood_features_scor": [57, 72, 106], "88988177": 57, "80519832": 57, "toler": 57, "minkowski": 57, "noth": 57, "epsilon": 57, "sensibl": 57, "fixed_scor": 57, "readabl": 58, "lambda": [58, 90, 91, 99, 100, 103], "long_sent": 58, "headlin": 58, "charact": [58, 59], "s1": 58, "s2": 58, "processed_token": 58, "alecnlcb": 58, "entiti": [58, 85, 99, 110], "mapped_ent": 58, "unique_ident": 58, "loc": [58, 91, 92, 93, 95, 97, 110], "nbitbas": [58, 67], "probs_merg": 58, "0125": [58, 83], "0375": 58, "075": 58, "025": 58, "color": [58, 80, 91, 92, 95, 97, 101, 104, 106, 108, 109], "red": [58, 71, 91, 92, 97, 98, 101, 104, 105, 106, 109], "colored_sent": 58, "termcolor": 58, "31msentenc": 58, "0m": 58, "ancillari": 59, "class_without_nois": 59, "any_other_class": 59, "choos": [59, 73, 88, 95, 99, 101, 108], "tradition": 59, "new_sum": 59, "fill": 59, "major": [59, 63, 86, 93, 106], "versu": [59, 101], "obviou": 59, "cgdeboer": 59, "iteround": 59, "reach": 59, "prob_s_eq_1": 59, "claesen": 59, "f1": [59, 71, 96, 101], "BE": 59, "left_nam": 59, "top_nam": 59, "titl": [59, 91, 92, 97, 101, 104, 106], "short_titl": 59, "round_plac": 59, "pretti": [59, 101], "joint_matrix": 59, "num_possible_valu": 59, "holdout_idx": 59, "extract": [59, 72, 89, 90, 95, 96, 100, 103, 106, 109], "allow_shuffl": 59, "turn": [59, 85, 105], "shuffledataset": 59, "histori": 59, "pre_x": 59, "buffer_s": 59, "csr_matric": 59, "append": [59, 90, 93, 98, 99, 100, 101, 103, 104, 105, 106, 110], "bottom": [59, 68, 71, 97, 105], "to_data": 59, "from_data": 59, "taken": 59, "label_matrix": 59, "canon": 59, "displai": [59, 71, 80, 84, 89, 90, 95, 96, 97, 101, 110], "jupyt": [59, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "notebook": [59, 63, 90, 92, 98, 99, 100, 101, 103, 104, 105, 107, 109, 110], "consol": 59, "allow_missing_class": 60, "allow_one_class": 60, "length_x": 60, "labellik": 60, "labels_list": [60, 65], "keraswrappermodel": [61, 62, 85], "keraswrappersequenti": [61, 62], "tf": [62, 90], "legaci": 62, "newer": 62, "interim": 62, "advis": [62, 104], "stabil": [62, 72], "until": 62, "accommod": 62, "keraswrapp": 62, "huggingface_keras_imdb": 62, "unit": [62, 110], "model_kwarg": [62, 75], "compile_kwarg": 62, "sparsecategoricalcrossentropi": 62, "layer": [62, 89, 90, 96, 106], "my_keras_model": 62, "from_logit": 62, "declar": 62, "apply_softmax": 62, "analysi": 63, "analyz": [63, 85, 97, 101, 103, 104], "get_label_quality_multiannot": [63, 103], "vote": 63, "crowdsourc": [63, 85, 103], "dawid": [63, 103], "skene": [63, 103], "analog": [63, 98, 103], "chosen": [63, 73, 99, 103], "crowdlab": [63, 103], "unlabel": [63, 93, 103, 106, 109], "get_active_learning_scor": [63, 103], "activelab": [63, 103], "priorit": [63, 70, 105, 109, 110], "showcas": 63, "best_qual": 63, "quality_method": 63, "calibrate_prob": 63, "return_detailed_qu": 63, "return_annotator_stat": 63, "return_weight": 63, "label_quality_score_kwarg": 63, "did": [63, 64, 88, 89, 90, 95, 101, 103, 108], "majority_vot": 63, "broken": [63, 71, 98, 108], "highest": [63, 71, 91, 93, 100, 107], "0th": 63, "consensus_quality_scor": [63, 103], "annotator_agr": [63, 103], "reman": 63, "1st": 63, "2nd": [63, 77], "3rd": 63, "consensus_label_suffix": 63, "consensus_quality_score_suffix": 63, "suffix": 63, "emsembl": 63, "weigh": [63, 98], "agreement": [63, 103], "agre": 63, "prevent": [63, 99], "overconfid": [63, 107], "detailed_label_qu": [63, 103], "annotator_stat": [63, 103], "model_weight": 63, "annotator_weight": 63, "warn": 63, "labels_info": 63, "num_annot": [63, 103], "deriv": [63, 103], "quality_annotator_1": 63, "quality_annotator_2": 63, "quality_annotator_m": 63, "annotator_qu": [63, 103], "num_examples_label": [63, 103], "agreement_with_consensu": [63, 103], "worst_class": [63, 103], "trustworthi": [63, 103, 108], "get_label_quality_multiannotator_ensembl": 63, "weigtht": 63, "budget": 63, "retrain": [63, 89, 108], "active_learning_scor": 63, "active_learning_scores_unlabel": 63, "get_active_learning_scores_ensembl": 63, "henc": [63, 90, 91, 100, 103], "get_majority_vote_label": [63, 103], "event": 63, "lastli": [63, 95], "convert_long_to_wide_dataset": 63, "labels_multiannotator_long": 63, "wide": [63, 88, 89, 90], "labels_multiannotator_wid": 63, "common_multilabel_issu": [64, 66], "exclus": [64, 104], "rank_classes_by_multilabel_qu": [64, 66], "overall_multilabel_health_scor": [64, 66], "multilabel_health_summari": [64, 66], "classes_by_multilabel_qu": 64, "inner": [65, 79, 97], "find_multilabel_issues_per_class": [65, 66], "per_class_label_issu": 65, "label_issues_list": 65, "pred_probs_list": [65, 73, 93, 101], "anim": [66, 106], "rat": 66, "predat": 66, "pet": 66, "reptil": 66, "box": [68, 70, 71, 98, 105], "object_detect": [68, 70, 71, 105], "return_indices_ranked_by_scor": [68, 105], "overlapping_label_check": [68, 70], "suboptim": [68, 70], "locat": [68, 70, 97, 105, 109, 110], "bbox": [68, 71, 105], "image_nam": [68, 71], "y1": [68, 71, 105], "y2": [68, 71, 105], "later": [68, 71, 72, 89, 100, 110], "corner": [68, 71, 105], "xyxi": [68, 71, 105], "io": [68, 71, 90, 97, 98], "keras_cv": [68, 71], "bounding_box": [68, 71, 105], "detectron": [68, 71, 105], "detectron2": [68, 71, 105], "readthedoc": [68, 71], "en": [68, 71], "latest": [68, 71], "draw_box": [68, 71], "mmdetect": [68, 71, 105], "swap": [68, 70, 80, 84], "penal": [68, 70], "concern": [68, 70, 85, 92], "issues_from_scor": [69, 70, 78, 79, 80, 82, 83, 84, 105, 109, 110], "compute_overlooked_box_scor": [69, 70], "compute_badloc_box_scor": [69, 70], "compute_swap_box_scor": [69, 70], "pool_box_scores_per_imag": [69, 70], "object_counts_per_imag": [69, 71, 105], "bounding_box_size_distribut": [69, 71, 105], "class_label_distribut": [69, 71, 105], "get_sorted_bbox_count_idx": [69, 71], "plot_class_size_distribut": [69, 71], "plot_class_distribut": [69, 71], "get_average_per_class_confusion_matrix": [69, 71], "calculate_per_class_metr": [69, 71], "aggregation_weight": 70, "imperfect": [70, 99, 100], "chose": [70, 103, 105], "imperfectli": [70, 105], "dirti": [70, 73, 76, 108], "subtyp": 70, "badloc": 70, "nonneg": 70, "high_probability_threshold": 70, "auxiliary_input": [70, 71], "iou": [70, 71], "heavili": 70, "auxiliarytypesdict": 70, "pred_label": [70, 89], "pred_label_prob": 70, "pred_bbox": 70, "lab_label": 70, "lab_bbox": 70, "similarity_matrix": 70, "min_possible_similar": 70, "scores_overlook": 70, "low_probability_threshold": 70, "scores_badloc": 70, "accident": [70, 89, 95, 96, 99], "scores_swap": 70, "box_scor": 70, "image_scor": [70, 79, 109], "discov": [71, 92, 97, 110], "abnorm": [71, 93, 105], "auxiliari": [71, 106, 109], "_get_valid_inputs_for_compute_scor": 71, "object_count": 71, "down": 71, "bbox_siz": 71, "class_distribut": 71, "plot": [71, 91, 92, 97, 101, 104, 106, 108, 109], "sorted_idx": [71, 106], "class_to_show": 71, "hidden": [71, 106], "max_class_to_show": 71, "plt": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "matplotlib": [71, 80, 91, 92, 93, 97, 101, 104, 105, 106, 108], "pyplot": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "prediction_threshold": 71, "overlai": [71, 105], "figsiz": [71, 91, 92, 93, 97, 101, 104, 106], "save_path": [71, 105], "blue": [71, 98, 101, 105], "overlaid": 71, "side": [71, 98, 105], "figur": [71, 97, 101, 104, 106, 108], "extens": [71, 101, 103], "png": [71, 105], "pdf": [71, 72], "svg": 71, "num_proc": [71, 93], "intersect": [71, 99], "tp": 71, "fp": 71, "ground": [71, 98, 101, 103, 108], "truth": [71, 101, 103, 108], "bias": [71, 97], "avg_metr": 71, "distionari": 71, "95": [71, 81, 83, 95, 98, 100, 101, 108], "per_class_metr": 71, "Of": 72, "find_top_issu": [72, 73, 106], "behind": [72, 101], "dist_metr": 72, "subtract": [72, 73], "renorm": [72, 73, 99], "least_confid": 72, "sum_": 72, "log": [72, 73, 86], "softmax": [72, 79, 83, 93], "literatur": 72, "gen": 72, "liu": 72, "lochman": 72, "zach": 72, "openaccess": 72, "thecvf": 72, "cvpr2023": 72, "liu_gen_pushing_the_limits_of_softmax": 72, "based_out": 72, "distribution_detection_cvpr_2023_pap": 72, "fit_scor": [72, 106], "ood_predictions_scor": 72, "pretrain": [72, 89, 90, 96, 100, 106], "adjust_confident_threshold": 72, "probabilist": [72, 88, 90, 91, 92, 95, 96, 106, 107], "order_label_issu": [73, 86], "whichev": [73, 107], "argsort": [73, 89, 93, 96, 101, 105, 106, 108], "max_": 73, "get_label_quality_ensemble_scor": [73, 99, 101], "weight_ensemble_members_bi": 73, "custom_weight": 73, "log_loss_search_t_valu": 73, "0001": [73, 98], "scheme": 73, "log_loss_search": 73, "log_loss": [73, 96], "1e0": 73, "1e1": 73, "1e2": 73, "2e2": 73, "quality_scor": [73, 106], "forth": 73, "top_issue_indic": 73, "rank_bi": [73, 86], "weird": [73, 84], "prob_label": 73, "max_prob_not_label": 73, "AND": [73, 96], "get_epistemic_uncertainti": [74, 75], "get_aleatoric_uncertainti": [74, 75], "corrupt": [75, 108], "linearregress": [75, 99, 108], "y_with_nois": 75, "n_boot": [75, 99], "include_aleatoric_uncertainti": [75, 99], "bootstrap": [75, 99, 108], "resampl": [75, 90, 99], "epistem": [75, 99, 106, 108], "aleator": [75, 99, 108], "model_final_kwarg": 75, "coars": 75, "thorough": [75, 99], "fine": [75, 89, 90, 96, 106], "grain": 75, "grid": [75, 100], "varianc": [75, 101], "epistemic_uncertainti": 75, "residu": [75, 76, 99], "deviat": [75, 105, 108], "aleatoric_uncertainti": 75, "outr": 76, "contin": 76, "raw": [76, 85, 86, 92, 93, 98, 99, 100, 103, 105, 106, 108], "aka": [76, 90, 101, 105, 108, 110], "00323821": 76, "33692597": 76, "00191686": 76, "semant": [77, 79, 80, 102], "pixel": [77, 79, 80, 93, 106, 109], "h": [77, 79, 80, 109], "height": [77, 79, 80, 109], "w": [77, 79, 80, 109], "width": [77, 79, 80, 109], "labels_one_hot": [77, 80, 109], "stream": [77, 106, 110], "downsampl": [77, 79, 109], "shrink": [77, 79], "divis": [77, 79, 91], "common_label_issu": [78, 80, 82, 84, 109, 110], "filter_by_class": [78, 80, 109], "segmant": [79, 80], "num_pixel_issu": [79, 109], "product": [79, 93, 97, 99, 100], "pixel_scor": [79, 109], "enter": 80, "legend": [80, 91, 92, 97, 104, 105, 108, 109], "colormap": 80, "background": [80, 97], "person": [80, 99, 105, 109, 110], "ambigu": [80, 84, 89, 90, 96, 98, 101, 110], "misunderstood": [80, 84], "issues_df": [80, 93], "class_index": 80, "issues_subset": [80, 84], "filter_by_token": [82, 84, 110], "token_score_method": 83, "sentence_score_method": 83, "sentence_score_kwarg": 83, "compris": [83, 84], "token_scor": [83, 110], "converg": 83, "toward": [83, 97], "_softmin_sentence_scor": 83, "sentence_scor": [83, 110], "token_info": 83, "02": [83, 91, 92, 97, 101, 105], "03": [83, 95, 97, 98, 100, 101, 105, 110], "04": [83, 95, 97, 105], "08": [83, 97, 101, 105, 108, 110], "commonli": [84, 86, 91, 92, 104, 110], "But": [84, 96, 100, 101, 108, 110], "restrict": [84, 99], "reliabl": [85, 88, 90, 97, 99, 100, 103, 109], "thousand": 85, "imagenet": [85, 98], "popular": [85, 103, 105], "centric": [85, 93, 102], "minut": [85, 88, 89, 90, 95, 96, 98, 103, 104, 105, 108, 109, 110], "conda": 85, "feature_embed": [85, 106], "your_dataset": [85, 90, 91, 92, 93, 95, 96, 99], "column_name_of_label": [85, 90, 91, 92, 93, 95, 96], "tool": [85, 98, 101, 103], "catch": [85, 100], "dive": [85, 96, 97, 100], "plagu": [85, 92], "untrain": 85, "\u30c4": 85, "label_issues_info": [85, 92], "sklearn_compatible_model": 85, "framework": [85, 104, 105], "complianc": 85, "tag": [85, 104, 110], "sequenc": 85, "recognit": [85, 90, 99, 110], "train_data": [85, 88, 89, 106, 108], "gotten": 85, "test_data": [85, 88, 89, 101, 104, 106, 108], "deal": [85, 92, 97, 100], "feel": [85, 90, 92, 99], "ask": [85, 99], "slack": [85, 99], "project": [85, 100, 108], "welcom": 85, "commun": [85, 99], "guidelin": [85, 105], "piec": 85, "smart": [85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 108], "edit": [85, 99, 100], "unreli": [85, 88, 90, 95, 96, 97, 100], "link": [85, 90, 98, 105], "older": 86, "outlin": 86, "substitut": [86, 100], "v2": [86, 88, 95], "get_noise_indic": 86, "psx": 86, "sorted_index_method": 86, "order_label_error": 86, "label_errors_bool": 86, "latent_estim": 86, "num_label_error": 86, "learningwithnoisylabel": 86, "neatli": 86, "organ": [86, 88, 95, 97, 98, 110], "reorgan": 86, "baseline_method": 86, "research": [86, 101], "polyplex": 86, "terminologi": 86, "label_error": 86, "quickstart": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 103, 104, 105, 106, 108, 109, 110], "sql": [88, 95], "databas": [88, 95], "excel": [88, 95], "parquet": [88, 95], "student": [88, 95, 100, 108, 110], "grade": [88, 95, 100, 108], "exam": [88, 95, 100, 108], "letter": [88, 95, 110], "hundr": [88, 95], "mistak": [88, 89, 93, 95, 96, 100], "extratreesclassifi": 88, "extratre": 88, "Then": [88, 89, 93, 99], "ranked_label_issu": [88, 89], "branch": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "standardscal": [88, 95, 100, 106], "labelencod": [88, 89, 100], "train_test_split": [88, 89, 91, 92, 106], "accuracy_scor": [88, 89, 90, 96, 100, 101], "grades_data": [88, 95], "read_csv": [88, 89, 95, 96, 97, 100, 108], "demo": [88, 92, 95, 104], "stud_id": [88, 95, 100], "exam_1": [88, 95, 100, 108], "exam_2": [88, 95, 100, 108], "exam_3": [88, 95, 100, 108], "letter_grad": [88, 95], "f48f73": [88, 95], "53": [88, 91, 92, 95, 97, 98, 100, 104, 105], "00": [88, 91, 92, 95, 97, 98, 100, 106], "77": [88, 91, 92, 95, 100, 105], "0bd4e7": [88, 95], "81": [88, 95, 96, 100, 105, 108, 110], "great": [88, 95, 98, 100], "particip": [88, 95, 100], "cb9d7a": [88, 95], "61": [88, 95, 97, 101, 105, 108], "94": [88, 95, 98, 100, 101, 105, 108], "9acca4": [88, 95], "48": [88, 95, 97, 98, 101, 105], "x_raw": [88, 95], "labels_raw": 88, "interg": [88, 89], "categorical_featur": [88, 108], "x_encod": [88, 95], "get_dummi": [88, 95, 108], "drop_first": [88, 95], "numeric_featur": [88, 95], "scaler": [88, 95, 106], "x_process": [88, 95], "fit_transform": [88, 95, 97, 100], "bring": [88, 89, 93, 95, 96, 103, 108], "byod": [88, 89, 93, 95, 96, 103, 108], "tress": 88, "held": [88, 90, 95, 96, 98, 105, 106, 107], "straightforward": [88, 90, 95], "benefit": [88, 90, 107, 109], "num_crossval_fold": [88, 90, 95, 100, 103], "tabl": [88, 95, 98, 103], "212": [88, 100, 101], "iloc": [88, 89, 90, 95, 96, 100, 108], "92": [88, 91, 100, 101, 105], "93": [88, 98, 100, 105, 108, 110], "827": 88, "99": [88, 97, 98, 100, 101], "86": [88, 92, 93, 95, 100, 101, 105, 108], "74": [88, 97, 100, 105, 108], "637": [88, 95], "79": [88, 98, 100, 105], "65": [88, 91, 97, 100, 105], "cheat": [88, 100], "0pt": [88, 100], "120": [88, 91, 92, 100], "233": 88, "83": [88, 100, 101, 105, 108, 110], "76": [88, 100, 101, 104, 105, 108], "suspici": [88, 95], "carefulli": [88, 93, 95, 96, 100], "examin": [88, 91, 92, 95, 97, 100, 105], "labels_train": 88, "labels_test": 88, "test_siz": [88, 89, 91, 92], "acc_og": [88, 89], "783068783068783": 88, "robustli": [88, 89, 108], "14": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "acc_cl": [88, 89], "8095238095238095": 88, "blindli": [88, 89, 90, 99, 100, 108], "trust": [88, 89, 90, 99, 100, 101, 103, 107, 108], "effort": [88, 89, 100, 108], "cumbersom": [88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "intent": [89, 96], "servic": [89, 96, 99], "onlin": [89, 96], "bank": [89, 96, 98], "banking77": [89, 96], "oo": [89, 96], "categori": [89, 93, 96, 97, 100], "shortlist": [89, 96, 108], "scope": [89, 96], "logist": [89, 91, 92, 96, 103, 106], "probabilit": [89, 90], "drop": [89, 95, 97, 99, 100, 103, 108], "sentence_transform": [89, 96], "sentencetransform": [89, 96], "payment": [89, 96], "cancel_transf": [89, 96], "transfer": [89, 96], "fund": [89, 96], "cancel": [89, 96], "transact": [89, 96], "my": [89, 96], "revert": [89, 96], "morn": [89, 96], "realis": [89, 96], "yesterdai": [89, 96], "rent": [89, 96], "tomorrow": [89, 96], "raw_text": [89, 96], "raw_label": 89, "raw_train_text": 89, "raw_test_text": 89, "raw_train_label": 89, "raw_test_label": 89, "change_pin": [89, 96], "lost_or_stolen_phon": [89, 96], "card_payment_fee_charg": [89, 96], "supported_cards_and_curr": [89, 96], "visa_or_mastercard": [89, 96], "getting_spare_card": [89, 96], "beneficiary_not_allow": [89, 96], "card_about_to_expir": [89, 96], "apple_pay_or_google_pai": [89, 96], "card": [89, 96, 98], "utter": [89, 96], "encond": 89, "test_label": [89, 100, 101, 104, 106], "suit": [89, 96, 97, 98, 99], "electra": [89, 96], "discrimin": [89, 96], "googl": [89, 96], "train_text": 89, "test_text": 89, "home": [89, 96, 98], "runner": [89, 96], "google_electra": [89, 96], "pool": [89, 96, 99, 106], "leverag": [89, 90, 96, 99, 101, 103], "computation": [89, 90, 96], "intens": [89, 90, 96], "400": [89, 96, 100], "858371": 89, "547274": 89, "826228": 89, "966008": 89, "792449": 89, "identified_issu": [89, 108], "lowest_quality_label": [89, 90, 96, 101, 108], "to_numpi": [89, 96, 97, 100, 108], "44": [89, 97, 98, 104, 105, 110], "646": 89, "390": 89, "628": 89, "121": [89, 101], "702": 89, "863": 89, "135": 89, "337": [89, 100, 105], "735": 89, "print_as_df": 89, "inverse_transform": 89, "charg": [89, 96], "cash": [89, 96], "holidai": [89, 96], "sent": [89, 96, 97, 110], "mine": [89, 96], "expir": [89, 96], "fight": 89, "hors": [89, 98, 106], "duck": [89, 98], "me": [89, 96, 97], "whoever": [89, 96], "consum": [89, 108], "18": [89, 90, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109], "baseline_model": [89, 108], "87": [89, 92, 93, 100, 105, 108], "acceler": [89, 108], "19": [89, 90, 93, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109], "89": [89, 91, 95, 100, 105, 108], "spoken": 90, "500": [90, 97, 100, 106, 110], "english": [90, 98], "pronunci": 90, "wav": 90, "voxceleb": 90, "speech": [90, 110], "your_pred_prob": [90, 91, 92, 95, 96], "tensorflow_io": 90, "huggingface_hub": 90, "reproduc": [90, 95, 97, 100, 101, 103], "command": 90, "wget": [90, 97, 105, 109, 110], "navig": 90, "browser": 90, "jakobovski": 90, "archiv": [90, 110], "v1": 90, "tar": [90, 106], "gz": [90, 106], "mkdir": [90, 110], "spoken_digit": 90, "xf": 90, "6_nicolas_32": 90, "data_path": 90, "listdir": 90, "nondeterminist": 90, "file_nam": 90, "endswith": 90, "file_path": 90, "join": [90, 93, 97, 99, 100], "7_george_26": 90, "0_nicolas_24": 90, "0_nicolas_6": 90, "listen": 90, "display_exampl": 90, "expand": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "pulldown": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "colab": [90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "tfio": 90, "pathlib": 90, "ipython": [90, 97], "load_wav_16k_mono": 90, "filenam": 90, "khz": 90, "file_cont": 90, "read_fil": 90, "sample_r": 90, "decode_wav": 90, "desired_channel": 90, "squeez": 90, "rate_in": 90, "rate_out": 90, "16000": 90, "wav_file_nam": 90, "audio_r": 90, "wav_file_exampl": 90, "plai": [90, 98, 99], "button": 90, "wav_file_name_exampl": 90, "7_jackson_43": 90, "hear": 90, "extractor": 90, "encoderclassifi": 90, "spkrec": 90, "xvect": 90, "feature_extractor": 90, "from_hparam": 90, "run_opt": 90, "uncom": [90, 97], "ffmpeg": 90, "backend": 90, "wav_audio_file_path": 90, "torchaudio": 90, "extract_audio_embed": 90, "emb": [90, 93], "signal": 90, "encode_batch": 90, "embeddings_list": [90, 93], "embeddings_arrai": 90, "512": [90, 93], "196311": 90, "319459": 90, "478975": 90, "2890875": 90, "8170238": 90, "89265": 90, "898056": 90, "256195": 90, "559641": 90, "559721": 90, "62067": 90, "285245": 90, "21": [90, 91, 97, 98, 100, 101, 105, 108, 110], "709627": 90, "5033693": 90, "913803": 90, "819831": 90, "1831515": 90, "208763": 90, "084257": 90, "3210397": 90, "005453": 90, "216152": 90, "478235": 90, "6821785": 90, "053807": 90, "242471": 90, "091424": 90, "78334856": 90, "03954": 90, "23": [90, 93, 97, 98, 100, 101, 105, 108], "569176": 90, "761097": 90, "1258295": 90, "753237": 90, "3508866": 90, "598274": 90, "23712": 90, "2500": 90, "tol": 90, "decreas": [90, 99], "cv_accuraci": 90, "9708": 90, "issue_type_descript": [90, 91, 92, 93, 95, 96, 100, 101], "lt": [90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 106], "gt": [90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 110], "9976": 90, "986": 90, "002161": 90, "176": [90, 98, 101, 104], "002483": 90, "2318": 90, "004411": 90, "1005": 90, "004857": 90, "1871": 90, "007494": 90, "040587": 90, "999207": 90, "999377": 90, "975220": 90, "999367": 90, "identified_label_issu": [90, 96], "516": [90, 100], "1946": 90, "469": 90, "2132": 90, "worth": [90, 101], "6_yweweler_25": 90, "7_nicolas_43": 90, "6_theo_27": 90, "6_yweweler_36": 90, "6_yweweler_14": 90, "6_yweweler_35": 90, "6_nicolas_8": 90, "sound": 90, "quit": [90, 106], "underneath": 91, "hood": [91, 97, 99], "alert": 91, "introduct": 91, "mayb": [91, 92, 96], "your_feature_matrix": [91, 92], "toi": [91, 92, 93, 97, 98, 101, 103, 107], "inf": [91, 92], "mid": [91, 92], "bins_map": [91, 92], "create_data": [91, 92], "y_bin": [91, 92], "y_i": [91, 92], "y_bin_idx": [91, 92], "y_train": [91, 92, 101, 108], "y_test": [91, 92, 101, 108], "y_train_idx": [91, 92], "y_test_idx": [91, 92], "slide": [91, 92, 98], "frame": [91, 92], "x_out": [91, 92], "tini": [91, 92], "concaten": [91, 92, 107], "y_out": [91, 92], "y_out_bin": [91, 92], "y_out_bin_idx": [91, 92], "exact_duplicate_idx": [91, 92], "x_duplic": [91, 92], "y_duplic": [91, 92], "y_duplicate_idx": [91, 92], "noisy_labels_idx": [91, 92, 104], "scatter": [91, 92, 97, 101, 104, 108], "black": [91, 92, 98, 108], "cyan": [91, 92], "plot_data": [91, 92, 97, 101, 104, 108], "fig": [91, 92, 93, 98, 106, 108], "ax": [91, 92, 93, 97, 106, 108], "subplot": [91, 92, 93, 106], "set_titl": [91, 92, 93, 106], "set_xlabel": [91, 92], "x_1": [91, 92], "fontsiz": [91, 92, 93, 97, 101, 104], "set_ylabel": [91, 92], "x_2": [91, 92], "set_xlim": [91, 92], "set_ylim": [91, 92], "linestyl": [91, 92, 97], "circl": [91, 92, 101, 104], "misclassifi": [91, 92], "zip": [91, 92, 93, 97, 105, 110], "label_err": [91, 92], "180": [91, 92, 97, 105], "marker": [91, 92], "facecolor": [91, 92, 97], "edgecolor": [91, 92, 97], "linewidth": [91, 92, 97, 106], "dup": [91, 92], "first_legend": [91, 92], "align": [91, 92], "title_fontproperti": [91, 92], "semibold": [91, 92], "second_legend": [91, 92], "45": [91, 92, 97, 98, 100, 101, 105], "gca": [91, 92], "add_artist": [91, 92], "tight_layout": [91, 92, 97], "ideal": [91, 92], "remaind": 91, "modal": [91, 92, 99, 100, 103], "132": [91, 92, 100, 101, 105], "9318": 91, "006940": 91, "007830": 91, "40": [91, 92, 96, 97, 98, 100], "014828": 91, "107": [91, 92, 101, 104], "021241": 91, "026407": 91, "notic": [91, 101, 103, 105], "3558": [91, 92], "126": [91, 92, 101, 105], "006636": [91, 92], "130": [91, 92], "012571": [91, 92], "129": [91, 92], "127": [91, 92, 100], "014909": [91, 92], "128": [91, 92, 93], "017443": [91, 92], "6160": [91, 92], "131": [91, 92, 100, 109], "000000e": [91, 92, 100], "000002": [91, 92], "463180e": [91, 92], "07": [91, 92, 93, 95, 97, 101, 105, 108], "51": [91, 92, 95, 97, 98, 101, 105], "161148": [91, 92], "859087e": [91, 92], "30": [91, 92, 93, 97, 98, 99, 100, 104, 109, 110], "3453": 91, "029542": 91, "031182": 91, "057961": 91, "058244": 91, "54": [91, 97, 98, 101, 105], "039122": 91, "044598": 91, "105": [91, 105, 110], "105196": 91, "133654": 91, "43": [91, 97, 98, 100, 101, 105, 110], "168033": 91, "125": 91, "101107": 91, "183382": 91, "109": [91, 97, 98, 100, 105], "209259": 91, "211042": 91, "221316": 91, "average_ood_scor": 91, "34530442089193386": 91, "52": [91, 97, 98, 100, 105, 110], "169820": 91, "087324e": 91, "259024": 91, "583757e": 91, "91": [91, 100, 105], "346458": 91, "341292e": 91, "specfi": 91, "new_lab": 91, "scoring_funct": 91, "div": 91, "rem": 91, "inv_scal": 91, "49": [91, 97, 98, 101, 105], "superstitionissuemanag": 91, "unlucki": 91, "superstit": 91, "to_seri": 91, "issues_mask": 91, "summary_scor": 91, "9242": 91, "is_superstition_issu": 91, "superstition_scor": 91, "26": [91, 93, 97, 98, 100, 101, 103, 105, 110], "047581": 91, "090635": 91, "129591": 91, "164840": 91, "lurk": [92, 93, 100, 101], "thoroughli": 92, "8561": 92, "001908": 92, "003564": 92, "007331": 92, "008963": 92, "009664": 92, "0227": 92, "022727": 92, "conceptu": 92, "856061": 92, "355772": 92, "616034": 92, "821750": 92, "926818": 92, "betweeen": 92, "859131": 92, "417707": 92, "664083": 92, "970324": 92, "816953": 92, "375317": 92, "641516": 92, "890575": 92, "910232": 92, "531021": 92, "460593": 92, "601188": 92, "826147": 92, "752808": 92, "321635": 92, "562539": 92, "948362": 92, "890169": 92, "090243": 92, "472909": 92, "746763": 92, "878267": 92, "examples_w_issu": [92, 99], "013445": 92, "025184": 92, "026376": 92, "inde": [92, 96], "miscellan": [92, 94, 110], "428571": 92, "111111": 92, "571429": 92, "407407": 92, "592593": 92, "337838": 92, "092593": 92, "662162": 92, "333333": [92, 98], "952381": 92, "666667": [92, 97], "portion": 92, "huge": [92, 101], "worri": [92, 96, 100], "critic": [92, 107], "60": [93, 97, 101, 108, 110], "torchvis": [93, 97, 106], "tensordataset": 93, "stratifiedkfold": [93, 104], "tqdm": 93, "autonotebook": 93, "math": [93, 100], "fashion_mnist": 93, "num_row": 93, "60000": 93, "transformed_dataset": 93, "with_format": 93, "255": [93, 98], "cpu_count": 93, "torch_dataset": 93, "quick": [93, 104, 106], "super": 93, "relu": 93, "batchnorm2d": 93, "maxpool2d": 93, "lazylinear": 93, "flatten": 93, "get_test_accuraci": 93, "testload": [93, 106], "energi": 93, "trainload": [93, 106], "n_epoch": 93, "patienc": 93, "criterion": 93, "crossentropyloss": 93, "adamw": 93, "best_test_accuraci": 93, "start_epoch": 93, "running_loss": 93, "best_epoch": 93, "end_epoch": 93, "3f": [93, 108], "acc": [93, 101], "time_taken": 93, "compute_embed": 93, "compute_pred_prob": 93, "train_batch_s": 93, "num_work": 93, "worker": [93, 110], "train_id_list": 93, "test_id_list": 93, "train_id": 93, "test_id": 93, "embeddings_model": 93, "ntrain": 93, "trainset": 93, "testset": 93, "pin_memori": 93, "fold_embed": 93, "fold_pred_prob": 93, "finish": 93, "482": 93, "720": 93, "329": [93, 95, 100, 105], "88": [93, 98, 100, 101, 104, 105, 108], "195": [93, 97, 100], "739": 93, "493": 93, "060": 93, "958": 93, "330": [93, 100, 105], "505": 93, "603": 93, "476": [93, 100], "340": [93, 100], "938": 93, "328": [93, 105], "310": 93, "605": [93, 100], "reorder": 93, "hstack": [93, 99, 101, 103], "max_preval": [93, 97], "7714": 93, "3772": 93, "3585": 93, "166": 93, "3651": 93, "27080": 93, "873833e": 93, "40378": 93, "915575e": 93, "25316": 93, "390277e": 93, "06": [93, 100, 101, 105, 110], "2090": 93, "751164e": 93, "14999": 93, "881301e": 93, "9569": 93, "11262": 93, "000003": 93, "coat": [93, 98], "shirt": [93, 98], "19228": 93, "000010": 93, "dress": 93, "32657": 93, "000013": 93, "bag": [93, 98, 106, 107], "21282": 93, "000016": [93, 100], "53564": 93, "000018": [93, 100], "pullov": 93, "6321": 93, "30968": 93, "001267": 93, "30659": 93, "000022": [93, 110], "47824": 93, "001454": 93, "3370": 93, "000026": 93, "54565": 93, "001854": 93, "9762": 93, "258": 93, "47139": 93, "000033": 93, "166980": 93, "986195": 93, "997205": 93, "sandal": [93, 98], "948781": 93, "999358": 93, "54078": 93, "17371": 93, "000025": 93, "plot_label_issue_exampl": 93, "nrow": [93, 106], "ceil": [93, 100], "axes_list": 93, "label_issue_indic": 93, "gl": 93, "sl": 93, "fontdict": 93, "imshow": [93, 106], "cmap": [93, 97, 108], "grai": 93, "subplots_adjust": 93, "hspace": 93, "outsiz": 93, "outlier_issu": [93, 96], "outlier_issues_df": 93, "depict": [93, 104, 105, 106, 107, 109], "plot_outlier_issues_exampl": 93, "n_comparison_imag": 93, "sample_from_class": 93, "number_of_sampl": 93, "non_outlier_indic": 93, "isnul": [93, 97], "non_outlier_indices_excluding_curr": 93, "sampled_indic": 93, "label_scores_of_sampl": 93, "top_score_indic": 93, "top_label_indic": 93, "sampled_imag": 93, "get_image_given_label_and_sampl": 93, "image_from_dataset": 93, "corresponding_label": 93, "comparison_imag": 93, "images_to_plot": 93, "idlist": 93, "iterrow": 93, "near_duplicate_issu": [93, 99], "closest": 93, "counterpart": 93, "near_duplicate_issues_df": 93, "plot_near_duplicate_issue_exampl": 93, "seen_id_pair": 93, "get_image_and_given_label_and_predicted_label": 93, "duplicate_imag": 93, "nd_set": 93, "challeng": 93, "dark_issu": 93, "reveal": [93, 105, 109], "dark_issues_df": 93, "is_dark_issu": [93, 97], "34848": 93, "203922": 93, "50270": 93, "204588": 93, "3936": 93, "213098": 93, "733": 93, "217686": 93, "8094": 93, "230118": 93, "plot_image_issue_exampl": 93, "difficult": 93, "disproportion": [93, 97], "lowinfo_issu": 93, "lowinfo_issues_df": 93, "is_low_information_issu": 93, "53050": 93, "067975": 93, "40875": 93, "089929": 93, "9594": 93, "092601": 93, "34825": 93, "107744": 93, "37530": 93, "108516": 93, "lot": 93, "workflow": [94, 99, 100, 102, 108], "histgradientboostingclassifi": 95, "cat_featur": 95, "boost": [95, 99, 103, 108], "xgboost": [95, 99, 100, 108], "think": [95, 96, 99, 104, 109, 110], "nonzero": 95, "358": 95, "941": 95, "294": [95, 105], "46": [95, 97, 98, 100, 101, 105], "7109": 95, "000005": [95, 96], "886": 95, "000059": 95, "709": [95, 100], "000104": 95, "000169": 95, "689": 95, "000181": 95, "3590": 95, "051882e": 95, "683133e": 95, "536582e": 95, "406589e": 95, "324246e": 95, "6165": 95, "582": [95, 100], "185": [95, 97, 98, 105, 110], "187": [95, 98, 100], "898": 95, "0000": [95, 96, 98, 100, 101], "865": 95, "515002": 95, "837": 95, "556480": 95, "622": 95, "593068": 95, "593207": 95, "920": 95, "618041": 95, "4386345844794593e": 95, "issue_result": 95, "000842": 95, "555944": 95, "004374": 95, "sorted_issu": 95, "73": [95, 97, 98, 100, 104, 105, 108], "deserv": 95, "outlier_result": 95, "sorted_outli": 95, "56": [95, 97, 98, 108], "96": [95, 97, 98, 100, 101, 104, 105, 108], "style": [95, 97, 109], "font": 95, "18px": 95, "ff00ff": 95, "bac": 95, "duplicate_result": 95, "lowest_scoring_dupl": 95, "idxmin": [95, 99], "indices_to_displai": 95, "tolist": [95, 99, 100, 104], "perhap": [95, 101, 103], "second_lowest_scoring_dupl": 95, "next_indices_to_displai": 95, "wari": [95, 96, 99], "your_featur": 96, "text_embed": 96, "data_dict": [96, 101, 103], "85": [96, 100, 105], "38": [96, 97, 98, 105], "9710": 96, "981": 96, "974": 96, "000146": 96, "982": [96, 98], "000224": 96, "971": 96, "000507": 96, "980": [96, 98], "000960": 96, "3584": 96, "994": 96, "009642": 96, "999": 96, "013067": 96, "013841": 96, "433": 96, "014722": 96, "989": 96, "018224": 96, "6070": 96, "160": [96, 108], "095724": 96, "148": 96, "006237": 96, "546": [96, 100], "099341": 96, "514": 96, "006485": 96, "481": 96, "123418": 96, "008165": 96, "313": [96, 100, 105], "564102": 96, "572258": 96, "574915": 96, "31": [96, 97, 98, 100, 101, 103, 105], "575507": 96, "575874": 96, "792090": 96, "257611": 96, "698710": 96, "182121": 96, "771619": 96, "data_with_suggested_label": 96, "suggested_label": 96, "withdraw": 96, "monei": 96, "lowest_quality_outli": 96, "OR": 96, "636c65616e6c616220697320617765736f6d6521": 96, "phone": [96, 98], "gone": 96, "samp": 96, "br": 96, "press": [96, 110], "nonsens": 96, "sens": 96, "detriment": 96, "duplicate_issu": 96, "fee": 96, "go": [96, 97, 98, 101], "p_valu": 96, "benign": 96, "curat": [96, 102], "bigger": 97, "make_classif": 97, "5000": [97, 106], "n_featur": 97, "n_inform": 97, "n_redund": 97, "n_repeat": 97, "n_class": 97, "n_clusters_per_class": 97, "flip_i": 97, "class_sep": 97, "faiss": 97, "x_faiss": 97, "float32": [97, 105], "normalize_l2": 97, "index_factori": 97, "hnsw32": 97, "flat": [97, 98], "metric_inner_product": 97, "a_min": 97, "a_max": 97, "create_knn_graph": 97, "assert": 97, "indices_1d": 97, "ravel": 97, "distances_1d": 97, "sort_graph_by_row_valu": 97, "warn_when_not_sort": 97, "50000": 97, "523": [97, 100], "991400": 97, "356958": 97, "362": 97, "619565": 97, "108": [97, 105], "500000": 97, "651838": 97, "999827": 97, "031217": 97, "933716": 97, "627345": 97, "998540": 97, "530909": 97, "296974": 97, "646765": 97, "942721": 97, "332824": 97, "803246": 97, "625202": 97, "999816": 97, "474031": 97, "706253": 97, "655108": 97, "997703": 97, "131466": 97, "912389": 97, "639200": 97, "4995": 97, "998646": 97, "504755": 97, "746777": 97, "680033": 97, "4996": 97, "894230": 97, "340986": 97, "816472": 97, "640711": 97, "4997": 97, "999100": 97, "428545": 97, "592421": 97, "658949": 97, "4998": 97, "986792": 97, "273710": 97, "618033": 97, "4999": 97, "986776": 97, "273524": 97, "618084": 97, "instabl": 97, "proxim": 97, "analys": 97, "comfort": 97, "explor": [97, 105, 106], "third": 97, "parti": [97, 110], "newsgroup": 97, "alt": [97, 98], "atheism": [97, 98], "sci": [97, 98], "fetch_20newsgroup": 97, "newsgroups_train": 97, "header": 97, "footer": 97, "quot": 97, "df_text": 97, "target_nam": 97, "enlighten": 97, "omnipot": 97, "19apr199320262420": 97, "kelvin": 97, "jpl": 97, "nasa": 97, "gov": 97, "baa": 97, "nhenri": 97, "he": 97, "nno": 97, "ge": 97, "nlucki": 97, "babi": [97, 98], "tfidfvector": 97, "feature_extract": 97, "x_vector": 97, "data_valuation_issu": 97, "147": [97, 101, 105], "500047": 97, "500093": 97, "499953": 97, "1068": 97, "1069": 97, "1070": 97, "1071": 97, "1072": 97, "1073": 97, "concentr": 97, "seaborn": 97, "sn": 97, "distinguish": [97, 100], "strip": 97, "stripplot": 97, "hue": [97, 108], "dodg": 97, "jitter": 97, "axvlin": [97, 106], "xlabel": 97, "ourselv": 97, "make_blob": 97, "center": [97, 98], "cluster_std": 97, "n_noisy_label": 97, "meaning": [97, 99, 100, 106], "silhouette_scor": 97, "gridsearchcv": 97, "silhouett": 97, "cluster_label": 97, "fit_predict": 97, "param_grid": [97, 100], "grid_search": 97, "best_kmean": 97, "best_estimator_": 97, "underperforming_group_issu": 97, "328308": 97, "tab10": 97, "domain": 97, "knowledg": [97, 101], "dataset_tsv": 97, "ag": [97, 108], "gender": 97, "educ": 97, "experi": 97, "highsalari": 97, "indiana": 97, "phd": 97, "male": 97, "bachelor": 97, "femal": 97, "kansa": 97, "school": [97, 98], "ohio": 97, "57": [97, 98, 100, 101], "california": 97, "59": [97, 98, 105], "34": [97, 98, 101, 103, 105, 110], "63": [97, 100, 101, 105, 108], "47": [97, 98, 105], "stringio": 97, "sep": [97, 110], "easier": [97, 101], "simplic": [97, 104], "ordinalencod": 97, "columns_to_encod": 97, "encoded_df": 97, "salari": 97, "573681": 97, "underpin": 97, "caught": 97, "whenev": 97, "generate_data_depend": 97, "num_sampl": 97, "a1": 97, "a2": 97, "a3": 97, "375": 97, "975": 97, "non_iid_issu": 97, "796474": 97, "842432": 97, "922562": 97, "820759": 97, "873136": 97, "887373": 97, "825101": 97, "855875": 97, "751795": 97, "835796": 97, "ylabel": [97, 106], "coolwarm": 97, "colorbar": [97, 108], "strong": 97, "evid": [97, 100], "inter": 97, "mitig": 97, "risk": [97, 100], "deeper": 97, "tsv": 97, "tab": 97, "pars": 97, "annual_spend": 97, "number_of_transact": 97, "last_purchase_d": 97, "rural": 97, "4099": 97, "2024": [97, 110], "6421": 97, "nat": 97, "suburban": 97, "5436": 97, "4046": 97, "66": [97, 98, 100], "3467": 97, "67": [97, 98, 100, 105, 108], "4757": 97, "4199": 97, "4991": 97, "4655": 97, "82": [97, 98, 100, 101, 105, 108, 110], "5584": 97, "urban": 97, "3102": 97, "6637": 97, "9167": 97, "6790": 97, "5327": 97, "parse_d": 97, "lose": 97, "intact": 97, "encode_categorical_column": 97, "placehold": 97, "dropna": [97, 103], "category_to_numb": 97, "_encod": 97, "gender_encod": 97, "location_encod": 97, "focus": [97, 100, 101, 103, 104, 108], "null_issu": 97, "833333": 97, "sorted_indic": [97, 105], "sorted_df": 97, "nice": 97, "styler": 97, "combined_df": 97, "concat": [97, 100, 108], "highlight_null_valu": 97, "val": [97, 101], "yellow": [97, 98], "highlight_datalab_column": 97, "lightblu": 97, "highlight_is_null_issu": 97, "orang": [97, 98], "styled_df": 97, "nbsp": [97, 99, 100, 101], "160000": 97, "820000": 97, "460000": 97, "470000": 97, "960000": 97, "620000": 97, "550000": 97, "660000": 97, "670000": [97, 98], "370000": 97, "530000": 97, "710000": 97, "020000": 97, "320000": 97, "990000": 97, "rarer": 97, "fairer": 97, "randomli": [97, 100, 101], "class_imbalance_issu": 97, "countplot": 97, "xtick": 97, "rotat": 97, "ytick": 97, "filtered_df": 97, "xy": 97, "va": 97, "textual": 97, "get_ytick": 97, "nbar": 97, "nimbal": 97, "get_legend_handles_label": 97, "title_fonts": 97, "aspect": 97, "anomali": [97, 105], "enhanc": [97, 101, 103, 105], "artifici": 97, "directori": [97, 110], "subdirectori": 97, "nc": [97, 105, 109, 110], "unzip": [97, 105, 110], "09": [97, 100, 104, 105, 108, 110], "199": [97, 100, 105], "111": [97, 103, 108], "153": [97, 100, 105, 110], "110": [97, 105], "connect": [97, 110], "443": [97, 110], "await": [97, 110], "ok": [97, 107, 110], "986707": 97, "964k": 97, "963": 97, "58k": 97, "kb": 97, "mb": [97, 110], "imagefold": 97, "load_image_dataset": 97, "data_dir": 97, "root": [97, 106], "image_dataset": 97, "img": [97, 106, 108], "from_dict": [97, 99], "darkened_imag": 97, "job": 97, "015": 97, "label_uncorrelatedness_scor": 97, "image_issu": 97, "nimag": 97, "237196": 97, "197229": 97, "254188": 97, "229170": 97, "208907": 97, "793840": 97, "196": [97, 100, 101, 105], "197": [97, 101, 105], "971560": 97, "198": [97, 101, 105], "862236": 97, "973533": 97, "stronger": 97, "frog": [97, 98, 106], "darken": 97, "concept": 97, "notabl": 97, "preval": 97, "warrant": 97, "programmat": 97, "plot_scores_label": 97, "issues_copi": 97, "boxplot": 97, "refin": 98, "instruct": [98, 99, 100], "studi": [98, 105], "mnist_test_set": 98, "imagenet_val_set": 98, "tench": 98, "goldfish": 98, "white": [98, 110], "shark": 98, "tiger": 98, "hammerhead": 98, "electr": 98, "rai": 98, "stingrai": 98, "cock": 98, "hen": 98, "ostrich": 98, "brambl": 98, "goldfinch": 98, "hous": 98, "finch": 98, "junco": 98, "indigo": 98, "bunt": 98, "american": [98, 110], "robin": 98, "bulbul": 98, "jai": 98, "magpi": 98, "chickade": 98, "dipper": 98, "kite": 98, "bald": 98, "eagl": 98, "vultur": 98, "grei": 98, "owl": 98, "salamand": 98, "smooth": 98, "newt": 98, "spot": [98, 99, 105], "axolotl": 98, "bullfrog": 98, "tree": 98, "tail": 98, "loggerhead": 98, "sea": 98, "turtl": 98, "leatherback": 98, "mud": 98, "terrapin": 98, "band": 98, "gecko": 98, "green": [98, 110], "iguana": 98, "carolina": 98, "anol": 98, "desert": 98, "grassland": 98, "whiptail": 98, "lizard": 98, "agama": 98, "frill": 98, "neck": 98, "allig": 98, "gila": 98, "monster": 98, "european": 98, "chameleon": 98, "komodo": 98, "dragon": 98, "nile": 98, "crocodil": 98, "triceratop": 98, "worm": 98, "snake": 98, "ring": 98, "eastern": 98, "hog": 98, "nose": 98, "kingsnak": 98, "garter": 98, "water": 98, "vine": 98, "night": 98, "boa": 98, "constrictor": 98, "african": 98, "rock": 98, "indian": 98, "cobra": 98, "mamba": 98, "saharan": 98, "horn": 98, "viper": 98, "diamondback": 98, "rattlesnak": 98, "sidewind": 98, "trilobit": 98, "harvestman": 98, "scorpion": 98, "garden": 98, "spider": 98, "barn": 98, "southern": 98, "widow": 98, "tarantula": 98, "wolf": 98, "tick": 98, "centiped": 98, "grous": 98, "ptarmigan": 98, "ruf": 98, "prairi": 98, "peacock": 98, "quail": 98, "partridg": 98, "parrot": 98, "macaw": 98, "sulphur": 98, "crest": 98, "cockatoo": 98, "lorikeet": 98, "coucal": 98, "bee": 98, "eater": 98, "hornbil": 98, "hummingbird": 98, "jacamar": 98, "toucan": 98, "breast": 98, "mergans": 98, "goos": 98, "swan": 98, "tusker": 98, "echidna": 98, "platypu": 98, "wallabi": 98, "koala": 98, "wombat": 98, "jellyfish": 98, "anemon": 98, "brain": 98, "coral": 98, "flatworm": 98, "nematod": 98, "conch": 98, "snail": 98, "slug": 98, "chiton": 98, "chamber": 98, "nautilu": 98, "dung": 98, "crab": 98, "fiddler": 98, "king": 98, "lobster": 98, "spini": 98, "crayfish": 98, "hermit": 98, "isopod": 98, "stork": 98, "spoonbil": 98, "flamingo": 98, "heron": 98, "egret": 98, "bittern": 98, "crane": 98, "bird": [98, 106], "limpkin": 98, "gallinul": 98, "coot": 98, "bustard": 98, "ruddi": 98, "turnston": 98, "dunlin": 98, "redshank": 98, "dowitch": 98, "oystercatch": 98, "pelican": 98, "penguin": 98, "albatross": 98, "whale": 98, "killer": 98, "dugong": 98, "lion": 98, "chihuahua": 98, "japanes": 98, "chin": 98, "maltes": 98, "pekinges": 98, "shih": 98, "tzu": 98, "charl": 98, "spaniel": 98, "papillon": 98, "terrier": 98, "rhodesian": 98, "ridgeback": 98, "afghan": [98, 110], "hound": 98, "basset": 98, "beagl": 98, "bloodhound": 98, "bluetick": 98, "coonhound": 98, "tan": 98, "walker": 98, "foxhound": 98, "redbon": 98, "borzoi": 98, "irish": 98, "wolfhound": 98, "italian": 98, "greyhound": 98, "whippet": 98, "ibizan": 98, "norwegian": 98, "elkhound": 98, "otterhound": 98, "saluki": 98, "scottish": 98, "deerhound": 98, "weimaran": 98, "staffordshir": 98, "bull": 98, "bedlington": 98, "border": 98, "kerri": 98, "norfolk": 98, "norwich": 98, "yorkshir": 98, "wire": 98, "fox": 98, "lakeland": 98, "sealyham": 98, "airedal": 98, "cairn": 98, "australian": 98, "dandi": 98, "dinmont": 98, "boston": 98, "miniatur": 98, "schnauzer": 98, "giant": 98, "tibetan": 98, "silki": 98, "wheaten": 98, "west": 98, "highland": 98, "lhasa": 98, "apso": 98, "retriev": 98, "curli": 98, "golden": 98, "labrador": 98, "chesapeak": 98, "bai": 98, "german": [98, 110], "shorthair": 98, "pointer": 98, "vizsla": 98, "setter": 98, "gordon": 98, "brittani": 98, "clumber": 98, "springer": 98, "welsh": 98, "cocker": 98, "sussex": 98, "kuvasz": 98, "schipperk": 98, "groenendael": 98, "malinoi": 98, "briard": 98, "kelpi": 98, "komondor": 98, "sheepdog": 98, "shetland": 98, "colli": 98, "bouvier": 98, "de": 98, "flandr": 98, "rottweil": 98, "shepherd": 98, "dobermann": 98, "pinscher": 98, "swiss": [98, 110], "mountain": 98, "bernes": 98, "appenzel": 98, "sennenhund": 98, "entlebuch": 98, "boxer": 98, "bullmastiff": 98, "mastiff": 98, "french": 98, "bulldog": 98, "dane": 98, "st": 98, "bernard": 98, "huski": 98, "alaskan": 98, "malamut": 98, "siberian": 98, "dalmatian": 98, "affenpinsch": 98, "basenji": 98, "pug": 98, "leonberg": 98, "newfoundland": 98, "pyrenean": 98, "samoi": 98, "pomeranian": 98, "chow": 98, "keeshond": 98, "griffon": 98, "bruxelloi": 98, "pembrok": 98, "corgi": 98, "cardigan": 98, "poodl": 98, "mexican": 98, "hairless": 98, "tundra": 98, "coyot": 98, "dingo": 98, "dhole": 98, "wild": 98, "hyena": 98, "kit": 98, "arctic": 98, "tabbi": 98, "persian": 98, "siames": 98, "egyptian": 98, "mau": 98, "cougar": 98, "lynx": 98, "leopard": 98, "snow": 98, "jaguar": 98, "cheetah": 98, "brown": [98, 109], "bear": 98, "polar": 98, "sloth": 98, "mongoos": 98, "meerkat": 98, "beetl": 98, "ladybug": 98, "longhorn": 98, "leaf": 98, "rhinocero": 98, "weevil": 98, "fly": 98, "ant": 98, "grasshopp": 98, "cricket": 98, "stick": 98, "insect": 98, "cockroach": 98, "manti": 98, "cicada": 98, "leafhopp": 98, "lacew": 98, "dragonfli": 98, "damselfli": 98, "admir": 98, "ringlet": 98, "monarch": 98, "butterfli": 98, "gossam": 98, "wing": 98, "starfish": 98, "urchin": 98, "cucumb": 98, "cottontail": 98, "rabbit": 98, "hare": 98, "angora": 98, "hamster": 98, "porcupin": 98, "squirrel": 98, "marmot": 98, "beaver": 98, "guinea": 98, "pig": 98, "sorrel": 98, "zebra": 98, "boar": 98, "warthog": 98, "hippopotamu": 98, "ox": 98, "buffalo": 98, "bison": 98, "bighorn": 98, "sheep": 98, "alpin": 98, "ibex": 98, "hartebeest": 98, "impala": 98, "gazel": 98, "dromedari": 98, "llama": 98, "weasel": 98, "mink": 98, "polecat": 98, "foot": 98, "ferret": 98, "otter": 98, "skunk": 98, "badger": 98, "armadillo": 98, "toed": 98, "orangutan": 98, "gorilla": 98, "chimpanze": 98, "gibbon": 98, "siamang": 98, "guenon": 98, "pata": 98, "monkei": 98, "baboon": 98, "macaqu": 98, "langur": 98, "colobu": 98, "probosci": 98, "marmoset": 98, "capuchin": 98, "howler": 98, "titi": 98, "geoffroi": 98, "lemur": 98, "indri": 98, "asian": 98, "eleph": 98, "bush": 98, "snoek": 98, "eel": 98, "coho": 98, "salmon": 98, "beauti": 98, "clownfish": 98, "sturgeon": 98, "garfish": 98, "lionfish": 98, "pufferfish": 98, "abacu": 98, "abaya": 98, "academ": 98, "gown": 98, "accordion": 98, "acoust": 98, "guitar": 98, "aircraft": 98, "carrier": 98, "airlin": 98, "airship": 98, "altar": 98, "ambul": 98, "amphibi": 98, "clock": [98, 110], "apiari": 98, "apron": 98, "wast": 98, "assault": 98, "rifl": 98, "backpack": 98, "bakeri": 98, "balanc": 98, "beam": 98, "balloon": 98, "ballpoint": 98, "pen": 98, "aid": 98, "banjo": 98, "balust": 98, "barbel": 98, "barber": 98, "chair": [98, 105], "barbershop": 98, "baromet": 98, "barrel": 98, "wheelbarrow": 98, "basebal": 98, "basketbal": 98, "bassinet": 98, "bassoon": 98, "swim": 98, "cap": 98, "bath": 98, "towel": 98, "bathtub": 98, "station": 98, "wagon": 98, "lighthous": 98, "beaker": 98, "militari": 98, "beer": 98, "bottl": 98, "glass": 98, "bell": 98, "cot": 98, "bib": 98, "bicycl": [98, 109], "bikini": 98, "binder": 98, "binocular": 98, "birdhous": 98, "boathous": 98, "bobsleigh": 98, "bolo": 98, "tie": 98, "poke": 98, "bonnet": 98, "bookcas": 98, "bookstor": 98, "bow": 98, "brass": 98, "bra": 98, "breakwat": 98, "breastplat": 98, "broom": 98, "bucket": 98, "buckl": 98, "bulletproof": 98, "vest": 98, "butcher": 98, "shop": 98, "taxicab": 98, "cauldron": 98, "candl": 98, "cannon": 98, "cano": 98, "mirror": [98, 105], "carousel": 98, "carton": 98, "wheel": 98, "teller": 98, "cassett": 98, "player": 98, "castl": 98, "catamaran": 98, "cd": 98, "cello": 98, "mobil": [98, 110], "chain": 98, "fenc": [98, 109], "mail": 98, "chainsaw": 98, "chest": 98, "chiffoni": 98, "chime": 98, "china": 98, "cabinet": 98, "christma": 98, "stock": 98, "church": 98, "movi": 98, "theater": 98, "cleaver": 98, "cliff": 98, "dwell": 98, "cloak": 98, "clog": 98, "cocktail": 98, "shaker": 98, "coffe": 98, "mug": 98, "coffeemak": 98, "coil": 98, "lock": 98, "keyboard": 98, "confectioneri": 98, "ship": [98, 106], "corkscrew": 98, "cornet": 98, "cowboi": 98, "boot": 98, "hat": 98, "cradl": 98, "crash": 98, "helmet": 98, "crate": 98, "infant": 98, "bed": 98, "crock": 98, "pot": 98, "croquet": 98, "crutch": 98, "cuirass": 98, "dam": 98, "desk": 98, "desktop": 98, "rotari": 98, "dial": 98, "telephon": 98, "diaper": 98, "watch": 98, "dine": 98, "dishcloth": 98, "dishwash": 98, "disc": 98, "brake": 98, "dock": 98, "sled": 98, "dome": 98, "doormat": 98, "drill": 98, "rig": 98, "drum": 98, "drumstick": 98, "dumbbel": 98, "dutch": 98, "oven": 98, "fan": 98, "locomot": 98, "entertain": 98, "envelop": 98, "espresso": 98, "powder": 98, "feather": 98, "fireboat": 98, "engin": [98, 109], "screen": 98, "sheet": 98, "flagpol": 98, "flute": 98, "footbal": 98, "forklift": 98, "fountain": 98, "poster": 98, "freight": 98, "fry": 98, "pan": 98, "fur": 98, "garbag": 98, "ga": 98, "pump": 98, "goblet": 98, "kart": 98, "golf": 98, "cart": 98, "gondola": 98, "gong": 98, "grand": 98, "piano": 98, "greenhous": 98, "grill": 98, "groceri": 98, "guillotin": 98, "barrett": 98, "hair": 98, "sprai": 98, "hammer": 98, "dryer": 98, "hand": [98, 101], "handkerchief": 98, "drive": 98, "harmonica": 98, "harp": 98, "harvest": 98, "hatchet": 98, "holster": 98, "honeycomb": 98, "hoop": 98, "skirt": 98, "horizont": 98, "bar": 98, "drawn": 98, "hourglass": 98, "ipod": 98, "cloth": 98, "iron": 98, "jack": 98, "lantern": 98, "jean": 98, "jeep": 98, "jigsaw": 98, "puzzl": 98, "pull": 98, "rickshaw": 98, "joystick": 98, "kimono": 98, "knee": 98, "pad": 98, "knot": 98, "ladl": 98, "lampshad": 98, "laptop": 98, "lawn": 98, "mower": 98, "knife": 98, "lifeboat": 98, "lighter": 98, "limousin": 98, "ocean": 98, "liner": 98, "lipstick": 98, "slip": 98, "shoe": 98, "lotion": 98, "speaker": 98, "loup": 98, "sawmil": 98, "magnet": 98, "compass": 98, "mailbox": 98, "tight": 98, "tank": 98, "manhol": 98, "maraca": 98, "marimba": 98, "maypol": 98, "maze": 98, "cup": [98, 105], "medicin": 98, "megalith": 98, "microphon": 98, "microwav": 98, "milk": 98, "minibu": 98, "miniskirt": 98, "minivan": 98, "missil": 98, "mitten": [98, 99], "mix": 98, "bowl": 98, "modem": 98, "monasteri": 98, "monitor": 98, "mope": 98, "mortar": 98, "mosqu": 98, "mosquito": 98, "scooter": 98, "bike": 98, "tent": 98, "mous": [98, 99], "mousetrap": 98, "van": 98, "muzzl": 98, "nail": 98, "brace": 98, "necklac": 98, "nippl": 98, "obelisk": 98, "obo": 98, "ocarina": 98, "odomet": 98, "oil": 98, "oscilloscop": 98, "overskirt": 98, "bullock": 98, "oxygen": 98, "packet": 98, "paddl": 98, "padlock": 98, "paintbrush": 98, "pajama": 98, "palac": [98, 110], "parachut": 98, "park": 98, "bench": 98, "meter": 98, "passeng": 98, "patio": 98, "payphon": 98, "pedest": 98, "pencil": 98, "perfum": 98, "petri": 98, "dish": 98, "photocopi": 98, "plectrum": 98, "pickelhaub": 98, "picket": 98, "pickup": 98, "pier": 98, "piggi": 98, "pill": 98, "pillow": 98, "ping": 98, "pong": 98, "pinwheel": 98, "pirat": 98, "pitcher": 98, "plane": 98, "planetarium": 98, "plastic": 98, "plate": 98, "rack": 98, "plow": 98, "plunger": 98, "polaroid": 98, "camera": 98, "pole": [98, 109], "polic": 98, "poncho": 98, "billiard": 98, "soda": 98, "potter": 98, "prayer": 98, "rug": 98, "printer": 98, "prison": 98, "projectil": 98, "projector": 98, "hockei": 98, "puck": 98, "punch": 98, "purs": 98, "quill": 98, "quilt": 98, "race": 98, "racket": 98, "radiat": 98, "radio": 98, "telescop": 98, "rain": 98, "recreat": 98, "reel": 98, "reflex": 98, "refriger": 98, "remot": 98, "restaur": 98, "revolv": 98, "rotisseri": 98, "eras": 98, "rugbi": 98, "ruler": 98, "safe": 98, "safeti": 98, "salt": 98, "sarong": 98, "saxophon": 98, "scabbard": 98, "bu": [98, 109], "schooner": 98, "scoreboard": 98, "crt": 98, "screw": 98, "screwdriv": 98, "seat": 98, "belt": 98, "sew": 98, "shield": 98, "shoji": 98, "basket": 98, "shovel": 98, "shower": 98, "curtain": 98, "ski": 98, "sleep": 98, "door": 98, "slot": 98, "snorkel": 98, "snowmobil": 98, "snowplow": 98, "soap": 98, "dispens": 98, "soccer": [98, 110], "sock": [98, 99], "solar": 98, "thermal": 98, "collector": 98, "sombrero": 98, "soup": 98, "heater": 98, "shuttl": 98, "spatula": 98, "motorboat": 98, "web": 98, "spindl": 98, "sport": [98, 110], "spotlight": 98, "stage": 98, "steam": 98, "arch": 98, "bridg": 98, "steel": 98, "stethoscop": 98, "scarf": 98, "stone": 98, "wall": [98, 109], "stopwatch": 98, "stove": 98, "strainer": 98, "tram": 98, "stretcher": 98, "couch": 98, "stupa": 98, "submarin": 98, "sundial": 98, "sunglass": 98, "sunscreen": 98, "suspens": 98, "mop": 98, "sweatshirt": 98, "swimsuit": 98, "swing": 98, "switch": 98, "syring": 98, "lamp": 98, "tape": 98, "teapot": 98, "teddi": 98, "televis": [98, 110], "tenni": 98, "thatch": 98, "roof": 98, "thimbl": 98, "thresh": 98, "throne": 98, "tile": 98, "toaster": 98, "tobacco": 98, "toilet": 98, "totem": 98, "tow": 98, "tractor": 98, "semi": 98, "trailer": 98, "trai": 98, "trench": 98, "tricycl": 98, "trimaran": 98, "tripod": 98, "triumphal": 98, "trolleybu": 98, "trombon": 98, "tub": 98, "turnstil": 98, "typewrit": 98, "umbrella": 98, "unicycl": 98, "upright": 98, "vacuum": 98, "cleaner": [98, 100], "vase": 98, "vault": 98, "velvet": 98, "vend": 98, "vestment": 98, "viaduct": 98, "violin": 98, "volleybal": 98, "waffl": 98, "wallet": 98, "wardrob": 98, "sink": 98, "wash": 98, "jug": 98, "tower": 98, "whiskei": 98, "whistl": 98, "wig": 98, "shade": [98, 109], "windsor": 98, "wine": 98, "wok": 98, "wooden": 98, "spoon": 98, "wool": 98, "rail": 98, "shipwreck": 98, "yawl": 98, "yurt": 98, "websit": 98, "comic": 98, "book": 98, "crossword": 98, "traffic": [98, 105, 109], "sign": [98, 109, 110], "dust": 98, "jacket": [98, 105], "menu": 98, "guacamol": 98, "consomm": 98, "trifl": 98, "ic": 98, "cream": 98, "pop": 98, "baguett": 98, "bagel": 98, "pretzel": 98, "cheeseburg": 98, "mash": 98, "potato": 98, "cabbag": 98, "broccoli": 98, "cauliflow": 98, "zucchini": 98, "spaghetti": 98, "squash": 98, "acorn": 98, "butternut": 98, "artichok": 98, "pepper": [98, 99], "cardoon": 98, "mushroom": 98, "granni": 98, "smith": 98, "strawberri": 98, "lemon": 98, "pineappl": 98, "banana": 98, "jackfruit": 98, "custard": 98, "appl": 98, "pomegran": 98, "hai": 98, "carbonara": 98, "chocol": 98, "syrup": 98, "dough": 98, "meatloaf": 98, "pizza": 98, "pie": 98, "burrito": 98, "eggnog": 98, "alp": 98, "bubbl": 98, "reef": 98, "geyser": 98, "lakeshor": 98, "promontori": 98, "shoal": 98, "seashor": 98, "vallei": 98, "volcano": 98, "bridegroom": 98, "scuba": 98, "diver": 98, "rapese": 98, "daisi": 98, "ladi": 98, "slipper": 98, "corn": 98, "rose": 98, "hip": 98, "chestnut": 98, "fungu": 98, "agar": 98, "gyromitra": 98, "stinkhorn": 98, "earth": 98, "star": 98, "wood": 98, "bolet": 98, "ear": 98, "cifar10_test_set": 98, "airplan": [98, 106], "automobil": [98, 106], "deer": [98, 106], "cifar100_test_set": 98, "aquarium_fish": 98, "boi": 98, "camel": 98, "caterpillar": 98, "cattl": [98, 110], "cloud": 98, "dinosaur": 98, "dolphin": 98, "flatfish": 98, "forest": 98, "girl": 98, "kangaroo": 98, "lawn_mow": 98, "man": 98, "maple_tre": 98, "motorcycl": [98, 109], "oak_tre": 98, "orchid": 98, "palm_tre": 98, "pear": 98, "pickup_truck": 98, "pine_tre": 98, "plain": 98, "poppi": 98, "possum": 98, "raccoon": 98, "road": [98, 109], "rocket": 98, "seal": 98, "shrew": 98, "skyscrap": 98, "streetcar": 98, "sunflow": 98, "sweet_pepp": 98, "trout": 98, "tulip": 98, "willow_tre": 98, "woman": [98, 105], "caltech256": 98, "ak47": 98, "bat": 98, "glove": 98, "birdbath": 98, "blimp": 98, "bonsai": 98, "boom": 98, "breadmak": 98, "buddha": 98, "bulldoz": 98, "cactu": 98, "cake": 98, "tire": 98, "cartman": 98, "cereal": 98, "chandeli": 98, "chess": 98, "board": 98, "chimp": 98, "chopstick": 98, "coffin": 98, "coin": 98, "comet": 98, "cormor": 98, "globe": 98, "diamond": 98, "dice": 98, "doorknob": 98, "drink": 98, "straw": 98, "dumb": 98, "eiffel": 98, "elk": 98, "ewer": 98, "eyeglass": 98, "fern": 98, "fighter": 98, "jet": [98, 108], "extinguish": 98, "hydrant": 98, "firework": 98, "flashlight": 98, "floppi": 98, "fri": 98, "frisbe": 98, "galaxi": 98, "giraff": 98, "goat": 98, "gate": 98, "grape": 98, "pick": [98, 99], "hamburg": 98, "hammock": 98, "harpsichord": 98, "hawksbil": 98, "helicopt": 98, "hibiscu": 98, "homer": 98, "simpson": 98, "horsesho": 98, "air": 98, "skeleton": 98, "ibi": 98, "cone": 98, "iri": 98, "jesu": 98, "christ": 98, "joi": 98, "kayak": 98, "ketch": 98, "ladder": 98, "lath": 98, "licens": 98, "lightbulb": 98, "lightn": 98, "mandolin": 98, "mar": 98, "mattress": 98, "megaphon": 98, "menorah": 98, "microscop": 98, "minaret": 98, "minotaur": 98, "motorbik": 98, "mussel": 98, "neckti": 98, "octopu": 98, "palm": 98, "pilot": 98, "paperclip": 98, "shredder": 98, "pci": 98, "peopl": [98, 105], "pez": 98, "picnic": 98, "pram": 98, "prai": 98, "pyramid": 98, "rainbow": 98, "roulett": 98, "saddl": 98, "saturn": 98, "segwai": 98, "propel": 98, "sextant": 98, "music": 98, "skateboard": 98, "smokestack": 98, "sneaker": 98, "boat": 98, "stain": 98, "steer": 98, "stirrup": 98, "superman": 98, "sushi": 98, "armi": [98, 110], "sword": 98, "tambourin": 98, "teepe": 98, "court": 98, "theodolit": 98, "tomato": 98, "tombston": 98, "tour": 98, "pisa": 98, "treadmil": 98, "fork": 98, "tweezer": 98, "unicorn": 98, "vcr": 98, "waterfal": 98, "watermelon": 98, "weld": 98, "windmil": 98, "xylophon": 98, "yarmulk": 98, "yo": 98, "toad": 98, "twenty_news_test_set": 98, "comp": 98, "graphic": [98, 109], "misc": [98, 110], "sy": 98, "ibm": 98, "pc": 98, "hardwar": 98, "mac": 98, "forsal": 98, "rec": 98, "crypt": 98, "electron": 98, "med": 98, "soc": 98, "religion": 98, "christian": [98, 110], "talk": [98, 110], "polit": 98, "gun": 98, "mideast": 98, "amazon": 98, "neutral": 98, "imdb_test_set": 98, "all_class": 98, "20news_test_set": 98, "_load_classes_predprobs_label": 98, "dataset_nam": 98, "labelerror": 98, "url_bas": 98, "5392f6c71473055060be3044becdde1cbc18284d": 98, "url_label": 98, "original_test_label": 98, "_original_label": 98, "url_prob": 98, "cross_validated_predicted_prob": 98, "_pyx": 98, "num_part": 98, "datatset": 98, "bytesio": 98, "allow_pickl": 98, "pred_probs_part": 98, "url": 98, "_of_": 98, "nload": 98, "imdb": 98, "ve": [98, 99, 100, 101, 103, 105], "capit": 98, "29780": 98, "256": [98, 99, 100, 105], "780": 98, "medic": [98, 110], "doctor": 98, "254": [98, 105], "359223": 98, "640777": 98, "184": [98, 101], "258427": 98, "341176": 98, "263158": 98, "658824": 98, "337349": 98, "246575": 98, "662651": 98, "248": 98, "330000": 98, "355769": 98, "251": [98, 105, 110], "167": [98, 101, 105], "252": [98, 100], "112": [98, 100], "253": [98, 105], "022989": 98, "049505": 98, "190": [98, 101, 105], "002216": 98, "000974": 98, "000873": 98, "000739": 98, "32635": 98, "32636": 98, "32637": 98, "32638": 98, "32639": 98, "32640": 98, "051": 98, "002242": 98, "997758": 98, "002088": 98, "001045": 98, "997912": 98, "002053": 98, "997947": 98, "001980": 98, "000991": 98, "998020": 98, "001946": 98, "002915": 98, "998054": 98, "001938": 98, "002904": 98, "998062": 98, "001020": 98, "998980": 98, "001018": 98, "002035": 98, "998982": 98, "999009": 98, "0003": 98, "0002": 98, "071": 98, "067269": 98, "929": 98, "046": 98, "058243": 98, "954": 98, "035": 98, "032096": 98, "965": 98, "031": 98, "012232": 98, "969": 98, "022": 98, "025896": 98, "978": 98, "020": [98, 101], "013092": 98, "018": 98, "013065": 98, "016": 98, "030542": 98, "984": 98, "013": 98, "020833": 98, "987": 98, "012": 98, "010020": 98, "988": 98, "0073": 98, "0020": 98, "0016": 98, "0015": 98, "0014": 98, "0013": 98, "0012": 98, "0010": 98, "0008": 98, "0007": 98, "0006": 98, "0005": 98, "0004": 98, "244": [98, 105], "452381": 98, "459770": 98, "523364": 98, "460784": 98, "446602": 98, "103774": 98, "030612": 98, "110092": 98, "049020": 98, "0034": 98, "0032": 98, "0026": 98, "0025": 98, "4945": 98, "4946": 98, "4947": 98, "4948": 98, "4949": 98, "4950": 98, "846": 98, "7532": 98, "532": 98, "034483": 98, "009646": 98, "965517": 98, "030457": 98, "020513": 98, "969543": 98, "028061": 98, "035443": 98, "971939": 98, "025316": 98, "005168": 98, "974684": 98, "049751": 98, "979487": 98, "019920": 98, "042802": 98, "980080": 98, "017677": 98, "005115": 98, "982323": 98, "012987": 98, "005236": 98, "987013": 98, "012723": 98, "025126": 98, "987277": 98, "010989": 98, "008264": 98, "989011": 98, "010283": 98, "027778": 98, "989717": 98, "009677": 98, "990323": 98, "007614": 98, "010127": 98, "992386": 98, "005051": 98, "994949": 98, "005025": 98, "994975": 98, "005013": 98, "994987": 98, "001859": 98, "001328": 98, "000929": 98, "000664": 98, "186": [98, 101], "188": [98, 101, 104], "189": [98, 101], "snippet": 99, "nlp": [99, 110], "mind": [99, 101], "alphanumer": 99, "facilit": 99, "seamless": 99, "classlabel": 99, "guidanc": 99, "labels_str": 99, "datalab_str": 99, "labels_int": 99, "remap": 99, "datalab_int": 99, "my_dict": 99, "pet_nam": 99, "rover": 99, "rocki": 99, "speci": 99, "datalab_dataset": 99, "number_of_class": 99, "total_number_of_data_point": 99, "feed": 99, "alphabet": 99, "labels_proper_format": 99, "your_classifi": 99, "issues_datafram": 99, "class_predicted_for_flagged_exampl": 99, "class_predicted_for_all_exampl": 99, "grant": 99, "On": [99, 100, 101, 105], "merged_dataset": 99, "label_column_nam": 99, "datataset": 99, "fair": [99, 101], "game": 99, "speedup": [99, 106], "tempfil": 99, "mkdtemp": 99, "sped": 99, "anywai": 99, "pred_probs_merg": 99, "merge_rare_class": 99, "count_threshold": 99, "class_mapping_orig2new": 99, "heath_summari": 99, "num_examples_per_class": 99, "rare_class": 99, "num_classes_merg": 99, "other_class": 99, "labels_merg": 99, "new_c": 99, "merged_prob": 99, "new_class": 99, "original_class": 99, "num_check": 99, "ones_array_ref": 99, "isclos": 99, "though": [99, 101, 110], "successfulli": 99, "virtuou": [99, 103], "cycl": [99, 103], "jointli": 99, "junk": 99, "clutter": 99, "unknown": 99, "caltech": 99, "combined_boolean_mask": 99, "mask1": 99, "mask2": 99, "gradientboostingclassifi": [99, 101], "true_error": [99, 101, 104], "101": [99, 100, 105], "102": [99, 104, 105], "104": [99, 101, 105], "model_to_find_error": 99, "model_to_return": 99, "cl0": 99, "randomizedsearchcv": 99, "expens": 99, "param_distribut": 99, "learning_r": [99, 100, 101], "max_depth": [99, 100, 101], "magnitud": 99, "coeffici": [99, 108], "optin": 99, "environ": [99, 100, 101], "rerun": [99, 100, 101], "cell": [99, 100, 101], "unabl": [99, 100, 101], "render": [99, 100, 101], "nbviewer": [99, 100, 101], "cleanlearninginot": [99, 101], "fittedcleanlearn": [99, 101], "linearregressionlinearregress": 99, "unexpectedli": 99, "emphas": 99, "crucial": 99, "merge_duplicate_set": 99, "merge_kei": 99, "construct_group_kei": 99, "merged_set": 99, "consolidate_set": 99, "issubset": 99, "frozenset": [99, 100], "sets_list": 99, "mutabl": 99, "new_set": 99, "current_set": 99, "intersecting_set": 99, "lowest_score_strategi": 99, "sub_df": 99, "filter_near_dupl": 99, "strategy_fn": 99, "strategy_kwarg": 99, "duplicate_row": 99, "group_kei": 99, "to_keep_indic": 99, "groupbi": 99, "explod": 99, "to_remov": 99, "isin": [99, 106], "kept": 99, "ids_to_remove_seri": 99, "assist": 99, "streamlin": [99, 100], "ux": 99, "agpl": 99, "compani": 99, "commerci": 99, "alter": [99, 100], "email": 99, "team": 99, "anywher": 99, "profession": 99, "expert": 99, "recogn": 100, "vital": 100, "leakag": 100, "comparion": 100, "leak": 100, "blueprint": 100, "divers": 100, "parameter": 100, "tldr": 100, "answer": [100, 101], "subtl": 100, "faith": 100, "danger": 100, "inevit": [100, 106], "xgbclassifi": 100, "123456": 100, "df_train": 100, "s3": [100, 105, 109, 110], "amazonaw": [100, 105, 109, 110], "clos_train_data": 100, "df_test": 100, "clos_test_data": 100, "noisy_letter_grad": 100, "018bff": 100, "076d92": 100, "c80059": 100, "e38f8a": 100, "d57e1a": 100, "grade_l": 100, "notes_l": 100, "train_featur": 100, "train_features_v2": 100, "train_labels_v2": 100, "test_featur": 100, "preprocessed_train_data": 100, "preprocessed_test_data": 100, "haven": 100, "features_df": 100, "heterogenou": 100, "full_df": 100, "reset_index": [100, 103], "749": 100, "583745": 100, "291382": 100, "5837": 100, "748": 100, "604": 100, "510": 100, "227": [100, 104, 105], "719": 100, "690": 100, "444": 100, "547": 100, "647": 100, "2914": 100, "611": 100, "687869": 100, "610": 100, "687883": 100, "612": 100, "688146": 100, "609": 100, "688189": 100, "613": 100, "688713": 100, "2913818469137725": 100, "came": [100, 110], "full_duplicate_result": 100, "train_idx_cutoff": 100, "nd_set_has_index_over_training_cutoff": 100, "exact_dupl": 100, "627": 100, "678": 100, "615": 100, "292": 100, "620": 100, "420": 100, "704": 100, "431": 100, "459": 100, "672": 100, "564": 100, "696": 100, "exact_duplicates_indic": 100, "indices_of_duplicates_to_drop": 100, "4a3f75": 100, "d030b5": 100, "ddd0ba": 100, "8e6d24": 100, "464aab": 100, "ee3387": 100, "61e807": 100, "71d7b9": 100, "83e31f": 100, "edeb53": 100, "cd52b5": 100, "84": [100, 105, 108], "454e51": 100, "042686": 100, "12a73f": 100, "tree_method": 100, "hist": [100, 106], "enable_categor": 100, "booster": 100, "callback": 100, "colsample_bylevel": 100, "colsample_bynod": 100, "colsample_bytre": 100, "early_stopping_round": 100, "eval_metr": 100, "feature_typ": 100, "gamma": 100, "grow_polici": 100, "importance_typ": 100, "interaction_constraint": 100, "max_bin": 100, "max_cat_threshold": 100, "max_cat_to_onehot": 100, "max_delta_step": 100, "max_leav": 100, "min_child_weight": 100, "monotone_constraint": 100, "multi_strategi": 100, "n_estim": [100, 101], "num_parallel_tre": 100, "x27": [100, 101], "softprob": 100, "xgbclassifierifittedxgbclassifi": 100, "test_pred_prob": [100, 106], "test_lab": 100, "test_features_arrai": 100, "134": 100, "798507": 100, "370259": 100, "625352": 100, "524042": 100, "097015": 100, "7985": 100, "000537": 100, "000903": 100, "001743": 100, "106": 100, "001853": 100, "002121": 100, "3703": 100, "752463e": 100, "784418e": 100, "477741e": 100, "134230e": 100, "153555e": 100, "6254": 100, "143272": 100, "146501": 100, "161431": 100, "5240": 100, "765240": 100, "771221": 100, "801589": 100, "801652": 100, "810735": 100, "5240417899434826": 100, "0970": 100, "na": [100, 103], "test_label_issue_result": 100, "test_label_issues_ord": 100, "2bd759": 100, "34ccdd": 100, "bb3bab": 100, "103": [100, 101, 105], "bf1b14": 100, "4787de": 100, "865cbd": 100, "32d53f": 100, "5b2f76": 100, "28f8b4": 100, "df814d": 100, "f17261": 100, "1db3ff": 100, "ded944": 100, "124": [100, 105], "343dd3": 100, "homework": [100, 108], "8d904d": 100, "e4f0d5": 100, "d6d208": 100, "76c083": 100, "695f96": 100, "745c23": 100, "13b36e": 100, "5ba892": 100, "9f0216": 100, "003628": 100, "004006": 100, "004031": 100, "007930": 100, "013226": 100, "015255": 100, "017692": 100, "019767": 100, "036197": 100, "054746": 100, "055110": 100, "062675": 100, "112695": 100, "121059": 100, "171280": 100, "181689": 100, "208001": 100, "275028": 100, "346032": 100, "396350": 100, "401493": 100, "474349": 100, "mislead": 100, "breviti": 100, "indices_to_drop_from_test_data": 100, "df_test_clean": 100, "acc_origin": 100, "tediou": 100, "train_features_arrai": 100, "train_lab": 100, "318": [100, 108], "601": 100, "740433": 100, "344154": 100, "588290": 100, "437267": 100, "146423": 100, "977223": 100, "7404": 100, "162": 100, "000072": 100, "348": 100, "000161": 100, "232": [100, 105], "000256": 100, "205": [100, 105], "000458": 100, "000738": 100, "3442": 100, "588": 100, "358961e": 100, "336": [100, 105], "490911e": 100, "269": 100, "122475e": 100, "321": [100, 105], "374139e": 100, "311": 100, "358617e": 100, "5883": 100, "600": 100, "592": 100, "593": 100, "594": 100, "595": 100, "596": 100, "597": 100, "598": 100, "599": 100, "221": 100, "222": [100, 101], "315": 100, "332": [100, 105], "791060e": 100, "243": [100, 105], "540": 100, "379106e": 100, "396": 100, "397": 100, "398": 100, "399": 100, "4373": 100, "165": [100, 104], "550374": 100, "627357": 100, "627496": 100, "627502": 100, "627919": 100, "43726734378061227": 100, "1464": 100, "506": 100, "393": 100, "508": 100, "9772": 100, "402": 100, "401": 100, "aggress": 100, "faithfulli": 100, "label_issue_result": 100, "566": 100, "568": 100, "571": 100, "572": 100, "574": 100, "576": 100, "578": 100, "585": 100, "587": 100, "590": 100, "near_duplicates_idx": 100, "117": [100, 101, 108], "122": [100, 101, 105], "146": 100, "155": [100, 101, 105], "156": [100, 101], "173": [100, 105], "224": [100, 105], "272": 100, "277": [100, 105], "279": [100, 105], "288": 100, "300": [100, 103, 110], "342": 100, "352": 100, "363": 100, "365": 100, "366": 100, "384": 100, "388": 100, "394": 100, "404": 100, "474": 100, "480": 100, "494": 100, "515": 100, "536": 100, "537": 100, "539": 100, "542": 100, "outliers_idx": 100, "143": [100, 104, 105], "159": [100, 104, 105], "163": [100, 101], "193": [100, 101], "194": [100, 101], "208": 100, "240": [100, 105], "241": 100, "242": [100, 105], "247": [100, 105], "287": [100, 105], "295": [100, 105], "299": [100, 105], "307": [100, 105], "350": 100, "361": 100, "378": 100, "379": 100, "392": 100, "419": 100, "432": 100, "479": 100, "484": 100, "485": 100, "489": 100, "492": 100, "504": 100, "511": 100, "522": 100, "535": 100, "543": 100, "567": 100, "579": 100, "591": 100, "idx_to_drop": 100, "276": [100, 105], "df_train_cur": 100, "clean_clf": 100, "clean_pr": 100, "acc_clean": 100, "inaccur": 100, "hybrid": 100, "quantit": 100, "hyper": 100, "default_edit_param": 100, "drop_label_issu": 100, "drop_outli": 100, "drop_near_dupl": 100, "candid": [100, 105], "edit_data": 100, "percentag": [100, 101], "num_label_issues_to_drop": 100, "num_outliers_to_drop": 100, "dedupl": 100, "unique_clust": 100, "unique_clusters_list": 100, "near_duplicates_idx_to_drop": 100, "n_drop": 100, "label_issues_idx_to_drop": 100, "outliers_idx_to_drop": 100, "train_features_clean": 100, "train_labels_clean": 100, "itertool": 100, "finer": 100, "param_combin": 100, "best_scor": 100, "best_param": 100, "train_features_preprocess": 100, "train_labels_preprocess": 100, "depth": 101, "survei": [101, 110], "scienc": 101, "multivariate_norm": [101, 103, 104], "make_data": [101, 103], "cov": [101, 103, 104], "avg_trac": [101, 104], "py_tru": 101, "noise_matrix_tru": 101, "noise_marix": 101, "s_test": 101, "noisy_test_label": 101, "purpl": 101, "namespac": 101, "exec": 101, "markerfacecolor": [101, 104], "markeredgecolor": [101, 104, 108], "markers": [101, 104, 108], "markeredgewidth": [101, 104, 108], "realist": 101, "7560": 101, "637318e": 101, "896262e": 101, "548391e": 101, "923417e": 101, "375075e": 101, "3454": 101, "014051": 101, "020451": 101, "249": [101, 105], "042594": 101, "043859": 101, "045954": 101, "6120": 101, "023714": 101, "007136": 101, "119": [101, 105], "107266": 101, "033738": 101, "238": [101, 105], "119505": 101, "236": [101, 105], "037843": 101, "614915": 101, "624422": 101, "625965": 101, "626079": 101, "118": 101, "627675": 101, "695223": 101, "323529": 101, "523015": 101, "013720": 101, "675727": 101, "646521": 101, "magic": 101, "liter": 101, "identif": 101, "logisticregressionlogisticregress": 101, "ever": 101, "092": 101, "040": 101, "024": 101, "004": 101, "surpris": 101, "1705": 101, "01936": 101, "ton": 101, "yourfavoritemodel1": 101, "merged_label": 101, "merged_test_label": 101, "newli": [101, 103], "yourfavoritemodel2": 101, "yourfavoritemodel3": 101, "cl3": 101, "takeawai": 101, "my_test_pred_prob": 101, "my_test_pr": 101, "issues_test": 101, "corrected_test_label": 101, "pretend": 101, "cl_test_pr": 101, "fairli": 101, "label_acc": 101, "offset": 101, "nquestion": 101, "overestim": 101, "experienc": 101, "prioiri": 101, "known": 101, "versatil": 101, "label_issues_indic": 101, "213": [101, 105], "218": [101, 105], "152": 101, "170": 101, "214": 101, "164": [101, 104], "191": [101, 105], "206": [101, 105], "115": [101, 105], "201": [101, 105], "174": 101, "150": [101, 103, 105], "169": [101, 110], "151": [101, 105], "168": 101, "precision_scor": 101, "recall_scor": 101, "f1_score": 101, "true_label_issu": 101, "filter_by_list": 101, "718750": [101, 103], "807018": 101, "912": 101, "733333": 101, "800000": 101, "721311": 101, "792793": 101, "908": 101, "676923": 101, "765217": 101, "892": 101, "567901": 101, "702290": 101, "844": 101, "gaug": 101, "label_issues_count": 101, "172": [101, 104], "157": 101, "easiest": 101, "modular": 101, "penalti": 101, "l2": 101, "model3": 101, "cv_pred_probs_1": 101, "cv_pred_probs_2": 101, "cv_pred_probs_3": 101, "label_quality_scores_best": 101, "cv_pred_probs_ensembl": 101, "label_quality_scores_bett": 101, "superior": [101, 107], "timm": 102, "glad": 103, "multiannotator_label": 103, "noisier": 103, "local_data": [103, 104], "true_labels_train": [103, 104], "noise_matrix_bett": 103, "noise_matrix_wors": 103, "transpos": [103, 106], "zfill": 103, "row_na_check": 103, "notna": 103, "a0001": 103, "a0002": 103, "a0003": 103, "a0004": 103, "a0005": 103, "a0006": 103, "a0007": 103, "a0008": 103, "a0009": 103, "a0010": 103, "a0041": 103, "a0042": 103, "a0043": 103, "a0044": 103, "a0045": 103, "a0046": 103, "a0047": 103, "a0048": 103, "a0049": 103, "a0050": 103, "60856743": 103, "41693214": 103, "40908785": 103, "87147629": 103, "64941785": 103, "10774851": 103, "0524466": 103, "71853246": 103, "37169848": 103, "66031048": 103, "multiannotator_util": 103, "crude": 103, "straight": 103, "majority_vote_label": 103, "736118": 103, "757751": 103, "782232": 103, "715565": 103, "824256": 103, "quality_annotator_a0001": 103, "quality_annotator_a0002": 103, "quality_annotator_a0003": 103, "quality_annotator_a0004": 103, "quality_annotator_a0005": 103, "quality_annotator_a0006": 103, "quality_annotator_a0007": 103, "quality_annotator_a0008": 103, "quality_annotator_a0009": 103, "quality_annotator_a0010": 103, "quality_annotator_a0041": 103, "quality_annotator_a0042": 103, "quality_annotator_a0043": 103, "quality_annotator_a0044": 103, "quality_annotator_a0045": 103, "quality_annotator_a0046": 103, "quality_annotator_a0047": 103, "quality_annotator_a0048": 103, "quality_annotator_a0049": 103, "quality_annotator_a0050": 103, "070564": 103, "216078": 103, "119188": 103, "alongisd": 103, "244981": 103, "208333": 103, "295979": 103, "294118": 103, "324197": 103, "310345": 103, "355316": 103, "346154": 103, "439732": 103, "480000": 103, "a0031": 103, "523205": 103, "580645": 103, "a0034": 103, "535313": 103, "607143": 103, "a0021": 103, "606999": 103, "a0015": 103, "609526": 103, "678571": 103, "a0011": 103, "621103": 103, "692308": 103, "improved_consensus_label": 103, "majority_vote_accuraci": 103, "cleanlab_label_accuraci": 103, "8581081081081081": 103, "9797297297297297": 103, "besid": 103, "sorted_consensus_quality_scor": 103, "worst_qual": 103, "better_qu": 103, "worst_quality_accuraci": 103, "better_quality_accuraci": 103, "9893238434163701": 103, "improved_pred_prob": 103, "treat": [103, 104, 108, 110], "analzi": 103, "copyright": 104, "advertis": 104, "violenc": 104, "nsfw": 104, "celeba": 104, "make_multilabel_data": 104, "boxes_coordin": 104, "box_multilabel": 104, "make_multi": 104, "bx1": 104, "by1": 104, "bx2": 104, "by2": 104, "label_list": 104, "ur": 104, "upper": 104, "inidx": 104, "logical_and": 104, "inv_d": 104, "labels_idx": 104, "true_labels_test": 104, "dict_unique_label": 104, "get_color_arrai": 104, "dcolor": 104, "aa4400": 104, "55227f": 104, "55a100": 104, "00ff00": 104, "007f7f": 104, "386b55": 104, "0000ff": 104, "y_onehot": 104, "single_class_label": 104, "stratifi": [104, 107], "kf": 104, "train_index": 104, "test_index": 104, "clf_cv": 104, "x_train_cv": 104, "x_test_cv": 104, "y_train_cv": 104, "y_test_cv": 104, "y_pred_cv": 104, "saw": 104, "num_to_displai": 104, "275": 104, "267": 104, "225": 104, "171": 104, "234": 104, "262": [104, 105], "263": [104, 105], "266": [104, 105], "139": 104, "216": [104, 105], "265": 104, "despit": [104, 110], "suspect": 104, "888": 104, "8224": 104, "9632": 104, "968": 104, "6512": 104, "0444": 104, "774": 104, "labels_binary_format": 104, "labels_list_format": 104, "surround": 105, "scene": 105, "coco": 105, "everydai": 105, "has_label_issu": 105, "objectdetectionbenchmark": 105, "tutorial_obj": 105, "pkl": 105, "example_imag": 105, "_separate_label": 105, "_separate_predict": 105, "begin": 105, "image_path": 105, "rb": 105, "image_to_visu": 105, "seg_map": 105, "334": 105, "bboxes_ignor": 105, "290": 105, "286": 105, "285": 105, "231": 105, "293": 105, "235": 105, "289": 105, "282": 105, "281": 105, "271": 105, "280": 105, "326": 105, "333": 105, "261": 105, "319": 105, "257": 105, "283": 105, "303": 105, "316": 105, "323": 105, "327": 105, "226": 105, "228": 105, "219": 105, "239": 105, "209": 105, "202": 105, "230": 105, "215": 105, "220": 105, "229": 105, "217": [105, 110], "237": 105, "207": 105, "204": 105, "223": 105, "149": 105, "140": 105, "246": 105, "268": 105, "273": 105, "284": 105, "136": 105, "145": 105, "297": 105, "317": 105, "192": 105, "324": 105, "203": 105, "320": 105, "314": 105, "291": 105, "000000481413": 105, "jpg": 105, "42398": 105, "44503": 105, "29968": 105, "21005": 105, "9978472": 105, "forgot": 105, "drew": 105, "label_issue_idx": 105, "num_examples_to_show": 105, "138": 105, "97489622": 105, "70610878": 105, "98764951": 105, "88899237": 105, "99085805": 105, "issue_idx": 105, "95569726e": 105, "03354841e": 105, "57510169e": 105, "58447666e": 105, "39755858e": 105, "issue_to_visu": 105, "000000009483": 105, "95569726168054e": 105, "addition": [105, 109], "visibl": 105, "missmatch": 105, "likelei": 105, "agnost": 105, "vaidat": 105, "inconsist": 105, "000000395701": 105, "033548411774308e": 105, "armchair": 105, "tv": 105, "000000154004": 105, "38300759625496356": 105, "foreground": 105, "000000448410": 105, "0008575101690203273": 105, "crowd": 105, "alon": 105, "resembl": [105, 106], "000000499768": 105, "9748962231208227": 105, "000000521141": 105, "8889923658893665": 105, "000000143931": 105, "9876495074395956": 105, "bonu": 105, "uncov": 105, "irregular": 105, "object_detection_util": 105, "calculate_bounding_box_area": 105, "num_imgs_to_show": 105, "lab_object_count": 105, "pred_object_count": 105, "000000430073": 105, "000000183709": 105, "000000189475": 105, "label_norm": 105, "pred_norm": 105, "area": [105, 109], "lab_area": 105, "pred_area": 105, "lab_area_mean": 105, "lab_area_std": 105, "max_deviation_valu": 105, "max_deviation_class": 105, "deviation_valu": 105, "deviation_class": 105, "mean_area": 105, "std_area": 105, "class_area": 105, "deviations_awai": 105, "max_deviation_index": 105, "num_imgs_to_show_per_class": 105, "class_num": 105, "000000422886": 105, "000000341828": 105, "000000461009": 105, "train_feature_embed": 106, "ood_train_feature_scor": 106, "test_feature_embed": 106, "ood_test_feature_scor": 106, "ood_train_predictions_scor": 106, "train_pred_prob": 106, "ood_test_predictions_scor": 106, "pylab": 106, "rcparam": 106, "baggingclassifi": 106, "therebi": 106, "rescal": 106, "transform_norm": 106, "totensor": 106, "animal_class": 106, "non_animal_class": 106, "animal_idx": 106, "test_idx": 106, "toronto": 106, "edu": 106, "kriz": 106, "170498071": 106, "106723750": 106, "68it": 106, "plot_imag": 106, "visualize_outli": 106, "txt_class": 106, "npimg": 106, "show_label": 106, "data_subset": 106, "resnet50": 106, "corpu": 106, "2048": 106, "embed_imag": 106, "create_model": 106, "strang": 106, "odd": 106, "train_ood_features_scor": 106, "top_train_ood_features_idx": 106, "fun": 106, "negat": 106, "homogen": 106, "bottom_train_ood_features_idx": 106, "test_ood_features_scor": 106, "top_ood_features_idx": 106, "trade": 106, "5th": 106, "percentil": 106, "fifth_percentil": 106, "plt_rang": 106, "train_outlier_scor": 106, "test_outlier_scor": 106, "ood_features_indic": 106, "revisit": 106, "return_invers": 106, "train_feature_embeddings_sc": 106, "test_feature_embeddings_sc": 106, "train_pred_label": 106, "9702": 106, "train_ood_predictions_scor": 106, "test_ood_predictions_scor": 106, "lost": 106, "unsuit": 107, "convention": 107, "aforement": 107, "hypothet": 107, "contrast": 107, "tradit": 107, "disjoint": 107, "out_of_sample_pred_probs_for_a": 107, "out_of_sample_pred_probs_for_b": 107, "out_of_sample_pred_probs_for_c": 107, "out_of_sample_pred_prob": 107, "unsur": 107, "price": 108, "incom": 108, "sensor": 108, "histgradientboostingregressor": 108, "r2_score": 108, "student_grades_r": 108, "final_scor": 108, "true_final_scor": 108, "3d": 108, "mpl_toolkit": 108, "mplot3d": 108, "axes3d": 108, "errors_idx": 108, "add_subplot": 108, "z": 108, "errors_mask": 108, "feature_column": 108, "predicted_column": 108, "x_train_raw": 108, "x_test_raw": 108, "randomforestregressor": 108, "385101": 108, "499503": 108, "698255": 108, "776647": 108, "109373": 108, "170547": 108, "481096": 108, "984759": 108, "645270": 108, "795928": 108, "141": 108, "659": 108, "367": 108, "305": 108, "560": 108, "657": 108, "view_datapoint": 108, "preds_og": 108, "r2_og": 108, "838": 108, "found_label_issu": 108, "preds_cl": 108, "r2_cl": 108, "926": 108, "favorit": 108, "968627e": 108, "228799": 108, "646674e": 108, "402962": 108, "323818e": 108, "952758": 108, "422144e": 108, "456908": 108, "465815e": 108, "753968": 108, "791186e": 108, "110719": 108, "485156e": 108, "670640": 108, "225300e": 108, "749976": 108, "499679e": 108, "947007": 108, "067882e": 108, "648396": 108, "synthia": 109, "imagesegment": 109, "given_mask": 109, "predicted_mask": 109, "set_printopt": [109, 110], "sky": 109, "sidewalk": 109, "veget": 109, "terrain": 109, "rider": 109, "pred_probs_filepath": 109, "1088": 109, "1920": 109, "label_filepath": 109, "synthia_class": 109, "maunal": 109, "100000": 109, "244800": 109, "leftmost": 109, "middl": [109, 110], "infact": 109, "rightmost": 109, "discrep": 109, "3263230": 109, "783381": 109, "275110": 109, "255917": 109, "78225": 109, "55990": 109, "54315": 109, "33591": 109, "24645": 109, "21054": 109, "15045": 109, "14171": 109, "13832": 109, "13498": 109, "11490": 109, "9164": 109, "8769": 109, "6999": 109, "6031": 109, "5011": 109, "mistakenli": 109, "class_issu": 109, "aim": [109, 110], "domin": 109, "bunch": 110, "conll": 110, "2003": 110, "love": 110, "n_i": 110, "optional_list_of_ordered_class_nam": 110, "deepai": 110, "conll2003": 110, "rm": 110, "tokenclassif": 110, "2400": 110, "52e0": 110, "1a00": 110, "1207": 110, "982975": 110, "960k": 110, "959": 110, "94k": 110, "41mb": 110, "inflat": 110, "182": 110, "17045998": 110, "16m": 110, "octet": 110, "26m": 110, "5mb": 110, "bert": 110, "read_npz": 110, "filepath": 110, "corrsespond": 110, "iob2": 110, "given_ent": 110, "entity_map": 110, "readfil": 110, "startswith": 110, "docstart": 110, "isalpha": 110, "isupp": 110, "indices_to_preview": 110, "nsentenc": 110, "eu": 110, "reject": 110, "boycott": 110, "british": 110, "lamb": 110, "00030412": 110, "00023826": 110, "99936208": 110, "00007009": 110, "00002545": 110, "99998795": 110, "00000401": 110, "00000218": 110, "00000455": 110, "00000131": 110, "00000749": 110, "99996115": 110, "00001371": 110, "0000087": 110, "00000895": 110, "99998936": 110, "00000382": 110, "00000178": 110, "00000366": 110, "00000137": 110, "99999101": 110, "00000266": 110, "00000174": 110, "0000035": 110, "00000109": 110, "99998768": 110, "00000482": 110, "00000202": 110, "00000438": 110, "0000011": 110, "00000465": 110, "99996392": 110, "00001105": 110, "0000116": 110, "00000878": 110, "99998671": 110, "00000364": 110, "00000213": 110, "00000472": 110, "00000281": 110, "99999073": 110, "00000211": 110, "00000159": 110, "00000442": 110, "00000115": 110, "peter": 110, "blackburn": 110, "00000358": 110, "00000529": 110, "99995623": 110, "0000129": 110, "0000024": 110, "00001812": 110, "99994141": 110, "00001645": 110, "00002162": 110, "brussel": 110, "1996": 110, "00001172": 110, "00000821": 110, "00004661": 110, "0000618": 110, "99987167": 110, "99999061": 110, "00000201": 110, "00000195": 110, "00000408": 110, "00000135": 110, "2254": 110, "2907": 110, "19392": 110, "9962": 110, "8904": 110, "19303": 110, "12918": 110, "9256": 110, "11855": 110, "18392": 110, "20426": 110, "19402": 110, "14744": 110, "19371": 110, "4645": 110, "10331": 110, "9430": 110, "6143": 110, "18367": 110, "12914": 110, "todai": 110, "weather": 110, "march": 110, "scalfaro": 110, "northern": 110, "himself": 110, "said": 110, "germani": 110, "nastja": 110, "rysich": 110, "north": 110, "spla": 110, "fought": 110, "khartoum": 110, "govern": 110, "south": 110, "1983": 110, "autonomi": 110, "animist": 110, "region": 110, "moslem": 110, "arabis": 110, "mayor": 110, "antonio": 110, "gonzalez": 110, "garcia": 110, "revolutionari": 110, "wednesdai": 110, "troop": 110, "raid": 110, "farm": 110, "stole": 110, "rape": 110, "women": 110, "spring": 110, "chg": 110, "hrw": 110, "12pct": 110, "princ": 110, "photo": 110, "moment": 110, "spokeswoman": 110, "rainier": 110, "told": 110, "reuter": 110, "danila": 110, "carib": 110, "w224": 110, "equip": 110, "radiomet": 110, "earn": 110, "19996": 110, "london": 110, "denom": 110, "sale": 110, "uk": 110, "jp": 110, "fr": 110, "maccabi": 110, "hapoel": 110, "haifa": 110, "tel": 110, "aviv": 110, "hospit": 110, "rever": 110, "roman": 110, "cathol": 110, "nun": 110, "admit": 110, "calcutta": 110, "week": 110, "ago": 110, "fever": 110, "vomit": 110, "allianc": 110, "embattl": 110, "kabul": 110, "salang": 110, "highwai": 110, "mondai": 110, "tuesdai": 110, "suprem": 110, "council": 110, "led": 110, "jumbish": 110, "milli": 110, "movement": 110, "warlord": 110, "abdul": 110, "rashid": 110, "dostum": 110, "dollar": 110, "exchang": 110, "3570": 110, "12049": 110, "born": 110, "1937": 110, "provinc": 110, "anhui": 110, "dai": 110, "shanghai": 110, "citi": 110, "prolif": 110, "author": 110, "teacher": 110, "chines": 110, "16764": 110, "1990": 110, "historian": 110, "alan": 110, "john": 110, "percival": 110, "taylor": 110, "di": 110, "20446": 110, "pace": 110, "bowler": 110, "ian": 110, "harvei": 110, "claim": 110, "victoria": 110, "15514": 110, "cotti": 110, "osc": 110, "foreign": 110, "minist": 110, "7525": 110, "sultan": 110, "specter": 110, "crown": 110, "abdullah": 110, "defenc": 110, "aviat": 110, "jeddah": 110, "saudi": 110, "agenc": 110, "2288": 110, "hi": 110, "customari": 110, "outfit": 110, "champion": 110, "damp": 110, "scalp": 110, "canada": 110, "reign": 110, "olymp": 110, "donovan": 110, "bailei": 110, "1992": 110, "linford": 110, "christi": 110, "britain": 110, "1984": 110, "1988": 110, "carl": 110, "lewi": 110, "ambigi": 110, "punctuat": 110, "chicago": 110, "digest": 110, "philadelphia": 110, "usda": 110, "york": 110, "token_issu": 110, "471": 110, "kean": 110, "year": 110, "contract": 110, "manchest": 110, "19072": 110, "societi": 110, "bite": 110, "deliv": 110, "19910": 110, "father": 110, "clarenc": 110, "woolmer": 110, "renam": 110, "uttar": 110, "pradesh": 110, "india": 110, "ranji": 110, "trophi": 110, "nation": 110, "championship": 110, "captain": 110, "1949": 110, "15658": 110, "19879": 110, "iii": 110, "brian": 110, "shimer": 110, "randi": 110, "jone": 110, "19104": 110}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [39, 0, 0, "-", "dataset"], [42, 0, 0, "-", "experimental"], [46, 0, 0, "-", "filter"], [47, 0, 0, "-", "internal"], [61, 0, 0, "-", "models"], [63, 0, 0, "-", "multiannotator"], [66, 0, 0, "-", "multilabel_classification"], [69, 0, 0, "-", "object_detection"], [72, 0, 0, "-", "outlier"], [73, 0, 0, "-", "rank"], [74, 0, 0, "-", "regression"], [78, 0, 0, "-", "segmentation"], [82, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.data_valuation": [[4, 1, 1, "", "data_shapley_knn"]], "cleanlab.datalab": [[5, 0, 0, "-", "datalab"], [18, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[5, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[5, 4, 1, "", "class_names"], [5, 3, 1, "", "find_issues"], [5, 3, 1, "", "get_info"], [5, 3, 1, "", "get_issue_summary"], [5, 3, 1, "", "get_issues"], [5, 4, 1, "", "has_labels"], [5, 4, 1, "", "info"], [5, 4, 1, "", "issue_summary"], [5, 4, 1, "", "issues"], [5, 4, 1, "", "labels"], [5, 3, 1, "", "list_default_issue_types"], [5, 3, 1, "", "list_possible_issue_types"], [5, 3, 1, "", "load"], [5, 3, 1, "", "report"], [5, 3, 1, "", "save"]], "cleanlab.datalab.internal.adapter": [[13, 0, 0, "-", "imagelab"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, 2, 1, "", "CorrelationReporter"], [13, 2, 1, "", "CorrelationVisualizer"], [13, 2, 1, "", "ImagelabDataIssuesAdapter"], [13, 2, 1, "", "ImagelabIssueFinderAdapter"], [13, 2, 1, "", "ImagelabReporterAdapter"], [13, 1, 1, "", "create_imagelab"], [13, 1, 1, "", "handle_spurious_correlations"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter": [[13, 3, 1, "", "report"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer": [[13, 3, 1, "", "visualize"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter": [[13, 3, 1, "", "collect_issues_from_imagelab"], [13, 3, 1, "", "collect_issues_from_issue_manager"], [13, 3, 1, "", "collect_statistics"], [13, 3, 1, "", "filter_based_on_max_prevalence"], [13, 3, 1, "", "get_info"], [13, 3, 1, "", "get_issue_summary"], [13, 3, 1, "", "get_issues"], [13, 3, 1, "", "set_health_score"], [13, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter": [[13, 3, 1, "", "find_issues"], [13, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter": [[13, 3, 1, "", "get_report"], [13, 3, 1, "", "report"]], "cleanlab.datalab.internal": [[15, 0, 0, "-", "data"], [16, 0, 0, "-", "data_issues"], [19, 0, 0, "-", "issue_finder"], [17, 0, 0, "-", "issue_manager_factory"], [35, 0, 0, "-", "model_outputs"], [36, 0, 0, "-", "report"], [37, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[15, 2, 1, "", "Data"], [15, 5, 1, "", "DataFormatError"], [15, 5, 1, "", "DatasetDictError"], [15, 5, 1, "", "DatasetLoadError"], [15, 2, 1, "", "Label"], [15, 2, 1, "", "MultiClass"], [15, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[16, 2, 1, "", "DataIssues"], [16, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[16, 3, 1, "", "collect_issues_from_imagelab"], [16, 3, 1, "", "collect_issues_from_issue_manager"], [16, 3, 1, "", "collect_statistics"], [16, 3, 1, "", "get_info"], [16, 3, 1, "", "get_issue_summary"], [16, 3, 1, "", "get_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_summary"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "set_health_score"], [16, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[19, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[19, 3, 1, "", "find_issues"], [19, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[21, 0, 0, "-", "data_valuation"], [22, 0, 0, "-", "duplicate"], [23, 0, 0, "-", "imbalance"], [25, 0, 0, "-", "issue_manager"], [26, 0, 0, "-", "label"], [29, 0, 0, "-", "noniid"], [30, 0, 0, "-", "null"], [31, 0, 0, "-", "outlier"], [34, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[21, 6, 1, "", "DEFAULT_THRESHOLD"], [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.duplicate": [[22, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[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, 6, 1, "", "near_duplicate_sets"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[25, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[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.label": [[26, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 3, 1, "", "get_health_summary"], [26, 6, 1, "", "health_summary_parameters"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[28, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[28, 3, 1, "", "collect_info"], [28, 6, 1, "", "description"], [28, 3, 1, "", "find_issues"], [28, 6, 1, "", "info"], [28, 6, 1, "", "issue_name"], [28, 6, 1, "", "issue_score_key"], [28, 6, 1, "", "issues"], [28, 3, 1, "", "make_summary"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[29, 2, 1, "", "NonIIDIssueManager"], [29, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[30, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[30, 3, 1, "", "collect_info"], [30, 6, 1, "", "description"], [30, 3, 1, "", "find_issues"], [30, 6, 1, "", "info"], [30, 6, 1, "", "issue_name"], [30, 6, 1, "", "issue_score_key"], [30, 6, 1, "", "issues"], [30, 3, 1, "", "make_summary"], [30, 3, 1, "", "report"], [30, 6, 1, "", "summary"], [30, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[31, 6, 1, "", "DEFAULT_THRESHOLDS"], [31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "find_issues"], [31, 6, 1, "", "info"], [31, 6, 1, "", "issue_name"], [31, 6, 1, "", "issue_score_key"], [31, 6, 1, "", "issues"], [31, 3, 1, "", "make_summary"], [31, 6, 1, "", "metric"], [31, 6, 1, "", "ood"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[33, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, 2, 1, "", "RegressionLabelIssueManager"], [33, 1, 1, "", "find_issues_with_features"], [33, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[33, 3, 1, "", "collect_info"], [33, 6, 1, "", "description"], [33, 3, 1, "", "find_issues"], [33, 6, 1, "", "info"], [33, 6, 1, "", "issue_name"], [33, 6, 1, "", "issue_score_key"], [33, 6, 1, "", "issues"], [33, 3, 1, "", "make_summary"], [33, 3, 1, "", "report"], [33, 6, 1, "", "summary"], [33, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[34, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [34, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [34, 3, 1, "", "collect_info"], [34, 6, 1, "", "description"], [34, 3, 1, "", "filter_cluster_ids"], [34, 3, 1, "", "find_issues"], [34, 3, 1, "", "get_underperforming_clusters"], [34, 6, 1, "", "info"], [34, 6, 1, "", "issue_name"], [34, 6, 1, "", "issue_score_key"], [34, 6, 1, "", "issues"], [34, 3, 1, "", "make_summary"], [34, 3, 1, "", "perform_clustering"], [34, 3, 1, "", "report"], [34, 6, 1, "", "summary"], [34, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, 7, 1, "", "REGISTRY"], [17, 1, 1, "", "list_default_issue_types"], [17, 1, 1, "", "list_possible_issue_types"], [17, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[35, 2, 1, "", "ModelOutput"], [35, 2, 1, "", "MultiClassPredProbs"], [35, 2, 1, "", "MultiLabelPredProbs"], [35, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[36, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[36, 3, 1, "", "get_report"], [36, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[37, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[37, 6, 1, "", "CLASSIFICATION"], [37, 6, 1, "", "MULTILABEL"], [37, 6, 1, "", "REGRESSION"], [37, 3, 1, "", "__contains__"], [37, 3, 1, "", "__getitem__"], [37, 3, 1, "", "__iter__"], [37, 3, 1, "", "__len__"], [37, 3, 1, "", "from_str"], [37, 4, 1, "", "is_classification"], [37, 4, 1, "", "is_multilabel"], [37, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[39, 1, 1, "", "find_overlapping_classes"], [39, 1, 1, "", "health_summary"], [39, 1, 1, "", "overall_label_health_score"], [39, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[40, 0, 0, "-", "cifar_cnn"], [41, 0, 0, "-", "coteaching"], [43, 0, 0, "-", "label_issues_batched"], [44, 0, 0, "-", "mnist_pytorch"], [45, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[40, 2, 1, "", "CNN"], [40, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[40, 6, 1, "", "T_destination"], [40, 3, 1, "", "__call__"], [40, 3, 1, "", "add_module"], [40, 3, 1, "", "apply"], [40, 3, 1, "", "bfloat16"], [40, 3, 1, "", "buffers"], [40, 6, 1, "", "call_super_init"], [40, 3, 1, "", "children"], [40, 3, 1, "", "compile"], [40, 3, 1, "", "cpu"], [40, 3, 1, "", "cuda"], [40, 3, 1, "", "double"], [40, 6, 1, "", "dump_patches"], [40, 3, 1, "", "eval"], [40, 3, 1, "", "extra_repr"], [40, 3, 1, "", "float"], [40, 3, 1, "id0", "forward"], [40, 3, 1, "", "get_buffer"], [40, 3, 1, "", "get_extra_state"], [40, 3, 1, "", "get_parameter"], [40, 3, 1, "", "get_submodule"], [40, 3, 1, "", "half"], [40, 3, 1, "", "ipu"], [40, 3, 1, "", "load_state_dict"], [40, 3, 1, "", "modules"], [40, 3, 1, "", "named_buffers"], [40, 3, 1, "", "named_children"], [40, 3, 1, "", "named_modules"], [40, 3, 1, "", "named_parameters"], [40, 3, 1, "", "parameters"], [40, 3, 1, "", "register_backward_hook"], [40, 3, 1, "", "register_buffer"], [40, 3, 1, "", "register_forward_hook"], [40, 3, 1, "", "register_forward_pre_hook"], [40, 3, 1, "", "register_full_backward_hook"], [40, 3, 1, "", "register_full_backward_pre_hook"], [40, 3, 1, "", "register_load_state_dict_post_hook"], [40, 3, 1, "", "register_module"], [40, 3, 1, "", "register_parameter"], [40, 3, 1, "", "register_state_dict_pre_hook"], [40, 3, 1, "", "requires_grad_"], [40, 3, 1, "", "set_extra_state"], [40, 3, 1, "", "share_memory"], [40, 3, 1, "", "state_dict"], [40, 3, 1, "", "to"], [40, 3, 1, "", "to_empty"], [40, 3, 1, "", "train"], [40, 6, 1, "", "training"], [40, 3, 1, "", "type"], [40, 3, 1, "", "xpu"], [40, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[41, 1, 1, "", "adjust_learning_rate"], [41, 1, 1, "", "evaluate"], [41, 1, 1, "", "forget_rate_scheduler"], [41, 1, 1, "", "initialize_lr_scheduler"], [41, 1, 1, "", "loss_coteaching"], [41, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[43, 2, 1, "", "LabelInspector"], [43, 7, 1, "", "adj_confident_thresholds_shared"], [43, 1, 1, "", "find_label_issues_batched"], [43, 7, 1, "", "labels_shared"], [43, 7, 1, "", "pred_probs_shared"], [43, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[43, 3, 1, "", "get_confident_thresholds"], [43, 3, 1, "", "get_label_issues"], [43, 3, 1, "", "get_num_issues"], [43, 3, 1, "", "get_quality_scores"], [43, 3, 1, "", "score_label_quality"], [43, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[44, 2, 1, "", "CNN"], [44, 2, 1, "", "SimpleNet"], [44, 1, 1, "", "get_mnist_dataset"], [44, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[44, 3, 1, "", "__init_subclass__"], [44, 6, 1, "", "batch_size"], [44, 6, 1, "", "dataset"], [44, 6, 1, "", "epochs"], [44, 3, 1, "id0", "fit"], [44, 3, 1, "", "get_metadata_routing"], [44, 3, 1, "", "get_params"], [44, 6, 1, "", "loader"], [44, 6, 1, "", "log_interval"], [44, 6, 1, "", "lr"], [44, 6, 1, "", "momentum"], [44, 6, 1, "", "no_cuda"], [44, 3, 1, "id1", "predict"], [44, 3, 1, "id4", "predict_proba"], [44, 6, 1, "", "seed"], [44, 3, 1, "", "set_fit_request"], [44, 3, 1, "", "set_params"], [44, 3, 1, "", "set_predict_proba_request"], [44, 3, 1, "", "set_predict_request"], [44, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[44, 6, 1, "", "T_destination"], [44, 3, 1, "", "__call__"], [44, 3, 1, "", "add_module"], [44, 3, 1, "", "apply"], [44, 3, 1, "", "bfloat16"], [44, 3, 1, "", "buffers"], [44, 6, 1, "", "call_super_init"], [44, 3, 1, "", "children"], [44, 3, 1, "", "compile"], [44, 3, 1, "", "cpu"], [44, 3, 1, "", "cuda"], [44, 3, 1, "", "double"], [44, 6, 1, "", "dump_patches"], [44, 3, 1, "", "eval"], [44, 3, 1, "", "extra_repr"], [44, 3, 1, "", "float"], [44, 3, 1, "", "forward"], [44, 3, 1, "", "get_buffer"], [44, 3, 1, "", "get_extra_state"], [44, 3, 1, "", "get_parameter"], [44, 3, 1, "", "get_submodule"], [44, 3, 1, "", "half"], [44, 3, 1, "", "ipu"], [44, 3, 1, "", "load_state_dict"], [44, 3, 1, "", "modules"], [44, 3, 1, "", "named_buffers"], [44, 3, 1, "", "named_children"], [44, 3, 1, "", "named_modules"], [44, 3, 1, "", "named_parameters"], [44, 3, 1, "", "parameters"], [44, 3, 1, "", "register_backward_hook"], [44, 3, 1, "", "register_buffer"], [44, 3, 1, "", "register_forward_hook"], [44, 3, 1, "", "register_forward_pre_hook"], [44, 3, 1, "", "register_full_backward_hook"], [44, 3, 1, "", "register_full_backward_pre_hook"], [44, 3, 1, "", "register_load_state_dict_post_hook"], [44, 3, 1, "", "register_module"], [44, 3, 1, "", "register_parameter"], [44, 3, 1, "", "register_state_dict_pre_hook"], [44, 3, 1, "", "requires_grad_"], [44, 3, 1, "", "set_extra_state"], [44, 3, 1, "", "share_memory"], [44, 3, 1, "", "state_dict"], [44, 3, 1, "", "to"], [44, 3, 1, "", "to_empty"], [44, 3, 1, "", "train"], [44, 6, 1, "", "training"], [44, 3, 1, "", "type"], [44, 3, 1, "", "xpu"], [44, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[45, 1, 1, "", "display_issues"], [45, 1, 1, "", "find_label_issues"], [45, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[46, 1, 1, "", "find_label_issues"], [46, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [46, 1, 1, "", "find_predicted_neq_given"], [46, 7, 1, "", "pred_probs_by_class"], [46, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[48, 0, 0, "-", "label_quality_utils"], [49, 0, 0, "-", "latent_algebra"], [50, 0, 0, "-", "multiannotator_utils"], [51, 0, 0, "-", "multilabel_scorer"], [52, 0, 0, "-", "multilabel_utils"], [53, 0, 0, "-", "neighbor"], [57, 0, 0, "-", "outlier"], [58, 0, 0, "-", "token_classification_utils"], [59, 0, 0, "-", "util"], [60, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[48, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, 1, 1, "", "compute_inv_noise_matrix"], [49, 1, 1, "", "compute_noise_matrix_from_inverse"], [49, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [49, 1, 1, "", "compute_py"], [49, 1, 1, "", "compute_py_inv_noise_matrix"], [49, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[50, 1, 1, "", "assert_valid_inputs_multiannotator"], [50, 1, 1, "", "assert_valid_pred_probs"], [50, 1, 1, "", "check_consensus_label_classes"], [50, 1, 1, "", "compute_soft_cross_entropy"], [50, 1, 1, "", "find_best_temp_scaler"], [50, 1, 1, "", "format_multiannotator_labels"], [50, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[51, 2, 1, "", "Aggregator"], [51, 2, 1, "", "ClassLabelScorer"], [51, 2, 1, "", "MultilabelScorer"], [51, 1, 1, "", "exponential_moving_average"], [51, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [51, 1, 1, "", "get_label_quality_scores"], [51, 1, 1, "", "multilabel_py"], [51, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[51, 3, 1, "", "__call__"], [51, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[51, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [51, 6, 1, "", "NORMALIZED_MARGIN"], [51, 6, 1, "", "SELF_CONFIDENCE"], [51, 3, 1, "", "__call__"], [51, 3, 1, "", "__contains__"], [51, 3, 1, "", "__getitem__"], [51, 3, 1, "", "__iter__"], [51, 3, 1, "", "__len__"], [51, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[51, 3, 1, "", "__call__"], [51, 3, 1, "", "aggregate"], [51, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[52, 1, 1, "", "get_onehot_num_classes"], [52, 1, 1, "", "int2onehot"], [52, 1, 1, "", "onehot2int"], [52, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[54, 0, 0, "-", "knn_graph"], [55, 0, 0, "-", "metric"], [56, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[54, 7, 1, "", "DEFAULT_K"], [54, 1, 1, "", "construct_knn_graph_from_index"], [54, 1, 1, "", "correct_knn_distances_and_indices"], [54, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [54, 1, 1, "", "correct_knn_graph"], [54, 1, 1, "", "create_knn_graph_and_index"], [54, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[55, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [55, 7, 1, "", "ROW_COUNT_CUTOFF"], [55, 1, 1, "", "decide_default_metric"], [55, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[57, 1, 1, "", "correct_precision_errors"], [57, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, 1, 1, "", "color_sentence"], [58, 1, 1, "", "filter_sentence"], [58, 1, 1, "", "get_sentence"], [58, 1, 1, "", "mapping"], [58, 1, 1, "", "merge_probs"], [58, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[59, 1, 1, "", "append_extra_datapoint"], [59, 1, 1, "", "clip_noise_rates"], [59, 1, 1, "", "clip_values"], [59, 1, 1, "", "compress_int_array"], [59, 1, 1, "", "confusion_matrix"], [59, 1, 1, "", "csr_vstack"], [59, 1, 1, "", "estimate_pu_f1"], [59, 1, 1, "", "extract_indices_tf"], [59, 1, 1, "", "force_two_dimensions"], [59, 1, 1, "", "format_labels"], [59, 1, 1, "", "get_missing_classes"], [59, 1, 1, "", "get_num_classes"], [59, 1, 1, "", "get_unique_classes"], [59, 1, 1, "", "is_tensorflow_dataset"], [59, 1, 1, "", "is_torch_dataset"], [59, 1, 1, "", "num_unique_classes"], [59, 1, 1, "", "print_inverse_noise_matrix"], [59, 1, 1, "", "print_joint_matrix"], [59, 1, 1, "", "print_noise_matrix"], [59, 1, 1, "", "print_square_matrix"], [59, 1, 1, "", "remove_noise_from_class"], [59, 1, 1, "", "round_preserving_row_totals"], [59, 1, 1, "", "round_preserving_sum"], [59, 1, 1, "", "smart_display_dataframe"], [59, 1, 1, "", "subset_X_y"], [59, 1, 1, "", "subset_data"], [59, 1, 1, "", "subset_labels"], [59, 1, 1, "", "train_val_split"], [59, 1, 1, "", "unshuffle_tensorflow_dataset"], [59, 1, 1, "", "value_counts"], [59, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[60, 1, 1, "", "assert_indexing_works"], [60, 1, 1, "", "assert_nonempty_input"], [60, 1, 1, "", "assert_valid_class_labels"], [60, 1, 1, "", "assert_valid_inputs"], [60, 1, 1, "", "labels_to_array"], [60, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[62, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[62, 2, 1, "", "KerasWrapperModel"], [62, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[63, 1, 1, "", "convert_long_to_wide_dataset"], [63, 1, 1, "", "get_active_learning_scores"], [63, 1, 1, "", "get_active_learning_scores_ensemble"], [63, 1, 1, "", "get_label_quality_multiannotator"], [63, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [63, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[64, 0, 0, "-", "dataset"], [65, 0, 0, "-", "filter"], [67, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[64, 1, 1, "", "common_multilabel_issues"], [64, 1, 1, "", "multilabel_health_summary"], [64, 1, 1, "", "overall_multilabel_health_score"], [64, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, 1, 1, "", "find_label_issues"], [65, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[67, 1, 1, "", "get_label_quality_scores"], [67, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[68, 0, 0, "-", "filter"], [70, 0, 0, "-", "rank"], [71, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[68, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[70, 1, 1, "", "compute_badloc_box_scores"], [70, 1, 1, "", "compute_overlooked_box_scores"], [70, 1, 1, "", "compute_swap_box_scores"], [70, 1, 1, "", "get_label_quality_scores"], [70, 1, 1, "", "issues_from_scores"], [70, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[71, 1, 1, "", "bounding_box_size_distribution"], [71, 1, 1, "", "calculate_per_class_metrics"], [71, 1, 1, "", "class_label_distribution"], [71, 1, 1, "", "get_average_per_class_confusion_matrix"], [71, 1, 1, "", "get_sorted_bbox_count_idxs"], [71, 1, 1, "", "object_counts_per_image"], [71, 1, 1, "", "plot_class_distribution"], [71, 1, 1, "", "plot_class_size_distributions"], [71, 1, 1, "", "visualize"]], "cleanlab.outlier": [[72, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[72, 3, 1, "", "fit"], [72, 3, 1, "", "fit_score"], [72, 3, 1, "", "score"]], "cleanlab.rank": [[73, 1, 1, "", "find_top_issues"], [73, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [73, 1, 1, "", "get_label_quality_ensemble_scores"], [73, 1, 1, "", "get_label_quality_scores"], [73, 1, 1, "", "get_normalized_margin_for_each_label"], [73, 1, 1, "", "get_self_confidence_for_each_label"], [73, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[75, 0, 0, "-", "learn"], [76, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[75, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[75, 3, 1, "", "__init_subclass__"], [75, 3, 1, "", "find_label_issues"], [75, 3, 1, "", "fit"], [75, 3, 1, "", "get_aleatoric_uncertainty"], [75, 3, 1, "", "get_epistemic_uncertainty"], [75, 3, 1, "", "get_label_issues"], [75, 3, 1, "", "get_metadata_routing"], [75, 3, 1, "", "get_params"], [75, 3, 1, "", "predict"], [75, 3, 1, "", "save_space"], [75, 3, 1, "", "score"], [75, 3, 1, "", "set_fit_request"], [75, 3, 1, "", "set_params"], [75, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[76, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[77, 0, 0, "-", "filter"], [79, 0, 0, "-", "rank"], [80, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[77, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[79, 1, 1, "", "get_label_quality_scores"], [79, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[80, 1, 1, "", "common_label_issues"], [80, 1, 1, "", "display_issues"], [80, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[81, 0, 0, "-", "filter"], [83, 0, 0, "-", "rank"], [84, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[81, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[83, 1, 1, "", "get_label_quality_scores"], [83, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[84, 1, 1, "", "common_label_issues"], [84, 1, 1, "", "display_issues"], [84, 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, 88, 89, 93, 95, 96, 99, 101, 104, 110], "count": [3, 101], "data_valu": [4, 21], "datalab": [5, 7, 9, 10, 12, 90, 91, 92, 93, 94, 95, 96, 97, 99, 101, 104], "creat": [7, 91, 92, 101, 103], "your": [7, 85, 91, 92, 96, 97, 99, 101], "own": 7, "issu": [7, 9, 10, 24, 33, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "manag": [7, 24], "prerequisit": 7, "implement": 7, "issuemanag": [7, 91], "basic": 7, "check": [7, 85, 97, 100], "intermedi": 7, "advanc": [7, 91], "us": [7, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "gener": [8, 97], "cluster": [8, 97, 99], "id": 8, "guid": [9, 12], "type": [9, 10, 101], "custom": [9, 91], "cleanlab": [9, 10, 85, 88, 89, 90, 93, 95, 96, 99, 101, 103, 104, 105, 106, 108, 109, 110], "studio": [9, 10], "easi": [9, 10, 85, 93], "mode": [9, 10, 85, 93], "can": [10, 92, 98, 99, 101, 103], "detect": [10, 90, 92, 93, 95, 96, 97, 99, 101, 105, 106], "estim": [10, 101, 103, 104], "each": 10, "input": 10, "label": [10, 26, 28, 33, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 101, 103, 104, 105, 108, 109, 110], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 31, 57, 72, 93, 95, 96, 104, 106], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 92, 93, 95, 96], "duplic": [10, 22, 92, 93, 95, 96, 99, 104], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 96, 97], "iid": [10, 96, 97], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 86, 97, 101, 109], "imbal": [10, 23, 97], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 93, 97, 106], "specif": [10, 24, 109], "spuriou": [10, 97], "correl": [10, 97], "between": 10, "properti": 10, "score": [10, 97, 101, 103, 104, 105, 109, 110], "underperform": [10, 97, 99], "group": [10, 97, 99], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 30, 97], "is_null_issu": 10, "null_scor": 10, "data": [10, 15, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "valuat": [10, 97], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 97], "paramet": [10, 101], "get": [12, 91, 92, 103, 104, 105, 109, 110], "start": [12, 98], "api": 12, "refer": 12, "imagelab": 13, "adapt": 14, "data_issu": 16, "factori": 17, "intern": [18, 47], "issue_find": 19, "issue_manag": [24, 25], "regist": 24, "ml": [24, 99, 100, 101], "task": [24, 37], "multilabel": 27, "noniid": 29, "regress": [32, 74, 75, 76, 99, 108], "prioriti": 33, "order": 33, "find": [33, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "underperforming_group": 34, "model_output": 35, "report": [36, 93], "dataset": [39, 64, 85, 89, 90, 92, 93, 96, 97, 98, 99, 101, 104, 105, 106, 108, 109, 110], "cifar_cnn": 40, "coteach": 41, "experiment": 42, "label_issues_batch": 43, "mnist_pytorch": 44, "span_classif": 45, "filter": [46, 65, 68, 77, 81, 101], "label_quality_util": 48, "latent_algebra": 49, "multiannotator_util": 50, "multilabel_scor": 51, "multilabel_util": 52, "neighbor": 53, "knn_graph": 54, "metric": 55, "search": [56, 91], "token_classification_util": 58, "util": 59, "valid": [60, 93, 107], "model": [61, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108], "kera": 62, "multiannot": [63, 103], "multilabel_classif": 66, "rank": [67, 70, 73, 76, 79, 83, 101], "object_detect": 69, "summari": [71, 80, 84], "learn": [75, 92, 99, 101], "segment": [78, 109], "token_classif": [82, 110], "open": [85, 99], "sourc": [85, 99], "document": 85, "quickstart": 85, "1": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "instal": [85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "2": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [85, 92, 101], "sort": [85, 97], "3": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "handl": [85, 99], "error": [85, 89, 93, 99, 101, 103, 104, 105, 108, 109, 110], "train": [85, 88, 89, 90, 97, 99, 100, 106, 108], "robust": [85, 88, 89, 101, 108], "noisi": [85, 88, 89, 100, 101, 108], "4": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 105, 106, 108], "curat": [85, 100], "fix": [85, 99], "level": [85, 98, 101, 110], "5": [85, 88, 90, 92, 93, 95, 97, 100, 101, 103, 108], "improv": [85, 100, 103], "via": [85, 100, 101, 103], "mani": [85, 101], "other": [85, 103, 105, 108], "techniqu": [85, 100], "contribut": 85, "how": [86, 99, 101, 103, 104, 110], "migrat": 86, "version": 86, "0": 86, "from": [86, 88, 89, 91, 92, 100, 101, 108], "pre": [86, 90, 97, 99, 106], "function": [86, 91], "name": 86, "chang": 86, "modul": [86, 101], "new": 86, "remov": 86, "common": [86, 110], "argument": [86, 91], "variabl": 86, "cleanlearn": [87, 99, 101], "tutori": [87, 94, 98, 100, 102], "structur": 88, "tabular": [88, 95], "requir": [88, 89, 91, 92, 93, 95, 96, 103, 104, 105, 106, 108, 109, 110], "depend": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "load": [88, 89, 90, 91, 92, 95, 96, 97, 108], "process": [88, 95, 106, 108], "select": [88, 95], "comput": [88, 90, 93, 95, 96, 97, 99, 100, 103, 107], "out": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "sampl": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "predict": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 107], "probabl": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 107], "more": [88, 89, 92, 101, 108], "spend": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "too": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "much": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "time": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "qualiti": [88, 89, 92, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108, 109, 110], "text": [89, 96, 97, 110], "format": [89, 96, 99, 104, 105], "defin": [89, 93, 96, 97, 108], "potenti": [89, 103, 108], "an": [90, 93, 99], "audio": 90, "import": [90, 91, 92, 93, 98, 101, 103], "them": [90, 98, 100, 101], "speechbrain": 90, "featur": [90, 93, 106], "fit": 90, "linear": 90, "workflow": [91, 97, 101], "audit": [91, 92], "classifi": [91, 92, 97], "instanti": 91, "object": [91, 105], "increment": 91, "specifi": [91, 99], "nondefault": 91, "save": 91, "ad": 91, "A": 92, "unifi": 92, "kind": [92, 105], "skip": [92, 98, 101, 103], "detail": [92, 98, 101, 103], "about": 92, "addit": 92, "inform": [92, 93], "fetch": [93, 98], "normal": 93, "fashion": 93, "mnist": 93, "prepar": [93, 97], "k": [93, 95, 107], "fold": [93, 107], "cross": [93, 107], "embed": [93, 106], "7": [93, 100, 101], "view": 93, "most": [93, 110], "like": 93, "exampl": [93, 99, 101, 106], "sever": 93, "set": [93, 101], "dark": 93, "top": [93, 109], "low": 93, "numer": 95, "categor": [95, 97], "column": 95, "construct": 95, "nearest": 95, "neighbour": 95, "graph": [95, 97], "drift": [96, 104], "miscellan": 97, "acceler": 97, "knn": 97, "obtain": 97, "identifi": [97, 99, 100, 105], "explan": 97, "vector": 97, "perform": [97, 100], "visual": [97, 101, 105, 106, 109], "synthet": 97, "result": 97, "predefin": 97, "slice": [97, 99], "i": [97, 99, 101, 107], "catch": 97, "valu": 97, "encod": 97, "initi": [97, 103], "6": [97, 100, 101], "run": [97, 99], "analysi": [97, 105], "interpret": 97, "understand": 98, "evalu": [98, 100], "health": [98, 101], "8": [98, 100, 101], "popular": 98, "faq": 99, "what": [99, 101, 107], "do": [99, 101], "infer": 99, "correct": [99, 100], "ha": 99, "flag": 99, "should": 99, "v": [99, 100], "test": [99, 100, 101, 106], "big": 99, "limit": 99, "memori": 99, "why": [99, 100], "isn": 99, "t": 99, "work": [99, 101, 103, 110], "me": 99, "differ": [99, 105], "clean": [99, 100, 101], "final": 99, "hyperparamet": [99, 100], "tune": 99, "onli": 99, "one": [99, 101, 104, 109], "doe": [99, 103, 110], "take": 99, "so": 99, "long": 99, "when": [99, 101], "licens": 99, "under": 99, "answer": 99, "question": 99, "split": 100, "did": 100, "you": [100, 101], "make": 100, "thi": [100, 101], "preprocess": 100, "fundament": 100, "problem": 100, "setup": 100, "origin": 100, "baselin": 100, "manual": 100, "address": 100, "algorithm": 100, "better": [100, 103], "strategi": 100, "optim": 100, "9": 100, "conclus": 100, "The": 101, "centric": 101, "ai": 101, "machin": 101, "find_label_issu": 101, "line": 101, "code": 101, "twenti": 101, "lowest": 101, "see": 101, "now": 101, "let": 101, "": 101, "happen": 101, "we": 101, "merg": 101, "seafoam": 101, "green": 101, "yellow": 101, "re": 101, "One": 101, "rule": 101, "overal": [101, 109], "accur": 101, "directli": 101, "fulli": 101, "character": 101, "nois": 101, "matrix": [101, 104], "joint": 101, "prior": 101, "true": 101, "distribut": 101, "flip": 101, "rate": 101, "ani": 101, "again": 101, "support": 101, "lot": 101, "method": 101, "filter_bi": 101, "automat": 101, "everi": 101, "uniqu": 101, "num_label_issu": 101, "threshold": 101, "found": 101, "Not": 101, "sure": 101, "ensembl": 101, "multipl": [101, 103], "predictor": 101, "consensu": 103, "annot": 103, "major": 103, "vote": 103, "statist": 103, "compar": 103, "inspect": 103, "retrain": 103, "further": 103, "multi": 104, "beyond": 104, "mislabel": [104, 109, 110], "given": 104, "hot": 104, "binari": 104, "without": 104, "applic": 104, "real": 104, "download": [105, 109, 110], "objectlab": 105, "exploratori": 105, "pytorch": 106, "timm": 106, "cifar10": 106, "some": 106, "pred_prob": [106, 109, 110], "wai": 108, "semant": 109, "which": 109, "ar": 109, "commonli": 109, "focus": 109, "token": 110, "word": 110, "sentenc": 110, "contain": 110, "particular": 110}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 58}, "alltitles": {"benchmarking": [[0, "module-cleanlab.benchmarking"]], "noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [21, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Spurious Correlations between image-specific properties and labels": [[10, "spurious-correlations-between-image-specific-properties-and-labels"]], "property": [[10, "property"]], "score": [[10, "score"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Spurious Correlations Issue Parameters": [[10, "spurious-correlations-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "adapter": [[14, "adapter"]], "data": [[15, "module-cleanlab.datalab.internal.data"]], "data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[18, "internal"], [47, "internal"]], "issue_finder": [[19, "issue-finder"]], "duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[24, "issue-manager"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[24, "registered-issue-managers"]], "ML task-specific issue managers": [[24, "ml-task-specific-issue-managers"]], "label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[27, "multilabel"]], "noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[30, "null"]], "outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [57, "module-cleanlab.internal.outlier"], [72, "module-cleanlab.outlier"]], "regression": [[32, "regression"], [74, "regression"]], "Priority Order for finding issues:": [[33, null]], "underperforming_group": [[34, "underperforming-group"]], "model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[36, "report"]], "task": [[37, "task"]], "dataset": [[39, "module-cleanlab.dataset"], [64, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "experimental": [[42, "experimental"]], "label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "filter": [[46, "module-cleanlab.filter"], [65, "module-cleanlab.multilabel_classification.filter"], [68, "filter"], [77, "filter"], [81, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[53, "neighbor"]], "knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "search": [[56, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "util": [[59, "module-cleanlab.internal.util"]], "validation": [[60, "module-cleanlab.internal.validation"]], "models": [[61, "models"]], "keras": [[62, "module-cleanlab.models.keras"]], "multiannotator": [[63, "module-cleanlab.multiannotator"]], "multilabel_classification": [[66, "multilabel-classification"]], "rank": [[67, "module-cleanlab.multilabel_classification.rank"], [70, "module-cleanlab.object_detection.rank"], [73, "module-cleanlab.rank"], [79, "module-cleanlab.segmentation.rank"], [83, "module-cleanlab.token_classification.rank"]], "object_detection": [[69, "object-detection"]], "summary": [[71, "summary"], [80, "module-cleanlab.segmentation.summary"], [84, "module-cleanlab.token_classification.summary"]], "regression.learn": [[75, "module-cleanlab.regression.learn"]], "regression.rank": [[76, "module-cleanlab.regression.rank"]], "segmentation": [[78, "segmentation"]], "token_classification": [[82, "token-classification"]], "cleanlab open-source documentation": [[85, "cleanlab-open-source-documentation"]], "Quickstart": [[85, "quickstart"]], "1. Install cleanlab": [[85, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[85, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[85, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[85, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[85, "improve-your-data-via-many-other-techniques"]], "Contributing": [[85, "contributing"]], "Easy Mode": [[85, "easy-mode"], [93, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[86, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[86, "function-and-class-name-changes"]], "Module name changes": [[86, "module-name-changes"]], "New modules": [[86, "new-modules"]], "Removed modules": [[86, "removed-modules"]], "Common argument and variable name changes": [[86, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[87, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[88, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[88, "1.-Install-required-dependencies"], [89, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [96, "1.-Install-required-dependencies"], [108, "1.-Install-required-dependencies"]], "2. Load and process the data": [[88, "2.-Load-and-process-the-data"], [95, "2.-Load-and-process-the-data"], [108, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[88, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [95, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[88, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[88, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[88, "Spending-too-much-time-on-data-quality?"], [89, "Spending-too-much-time-on-data-quality?"], [92, "Spending-too-much-time-on-data-quality?"], [95, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [98, "Spending-too-much-time-on-data-quality?"], [101, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [106, "Spending-too-much-time-on-data-quality?"], [107, "spending-too-much-time-on-data-quality"], [108, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[89, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[89, "2.-Load-and-format-the-text-dataset"], [96, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[89, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[89, "4.-Train-a-more-robust-model-from-noisy-labels"], [108, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[90, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[90, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[90, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[90, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[90, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[90, "5.-Use-cleanlab-to-find-label-issues"], [95, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[91, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[91, "Install-and-import-required-dependencies"]], "Create and load the data": [[91, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[91, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[91, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[91, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[91, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[91, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[91, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[92, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[92, "1.-Install-and-import-required-dependencies"], [93, "1.-Install-and-import-required-dependencies"], [103, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[92, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[92, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[92, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[92, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[92, "Get-additional-information"]], "Near duplicate issues": [[92, "Near-duplicate-issues"], [93, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[93, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[93, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[93, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[93, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[93, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[93, "7.-Use-cleanlab-to-find-issues"]], "View report": [[93, "View-report"]], "Label issues": [[93, "Label-issues"], [95, "Label-issues"], [96, "Label-issues"]], "View most likely examples with label errors": [[93, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[93, "Outlier-issues"], [95, "Outlier-issues"], [96, "Outlier-issues"]], "View most severe outliers": [[93, "View-most-severe-outliers"]], "View sets of near duplicate images": [[93, "View-sets-of-near-duplicate-images"]], "Dark images": [[93, "Dark-images"]], "View top examples of dark images": [[93, "View-top-examples-of-dark-images"]], "Low information images": [[93, "Low-information-images"]], "Datalab Tutorials": [[94, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[95, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[95, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[95, "Near-duplicate-issues"], [96, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[96, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[96, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[96, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[96, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[97, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[97, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[97, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[97, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[97, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[97, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[97, "Explanation:"]], "Data Valuation": [[97, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[97, "1.-Load-and-Prepare-the-Dataset"], [97, "id2"], [97, "id5"]], "2. Vectorize the Text Data": [[97, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[97, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[97, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[97, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[97, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[97, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [97, "id3"]], "3. (Optional) Cluster the Data": [[97, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[97, "4.-Identify-Underperforming-Groups-with-Datalab"], [97, "id4"]], "5. (Optional) Visualize the Results": [[97, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[97, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[97, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[97, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[97, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[97, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[97, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[97, "1.-Load-the-Dataset"], [97, "id8"]], "2: Encode Categorical Values": [[97, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[97, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[97, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[97, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[97, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[97, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[97, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[97, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[97, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[97, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[97, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[97, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[98, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[98, "Install-dependencies-and-import-them"], [101, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[98, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[98, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[99, "FAQ"]], "What data can cleanlab detect issues in?": [[99, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[99, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[99, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[99, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[99, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[99, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[99, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[99, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[99, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[99, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[99, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[99, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[99, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[99, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[100, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[100, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[100, "1.-Install-dependencies"]], "2. Preprocess the data": [[100, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[100, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[100, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[100, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[100, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[100, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[100, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[100, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[100, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[101, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[101, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[101, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[101, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[101, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[101, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[101, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[101, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[101, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[101, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[101, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[101, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[101, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[101, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[101, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[101, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[101, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[101, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[101, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[101, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[101, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[101, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[102, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[103, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[103, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[103, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[103, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[103, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[103, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[103, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[103, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[103, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[104, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[104, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[104, "2.-Format-data,-labels,-and-model-predictions"], [105, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[104, "3.-Use-cleanlab-to-find-label-issues"], [105, "3.-Use-cleanlab-to-find-label-issues"], [109, "3.-Use-cleanlab-to-find-label-issues"], [110, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[104, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[104, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[104, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[104, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[104, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[105, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[105, "1.-Install-required-dependencies-and-download-data"], [109, "1.-Install-required-dependencies-and-download-data"], [110, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[105, "Get-label-quality-scores"], [109, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[105, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[105, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[105, "Other-uses-of-visualize"]], "Exploratory data analysis": [[105, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[106, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[106, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[106, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[106, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[106, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[106, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[107, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[107, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[107, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[108, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[108, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[108, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[109, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[109, "2.-Get-data,-labels,-and-pred_probs"], [110, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[109, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[109, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[109, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[110, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[110, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[110, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[110, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[110, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.data_valuation"], [5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"], [13, "module-cleanlab.datalab.internal.adapter.imagelab"], [15, "module-cleanlab.datalab.internal.data"], [16, "module-cleanlab.datalab.internal.data_issues"], [17, "module-cleanlab.datalab.internal.issue_manager_factory"], [18, "module-cleanlab.datalab.internal"], [19, "module-cleanlab.datalab.internal.issue_finder"], [21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [22, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [23, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [29, "module-cleanlab.datalab.internal.issue_manager.noniid"], [30, "module-cleanlab.datalab.internal.issue_manager.null"], [31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [35, "module-cleanlab.datalab.internal.model_outputs"], [36, "module-cleanlab.datalab.internal.report"], [37, "module-cleanlab.datalab.internal.task"], [39, "module-cleanlab.dataset"], [40, "module-cleanlab.experimental.cifar_cnn"], [41, "module-cleanlab.experimental.coteaching"], [42, "module-cleanlab.experimental"], [43, "module-cleanlab.experimental.label_issues_batched"], [44, "module-cleanlab.experimental.mnist_pytorch"], [45, "module-cleanlab.experimental.span_classification"], [46, "module-cleanlab.filter"], [47, "module-cleanlab.internal"], [48, "module-cleanlab.internal.label_quality_utils"], [49, "module-cleanlab.internal.latent_algebra"], [50, "module-cleanlab.internal.multiannotator_utils"], [51, "module-cleanlab.internal.multilabel_scorer"], [52, "module-cleanlab.internal.multilabel_utils"], [53, "module-cleanlab.internal.neighbor"], [54, "module-cleanlab.internal.neighbor.knn_graph"], [55, "module-cleanlab.internal.neighbor.metric"], [56, "module-cleanlab.internal.neighbor.search"], [57, "module-cleanlab.internal.outlier"], [58, "module-cleanlab.internal.token_classification_utils"], [59, "module-cleanlab.internal.util"], [60, "module-cleanlab.internal.validation"], [61, "module-cleanlab.models"], [62, "module-cleanlab.models.keras"], [63, "module-cleanlab.multiannotator"], [64, "module-cleanlab.multilabel_classification.dataset"], [65, "module-cleanlab.multilabel_classification.filter"], [66, "module-cleanlab.multilabel_classification"], [67, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.filter"], [69, "module-cleanlab.object_detection"], [70, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.object_detection.summary"], [72, "module-cleanlab.outlier"], [73, "module-cleanlab.rank"], [74, "module-cleanlab.regression"], [75, "module-cleanlab.regression.learn"], [76, "module-cleanlab.regression.rank"], [77, "module-cleanlab.segmentation.filter"], [78, "module-cleanlab.segmentation"], [79, "module-cleanlab.segmentation.rank"], [80, "module-cleanlab.segmentation.summary"], [81, "module-cleanlab.token_classification.filter"], [82, "module-cleanlab.token_classification"], [83, "module-cleanlab.token_classification.rank"], [84, "module-cleanlab.token_classification.summary"]], "cleanlab.benchmarking.noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_n_rand_probabilities_that_sum_to_m"]], "generate_noise_matrix_from_trace() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noise_matrix_from_trace"]], "generate_noisy_labels() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noisy_labels"]], "noise_matrix_is_valid() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.noise_matrix_is_valid"]], "randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_params"]], "predict() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict"]], "predict_proba() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict_proba"]], "save_space() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.save_space"]], "score() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.score"]], "set_fit_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_fit_request"]], "set_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_params"]], "set_score_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_score_request"]], "calibrate_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.calibrate_confident_joint"]], "cleanlab.count": [[3, "module-cleanlab.count"]], "compute_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.compute_confident_joint"]], "estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_confident_joint_and_cv_pred_proba"]], "estimate_cv_predicted_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_cv_predicted_probabilities"]], "estimate_joint() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_joint"]], "estimate_latent() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_latent"]], "estimate_noise_matrices() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_noise_matrices"]], "estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_and_noise_matrices_from_probabilities"]], "estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba"]], "get_confident_thresholds() (in module cleanlab.count)": [[3, "cleanlab.count.get_confident_thresholds"]], "num_label_issues() (in module cleanlab.count)": [[3, "cleanlab.count.num_label_issues"]], "cleanlab.data_valuation": [[4, "module-cleanlab.data_valuation"]], "data_shapley_knn() (in module cleanlab.data_valuation)": [[4, "cleanlab.data_valuation.data_shapley_knn"]], "datalab (class in cleanlab.datalab.datalab)": [[5, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[5, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[5, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[12, "module-cleanlab.datalab"]], "correlationreporter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter"]], "correlationvisualizer (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer"]], "imagelabdataissuesadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter"]], "imagelabissuefinderadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter"]], "imagelabreporteradapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_statistics"]], "create_imagelab() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.create_imagelab"]], "filter_based_on_max_prevalence() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.filter_based_on_max_prevalence"]], "find_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.get_available_issue_types"]], "get_info() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issues"]], "get_report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.get_report"]], "handle_spurious_correlations() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.handle_spurious_correlations"]], "report() (cleanlab.datalab.internal.adapter.imagelab.correlationreporter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter.report"]], "report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.report"]], "set_health_score() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.set_health_score"]], "statistics (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter property)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.statistics"]], "visualize() (cleanlab.datalab.internal.adapter.imagelab.correlationvisualizer method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer.visualize"]], "data (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[15, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[15, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[15, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[15, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[15, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[18, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[19, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[30, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_underperforming_clusters() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_underperforming_clusters"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[36, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[36, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[37, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[37, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[39, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.forward"], [40, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[42, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [44, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [44, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [44, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[46, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[46, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[46, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[47, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[48, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[53, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[56, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[57, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[59, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[60, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[61, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[62, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[63, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[64, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[66, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[67, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[68, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[68, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[69, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[70, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[71, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[72, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[72, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[73, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[74, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[75, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[75, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[75, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[76, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[76, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[77, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[77, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[78, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[79, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[80, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[81, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[81, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[82, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[83, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[84, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.filter_by_token"]]}}) \ No newline at end of file diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb index 1310c3072..4b42f243a 100644 --- a/master/tutorials/clean_learning/tabular.ipynb +++ b/master/tutorials/clean_learning/tabular.ipynb @@ -113,10 +113,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:49.976999Z", - "iopub.status.busy": "2024-09-26T14:46:49.976816Z", - "iopub.status.idle": "2024-09-26T14:46:51.290105Z", - "shell.execute_reply": "2024-09-26T14:46:51.289537Z" + "iopub.execute_input": "2024-09-26T16:42:37.585924Z", + "iopub.status.busy": "2024-09-26T16:42:37.585514Z", + "iopub.status.idle": "2024-09-26T16:42:38.850555Z", + "shell.execute_reply": "2024-09-26T16:42:38.849972Z" }, "nbsphinx": "hidden" }, @@ -126,7 +126,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -151,10 +151,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:51.292353Z", - "iopub.status.busy": "2024-09-26T14:46:51.291898Z", - "iopub.status.idle": "2024-09-26T14:46:51.324181Z", - "shell.execute_reply": "2024-09-26T14:46:51.323699Z" + "iopub.execute_input": "2024-09-26T16:42:38.852815Z", + "iopub.status.busy": "2024-09-26T16:42:38.852365Z", + "iopub.status.idle": "2024-09-26T16:42:38.870694Z", + "shell.execute_reply": "2024-09-26T16:42:38.870234Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:51.326351Z", - "iopub.status.busy": "2024-09-26T14:46:51.325915Z", - "iopub.status.idle": "2024-09-26T14:46:51.516296Z", - "shell.execute_reply": "2024-09-26T14:46:51.515685Z" + "iopub.execute_input": "2024-09-26T16:42:38.872472Z", + "iopub.status.busy": "2024-09-26T16:42:38.872227Z", + "iopub.status.idle": "2024-09-26T16:42:39.043693Z", + "shell.execute_reply": "2024-09-26T16:42:39.043084Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:51.550190Z", - "iopub.status.busy": "2024-09-26T14:46:51.549718Z", - "iopub.status.idle": "2024-09-26T14:46:51.556194Z", - "shell.execute_reply": "2024-09-26T14:46:51.555704Z" + "iopub.execute_input": "2024-09-26T16:42:39.076643Z", + "iopub.status.busy": "2024-09-26T16:42:39.076424Z", + "iopub.status.idle": "2024-09-26T16:42:39.080314Z", + "shell.execute_reply": "2024-09-26T16:42:39.079868Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:51.558094Z", - "iopub.status.busy": "2024-09-26T14:46:51.557801Z", - "iopub.status.idle": "2024-09-26T14:46:51.566504Z", - "shell.execute_reply": "2024-09-26T14:46:51.566057Z" + "iopub.execute_input": "2024-09-26T16:42:39.081872Z", + "iopub.status.busy": "2024-09-26T16:42:39.081701Z", + "iopub.status.idle": "2024-09-26T16:42:39.089777Z", + "shell.execute_reply": "2024-09-26T16:42:39.089347Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:51.568613Z", - "iopub.status.busy": "2024-09-26T14:46:51.568153Z", - "iopub.status.idle": "2024-09-26T14:46:51.571064Z", - "shell.execute_reply": "2024-09-26T14:46:51.570501Z" + "iopub.execute_input": "2024-09-26T16:42:39.091408Z", + "iopub.status.busy": "2024-09-26T16:42:39.091229Z", + "iopub.status.idle": "2024-09-26T16:42:39.093970Z", + "shell.execute_reply": "2024-09-26T16:42:39.093386Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:51.573000Z", - "iopub.status.busy": "2024-09-26T14:46:51.572679Z", - "iopub.status.idle": "2024-09-26T14:46:52.105207Z", - "shell.execute_reply": "2024-09-26T14:46:52.104691Z" + "iopub.execute_input": "2024-09-26T16:42:39.095627Z", + "iopub.status.busy": "2024-09-26T16:42:39.095349Z", + "iopub.status.idle": "2024-09-26T16:42:39.616181Z", + "shell.execute_reply": "2024-09-26T16:42:39.615616Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:52.107319Z", - "iopub.status.busy": "2024-09-26T14:46:52.107018Z", - "iopub.status.idle": "2024-09-26T14:46:54.109749Z", - "shell.execute_reply": "2024-09-26T14:46:54.109008Z" + "iopub.execute_input": "2024-09-26T16:42:39.618433Z", + "iopub.status.busy": "2024-09-26T16:42:39.617868Z", + "iopub.status.idle": "2024-09-26T16:42:41.523858Z", + "shell.execute_reply": "2024-09-26T16:42:41.523238Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:54.112432Z", - "iopub.status.busy": "2024-09-26T14:46:54.111599Z", - "iopub.status.idle": "2024-09-26T14:46:54.122786Z", - "shell.execute_reply": "2024-09-26T14:46:54.122299Z" + "iopub.execute_input": "2024-09-26T16:42:41.526283Z", + "iopub.status.busy": "2024-09-26T16:42:41.525581Z", + "iopub.status.idle": "2024-09-26T16:42:41.536498Z", + "shell.execute_reply": "2024-09-26T16:42:41.535959Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:54.124636Z", - "iopub.status.busy": "2024-09-26T14:46:54.124305Z", - "iopub.status.idle": "2024-09-26T14:46:54.128711Z", - "shell.execute_reply": "2024-09-26T14:46:54.128162Z" + "iopub.execute_input": "2024-09-26T16:42:41.538355Z", + "iopub.status.busy": "2024-09-26T16:42:41.538022Z", + "iopub.status.idle": "2024-09-26T16:42:41.541867Z", + "shell.execute_reply": "2024-09-26T16:42:41.541434Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:54.130465Z", - "iopub.status.busy": "2024-09-26T14:46:54.130125Z", - "iopub.status.idle": "2024-09-26T14:46:54.138888Z", - "shell.execute_reply": "2024-09-26T14:46:54.138403Z" + "iopub.execute_input": "2024-09-26T16:42:41.543612Z", + "iopub.status.busy": "2024-09-26T16:42:41.543274Z", + "iopub.status.idle": "2024-09-26T16:42:41.550546Z", + "shell.execute_reply": "2024-09-26T16:42:41.550073Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:54.140677Z", - "iopub.status.busy": "2024-09-26T14:46:54.140309Z", - "iopub.status.idle": "2024-09-26T14:46:54.256974Z", - "shell.execute_reply": "2024-09-26T14:46:54.256488Z" + "iopub.execute_input": "2024-09-26T16:42:41.552204Z", + "iopub.status.busy": "2024-09-26T16:42:41.551875Z", + "iopub.status.idle": "2024-09-26T16:42:41.664888Z", + "shell.execute_reply": "2024-09-26T16:42:41.664398Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:54.258978Z", - "iopub.status.busy": "2024-09-26T14:46:54.258613Z", - "iopub.status.idle": "2024-09-26T14:46:54.261400Z", - "shell.execute_reply": "2024-09-26T14:46:54.260928Z" + "iopub.execute_input": "2024-09-26T16:42:41.666747Z", + "iopub.status.busy": "2024-09-26T16:42:41.666381Z", + "iopub.status.idle": "2024-09-26T16:42:41.669049Z", + "shell.execute_reply": "2024-09-26T16:42:41.668596Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:54.263212Z", - "iopub.status.busy": "2024-09-26T14:46:54.262873Z", - "iopub.status.idle": "2024-09-26T14:46:56.458045Z", - "shell.execute_reply": "2024-09-26T14:46:56.457352Z" + "iopub.execute_input": "2024-09-26T16:42:41.670858Z", + "iopub.status.busy": "2024-09-26T16:42:41.670529Z", + "iopub.status.idle": "2024-09-26T16:42:43.790609Z", + "shell.execute_reply": "2024-09-26T16:42:43.789898Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:56.460937Z", - "iopub.status.busy": "2024-09-26T14:46:56.459991Z", - "iopub.status.idle": "2024-09-26T14:46:56.471759Z", - "shell.execute_reply": "2024-09-26T14:46:56.471273Z" + "iopub.execute_input": "2024-09-26T16:42:43.793417Z", + "iopub.status.busy": "2024-09-26T16:42:43.792475Z", + "iopub.status.idle": "2024-09-26T16:42:43.803835Z", + "shell.execute_reply": "2024-09-26T16:42:43.803257Z" } }, "outputs": [ @@ -786,10 +786,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:46:56.473438Z", - "iopub.status.busy": "2024-09-26T14:46:56.473240Z", - "iopub.status.idle": "2024-09-26T14:46:56.529027Z", - "shell.execute_reply": "2024-09-26T14:46:56.528533Z" + "iopub.execute_input": "2024-09-26T16:42:43.805762Z", + "iopub.status.busy": "2024-09-26T16:42:43.805274Z", + "iopub.status.idle": "2024-09-26T16:42:43.846705Z", + "shell.execute_reply": "2024-09-26T16:42:43.846076Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html index e78e1c343..17ff9e845 100644 --- a/master/tutorials/clean_learning/text.html +++ b/master/tutorials/clean_learning/text.html @@ -821,7 +821,7 @@

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

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

@@ -884,43 +884,43 @@

2. Load and format the text dataset
-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -1223,7 +1223,7 @@

Spending too much time on data quality?Cleanlab Studio – an automated platform to find and fix issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. Try it for free!

The modern AI pipeline automated with Cleanlab Studio

diff --git a/master/tutorials/clean_learning/text.ipynb b/master/tutorials/clean_learning/text.ipynb index 81bd9574d..d7c538d0c 100644 --- a/master/tutorials/clean_learning/text.ipynb +++ b/master/tutorials/clean_learning/text.ipynb @@ -115,10 +115,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:00.005766Z", - "iopub.status.busy": "2024-09-26T14:47:00.005598Z", - "iopub.status.idle": "2024-09-26T14:47:03.458146Z", - "shell.execute_reply": "2024-09-26T14:47:03.457580Z" + "iopub.execute_input": "2024-09-26T16:42:47.118321Z", + "iopub.status.busy": "2024-09-26T16:42:47.118153Z", + "iopub.status.idle": "2024-09-26T16:42:50.268146Z", + "shell.execute_reply": "2024-09-26T16:42:50.267514Z" }, "nbsphinx": "hidden" }, @@ -135,7 +135,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -160,10 +160,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:03.460102Z", - "iopub.status.busy": "2024-09-26T14:47:03.459810Z", - "iopub.status.idle": "2024-09-26T14:47:03.463418Z", - "shell.execute_reply": "2024-09-26T14:47:03.462845Z" + "iopub.execute_input": "2024-09-26T16:42:50.270268Z", + "iopub.status.busy": "2024-09-26T16:42:50.269957Z", + "iopub.status.idle": "2024-09-26T16:42:50.273373Z", + "shell.execute_reply": "2024-09-26T16:42:50.272923Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:03.465142Z", - "iopub.status.busy": "2024-09-26T14:47:03.464800Z", - "iopub.status.idle": "2024-09-26T14:47:03.467949Z", - "shell.execute_reply": "2024-09-26T14:47:03.467483Z" + "iopub.execute_input": "2024-09-26T16:42:50.275041Z", + "iopub.status.busy": "2024-09-26T16:42:50.274739Z", + "iopub.status.idle": "2024-09-26T16:42:50.277871Z", + "shell.execute_reply": "2024-09-26T16:42:50.277333Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:03.469646Z", - "iopub.status.busy": "2024-09-26T14:47:03.469283Z", - "iopub.status.idle": "2024-09-26T14:47:03.521848Z", - "shell.execute_reply": "2024-09-26T14:47:03.521259Z" + "iopub.execute_input": "2024-09-26T16:42:50.279534Z", + "iopub.status.busy": "2024-09-26T16:42:50.279236Z", + "iopub.status.idle": "2024-09-26T16:42:50.324812Z", + "shell.execute_reply": "2024-09-26T16:42:50.324231Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:03.523805Z", - "iopub.status.busy": "2024-09-26T14:47:03.523447Z", - "iopub.status.idle": "2024-09-26T14:47:03.527108Z", - "shell.execute_reply": "2024-09-26T14:47:03.526668Z" + "iopub.execute_input": "2024-09-26T16:42:50.326583Z", + "iopub.status.busy": "2024-09-26T16:42:50.326170Z", + "iopub.status.idle": "2024-09-26T16:42:50.329897Z", + "shell.execute_reply": "2024-09-26T16:42:50.329322Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:03.528762Z", - "iopub.status.busy": "2024-09-26T14:47:03.528492Z", - "iopub.status.idle": "2024-09-26T14:47:03.532073Z", - "shell.execute_reply": "2024-09-26T14:47:03.531625Z" + "iopub.execute_input": "2024-09-26T16:42:50.331730Z", + "iopub.status.busy": "2024-09-26T16:42:50.331338Z", + "iopub.status.idle": "2024-09-26T16:42:50.334632Z", + "shell.execute_reply": "2024-09-26T16:42:50.334164Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'getting_spare_card', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'visa_or_mastercard', 'lost_or_stolen_phone', 'cancel_transfer', 'card_about_to_expire'}\n" + "Classes: {'change_pin', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'visa_or_mastercard', 'getting_spare_card', 'cancel_transfer', 'beneficiary_not_allowed', 'card_about_to_expire', 'apple_pay_or_google_pay'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:03.533776Z", - "iopub.status.busy": "2024-09-26T14:47:03.533438Z", - "iopub.status.idle": "2024-09-26T14:47:03.536702Z", - "shell.execute_reply": "2024-09-26T14:47:03.536252Z" + "iopub.execute_input": "2024-09-26T16:42:50.336403Z", + "iopub.status.busy": "2024-09-26T16:42:50.336025Z", + "iopub.status.idle": "2024-09-26T16:42:50.339039Z", + "shell.execute_reply": "2024-09-26T16:42:50.338589Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:03.538408Z", - "iopub.status.busy": "2024-09-26T14:47:03.538094Z", - "iopub.status.idle": "2024-09-26T14:47:03.541437Z", - "shell.execute_reply": "2024-09-26T14:47:03.540871Z" + "iopub.execute_input": "2024-09-26T16:42:50.340799Z", + "iopub.status.busy": "2024-09-26T16:42:50.340402Z", + "iopub.status.idle": "2024-09-26T16:42:50.343831Z", + "shell.execute_reply": "2024-09-26T16:42:50.343255Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:03.543307Z", - "iopub.status.busy": "2024-09-26T14:47:03.542863Z", - "iopub.status.idle": "2024-09-26T14:47:08.488107Z", - "shell.execute_reply": "2024-09-26T14:47:08.487533Z" + "iopub.execute_input": "2024-09-26T16:42:50.345657Z", + "iopub.status.busy": "2024-09-26T16:42:50.345275Z", + "iopub.status.idle": "2024-09-26T16:42:54.923218Z", + "shell.execute_reply": "2024-09-26T16:42:54.922557Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7bf569b1ec4240fbb7f1457722fe46c9", + "model_id": "c2643363bb89468b960a200c90333f96", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dc4eb1dc64da457a9d83b0bad4f4fd96", + "model_id": "3a1de9507a79467bacfdf470fe36ad10", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "47dd26560f0f4f14ae1d6235bf187f43", + "model_id": "e17be4756dc3431b86f73424c964e106", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6692a301895241f7894a3bace80aec4a", + "model_id": "cd2dc8a9dbe04b53b5e3e75f970e79ae", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21332e3c65394cf38141b89a7102833d", + "model_id": "3b99ec4020eb4d53ac9e59846c412d82", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "37b540a8453d4401b5a798e49297b5a2", + "model_id": "69e2a61b275146c580a41ab6348f7042", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c550a7da6dee4658a5e958b278220075", + "model_id": "fbb42843f43544bdbf56b7bdb0c00252", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:08.490505Z", - "iopub.status.busy": "2024-09-26T14:47:08.490089Z", - "iopub.status.idle": "2024-09-26T14:47:08.493151Z", - "shell.execute_reply": "2024-09-26T14:47:08.492644Z" + "iopub.execute_input": "2024-09-26T16:42:54.925475Z", + "iopub.status.busy": "2024-09-26T16:42:54.925287Z", + "iopub.status.idle": "2024-09-26T16:42:54.928184Z", + "shell.execute_reply": "2024-09-26T16:42:54.927606Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:08.494934Z", - "iopub.status.busy": "2024-09-26T14:47:08.494590Z", - "iopub.status.idle": "2024-09-26T14:47:08.497376Z", - "shell.execute_reply": "2024-09-26T14:47:08.496905Z" + "iopub.execute_input": "2024-09-26T16:42:54.929707Z", + "iopub.status.busy": "2024-09-26T16:42:54.929536Z", + "iopub.status.idle": "2024-09-26T16:42:54.932249Z", + "shell.execute_reply": "2024-09-26T16:42:54.931793Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:08.499043Z", - "iopub.status.busy": "2024-09-26T14:47:08.498709Z", - "iopub.status.idle": "2024-09-26T14:47:11.411424Z", - "shell.execute_reply": "2024-09-26T14:47:11.410600Z" + "iopub.execute_input": "2024-09-26T16:42:54.933917Z", + "iopub.status.busy": "2024-09-26T16:42:54.933642Z", + "iopub.status.idle": "2024-09-26T16:42:57.667235Z", + "shell.execute_reply": "2024-09-26T16:42:57.666453Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:11.414407Z", - "iopub.status.busy": "2024-09-26T14:47:11.413552Z", - "iopub.status.idle": "2024-09-26T14:47:11.421686Z", - "shell.execute_reply": "2024-09-26T14:47:11.421111Z" + "iopub.execute_input": "2024-09-26T16:42:57.669922Z", + "iopub.status.busy": "2024-09-26T16:42:57.669227Z", + "iopub.status.idle": "2024-09-26T16:42:57.677307Z", + "shell.execute_reply": "2024-09-26T16:42:57.676729Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:11.423755Z", - "iopub.status.busy": "2024-09-26T14:47:11.423300Z", - "iopub.status.idle": "2024-09-26T14:47:11.427791Z", - "shell.execute_reply": "2024-09-26T14:47:11.427280Z" + "iopub.execute_input": "2024-09-26T16:42:57.679199Z", + "iopub.status.busy": "2024-09-26T16:42:57.678870Z", + "iopub.status.idle": "2024-09-26T16:42:57.682906Z", + "shell.execute_reply": "2024-09-26T16:42:57.682293Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:11.429713Z", - "iopub.status.busy": "2024-09-26T14:47:11.429285Z", - "iopub.status.idle": "2024-09-26T14:47:11.432675Z", - "shell.execute_reply": "2024-09-26T14:47:11.432217Z" + "iopub.execute_input": "2024-09-26T16:42:57.684669Z", + "iopub.status.busy": "2024-09-26T16:42:57.684281Z", + "iopub.status.idle": "2024-09-26T16:42:57.687409Z", + "shell.execute_reply": "2024-09-26T16:42:57.686964Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:11.434490Z", - "iopub.status.busy": "2024-09-26T14:47:11.434156Z", - "iopub.status.idle": "2024-09-26T14:47:11.437228Z", - "shell.execute_reply": "2024-09-26T14:47:11.436765Z" + "iopub.execute_input": "2024-09-26T16:42:57.689213Z", + "iopub.status.busy": "2024-09-26T16:42:57.688829Z", + "iopub.status.idle": "2024-09-26T16:42:57.691980Z", + "shell.execute_reply": "2024-09-26T16:42:57.691405Z" } }, "outputs": [], @@ -852,10 +852,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:11.438815Z", - "iopub.status.busy": "2024-09-26T14:47:11.438635Z", - "iopub.status.idle": "2024-09-26T14:47:11.446087Z", - "shell.execute_reply": "2024-09-26T14:47:11.445615Z" + "iopub.execute_input": "2024-09-26T16:42:57.693567Z", + "iopub.status.busy": "2024-09-26T16:42:57.693274Z", + "iopub.status.idle": "2024-09-26T16:42:57.700464Z", + "shell.execute_reply": "2024-09-26T16:42:57.699903Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:11.447943Z", - "iopub.status.busy": "2024-09-26T14:47:11.447610Z", - "iopub.status.idle": "2024-09-26T14:47:11.721319Z", - "shell.execute_reply": "2024-09-26T14:47:11.720698Z" + "iopub.execute_input": "2024-09-26T16:42:57.702164Z", + "iopub.status.busy": "2024-09-26T16:42:57.701993Z", + "iopub.status.idle": "2024-09-26T16:42:57.930256Z", + "shell.execute_reply": "2024-09-26T16:42:57.929740Z" }, "scrolled": true }, @@ -1022,10 +1022,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:11.723702Z", - "iopub.status.busy": "2024-09-26T14:47:11.723282Z", - "iopub.status.idle": "2024-09-26T14:47:11.904646Z", - "shell.execute_reply": "2024-09-26T14:47:11.904102Z" + "iopub.execute_input": "2024-09-26T16:42:57.932337Z", + "iopub.status.busy": "2024-09-26T16:42:57.931950Z", + "iopub.status.idle": "2024-09-26T16:42:58.111474Z", + "shell.execute_reply": "2024-09-26T16:42:58.110932Z" }, "scrolled": true }, @@ -1073,10 +1073,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:11.906952Z", - "iopub.status.busy": "2024-09-26T14:47:11.906556Z", - "iopub.status.idle": "2024-09-26T14:47:11.910954Z", - "shell.execute_reply": "2024-09-26T14:47:11.910437Z" + "iopub.execute_input": "2024-09-26T16:42:58.113655Z", + "iopub.status.busy": "2024-09-26T16:42:58.113271Z", + "iopub.status.idle": "2024-09-26T16:42:58.117124Z", + "shell.execute_reply": "2024-09-26T16:42:58.116614Z" }, "nbsphinx": "hidden" }, @@ -1120,7 +1120,89 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "03473efde97f4db7aeb4bf94a8dcb6ac": { + "024c678365fb44c7af3b2c6a09d40eb3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_602fb306a03a4992b517e4ffc3a8945d", + "placeholder": "​", + "style": "IPY_MODEL_d3bd3aa0536e4888966db595d9e76f94", + "tabbable": null, + "tooltip": null, + "value": " 232k/232k [00:00<00:00, 2.97MB/s]" + } + }, + "030d4a7a49fe4c6a9ad80c7b5359c7ba": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_75a0dacc70d541dda27c4dd83c53445a", + "placeholder": "​", + "style": "IPY_MODEL_7af59be6dc3e43b09a1474a944823a7f", + "tabbable": null, + "tooltip": null, + "value": "vocab.txt: 100%" + } + }, + "03f9846cb1d14c1d9f4c1d101ab86e32": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "04048c2a4ea94eb0b16441d4e7efb2d9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "06b2f48afcd84d958179886e3ce3025d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1138,7 +1220,7 @@ "text_color": null } }, - "06bdd0de78584b4d8c963a129ebf2a90": { + "08f69468547b4774880dcce781d11225": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1191,23 +1273,7 @@ "width": null } }, - "076de96beb79463ab6936bf1bcf942f5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "0988aba1120e44e194591113d57b63d3": { + "0c1022f9dd1e4b90821853a519d82c85": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1260,7 +1326,7 @@ "width": null } }, - "09d4dbd7007b4cfda79c92844565bfd3": { + "0c7ae4ee46ee4bc0b896eef3fa94eaa3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1313,7 +1379,7 @@ "width": null } }, - "0a9bbe4ae4a7460aa182a6b390e2126a": { + "0f9f2396d1cc4fd38cdfbd7189900eeb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1366,30 +1432,65 @@ "width": null } }, - "0b20d7b224384162a625ab6b3d0f9b75": { + "0fb89d159b404add86c002f537763cc8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "130aabfb49f94f088bc47d302aef9d6a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "16ba14bab33347d3b2f7d63f5296038f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_159894c2bd95445e89e4038c12c6bb9f", - "placeholder": "​", - "style": "IPY_MODEL_cf82eb1ddad343378d886f2d760e8e19", + "layout": "IPY_MODEL_280139e612344c818922a9793c0c87e8", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0fb89d159b404add86c002f537763cc8", "tabbable": null, "tooltip": null, - "value": " 54.2M/54.2M [00:00<00:00, 95.5MB/s]" + "value": 391.0 } }, - "0bfe9f713e2d4b6ba3ef62402a269ffb": { + "1994d16f690b4a81afecab34748ae77d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1404,15 +1505,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ecf47058d5624d17a0cd5595cc9a9ea1", + "layout": "IPY_MODEL_debae1e29bd44145bfd9d09c776517b2", "placeholder": "​", - "style": "IPY_MODEL_5f805e6909fa4676b560df990b1225b3", + "style": "IPY_MODEL_04048c2a4ea94eb0b16441d4e7efb2d9", "tabbable": null, "tooltip": null, - "value": "pytorch_model.bin: 100%" + "value": " 665/665 [00:00<00:00, 114kB/s]" } }, - "12a2c923802042bbaf7afcb489317fc2": { + "1a82739465f642be9141dfa36c42789b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1427,31 +1528,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1fc253a07301484fbf34df123112adf2", + "layout": "IPY_MODEL_ab8ff183a42e48458e19db12e4aff633", "placeholder": "​", - "style": "IPY_MODEL_2745511db571407b909eadcf1041fbbb", + "style": "IPY_MODEL_641c75ba0e1341108bb4918b53677229", "tabbable": null, "tooltip": null, - "value": " 232k/232k [00:00<00:00, 33.8MB/s]" + "value": " 2.21k/2.21k [00:00<00:00, 373kB/s]" } }, - "13323479403a4c6ba63c87932eb6b7d1": { + "1db1f4745f7c41b09eb7ade25dd80789": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "159894c2bd95445e89e4038c12c6bb9f": { + "210251b0b4bd433994b27f5bfea877c0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1504,7 +1607,53 @@ "width": null } }, - "16a4915303e24ee999326b95f314a277": { + "2447903dfaee44b8a215d28fcd0894d0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9b3d4267e67742c7a50c68da4c077ce5", + "placeholder": "​", + "style": "IPY_MODEL_03f9846cb1d14c1d9f4c1d101ab86e32", + "tabbable": null, + "tooltip": null, + "value": "tokenizer_config.json: 100%" + } + }, + "264b7288d3c640769ff759e7d07ab524": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6afb2a72832a456c904eea5d20c8155f", + "placeholder": "​", + "style": "IPY_MODEL_9a897039093d4f668353b3c68a0d6f60", + "tabbable": null, + "tooltip": null, + "value": "tokenizer.json: 100%" + } + }, + "280139e612344c818922a9793c0c87e8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1557,7 +1706,7 @@ "width": null } }, - "17d77342577f4828b221978f61b1cca1": { + "2c525938ab3f40839e112d0514886621": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1610,7 +1759,55 @@ "width": null } }, - "18587e575408498d91cc167ce94971e8": { + "3a1de9507a79467bacfdf470fe36ad10": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_fc4190f3cdf54bc8a181cf578000ede5", + "IPY_MODEL_633ed034cdcc4522b350f94cca3f051b", + "IPY_MODEL_1a82739465f642be9141dfa36c42789b" + ], + "layout": "IPY_MODEL_08f69468547b4774880dcce781d11225", + "tabbable": null, + "tooltip": null + } + }, + "3b99ec4020eb4d53ac9e59846c412d82": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_264b7288d3c640769ff759e7d07ab524", + "IPY_MODEL_511d0770a32a4cb699d5e84912c9ac2d", + "IPY_MODEL_76d4b82d6d554db193d59f617263be71" + ], + "layout": "IPY_MODEL_2c525938ab3f40839e112d0514886621", + "tabbable": null, + "tooltip": null + } + }, + "3cadfe2f146044a09e7ca45ebb42dabe": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1663,60 +1860,25 @@ "width": null } }, - "1b45076c459847edb676c5da860e7c63": { - "model_module": "@jupyter-widgets/base", + "3f272745972e4c168cac6db59df9f06c": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "1c9880baaa514557b939f1bc383fd6dd": { + "3fb03a40105a4a8dbc546b7cfa8b900d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1734,7 +1896,30 @@ "text_color": null } }, - "1f782846199a4cd886bc9a2d8b267db5": { + "4055ef4e796f4f8483958016dc00dd55": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9cda8f7de6054952bbd9e41bd0a4c940", + "placeholder": "​", + "style": "IPY_MODEL_b4f2838fe33048d6bec330f5c4336b77", + "tabbable": null, + "tooltip": null, + "value": " 54.2M/54.2M [00:00<00:00, 235MB/s]" + } + }, + "46c99936a3904409a5d2c45a3ae8d429": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1787,7 +1972,7 @@ "width": null } }, - "1fc253a07301484fbf34df123112adf2": { + "474f62c90ab142829bd60ef8f670c2c8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1840,99 +2025,7 @@ "width": null } }, - "21332e3c65394cf38141b89a7102833d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_723beeec471a4b999f642afe6f412d4f", - "IPY_MODEL_6a1338550c7e47f2ace913c6db6703dc", - "IPY_MODEL_60390240aff543d7947dd2f2f3272bde" - ], - "layout": "IPY_MODEL_1b45076c459847edb676c5da860e7c63", - "tabbable": null, - "tooltip": null - } - }, - "2745511db571407b909eadcf1041fbbb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "27f46a8464f6451d9729e0085ec07516": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "2b2f96f6470846f1bd8aae7cb5dbeed7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "2b379296834846fa97be92e4b2d6df99": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "2b51658eec434f6c8284bd799690104b": { + "4a561e0c35224e959a51706de660c1c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1947,15 +2040,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e06dfc4b5e33464b8b271badc88425c4", + "layout": "IPY_MODEL_3cadfe2f146044a09e7ca45ebb42dabe", "placeholder": "​", - "style": "IPY_MODEL_1c9880baaa514557b939f1bc383fd6dd", + "style": "IPY_MODEL_c022ee6985d447c18dc0f780d2d2ac5f", "tabbable": null, "tooltip": null, - "value": "vocab.txt: 100%" + "value": "config.json: 100%" } }, - "2b64f899a53543fb8a6355d358ad40dc": { + "4bcac332c086434b897500c9fc8a4e13": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2008,7 +2101,7 @@ "width": null } }, - "3127352d799744d391cc01252a89de9a": { + "511d0770a32a4cb699d5e84912c9ac2d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2024,94 +2117,33 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c1eada512714499a9689453bf4dd17bb", - "max": 2211.0, + "layout": "IPY_MODEL_0c1022f9dd1e4b90821853a519d82c85", + "max": 466062.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_27f46a8464f6451d9729e0085ec07516", + "style": "IPY_MODEL_e5a4a1b7e0ee4ae8aaa1a07e7da4dae0", "tabbable": null, "tooltip": null, - "value": 2211.0 + "value": 466062.0 } }, - "37b540a8453d4401b5a798e49297b5a2": { + "59717a8253404f3498093319000f8ed7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f85452b3a7ee4bd8b96746e8f9cbd2a8", - "IPY_MODEL_6431ceb1e80c48e09225d44e2f40b86d", - "IPY_MODEL_a8240a8e7877465086dc27694c472f92" - ], - "layout": "IPY_MODEL_604c0d5a866c4704a89f806366a61376", - "tabbable": null, - "tooltip": null - } - }, - "3d76c17d5f61437bbb3f8fdefd0c78fa": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "3e46cd86409a4a129a6d5d33c5d96e40": { + "602fb306a03a4992b517e4ffc3a8945d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2164,7 +2196,7 @@ "width": null } }, - "40e898ed62284e21bf36ba199efb74ca": { + "627991388a1e43029b6a6db07ecbb108": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2217,7 +2249,7 @@ "width": null } }, - "4493cbda5ade4530a4f0dad6cc558cfa": { + "633ed034cdcc4522b350f94cca3f051b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2233,33 +2265,58 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3d76c17d5f61437bbb3f8fdefd0c78fa", - "max": 54245363.0, + "layout": "IPY_MODEL_627991388a1e43029b6a6db07ecbb108", + "max": 2211.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_13323479403a4c6ba63c87932eb6b7d1", + "style": "IPY_MODEL_f778d207cc6f436bbff1030f77f858d3", "tabbable": null, "tooltip": null, - "value": 54245363.0 + "value": 2211.0 } }, - "45bfcc5c5c3e4a10b411f71fb3237d72": { + "641c75ba0e1341108bb4918b53677229": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "65620908bd534eb6ae1cc64fa0fa9296": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ab2ae443e9404d0f967c7ea20dc4f167", + "placeholder": "​", + "style": "IPY_MODEL_3fb03a40105a4a8dbc546b7cfa8b900d", + "tabbable": null, + "tooltip": null, + "value": ".gitattributes: 100%" } }, - "46c6e93fc99f4795bfc2b82c11b1a4d6": { + "690068ad1f6e40f389a17ebdccf79f69": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2274,15 +2331,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1f782846199a4cd886bc9a2d8b267db5", + "layout": "IPY_MODEL_4bcac332c086434b897500c9fc8a4e13", "placeholder": "​", - "style": "IPY_MODEL_f350410541a947db9efae73d4e6d000d", + "style": "IPY_MODEL_3f272745972e4c168cac6db59df9f06c", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 129kB/s]" + "value": " 48.0/48.0 [00:00<00:00, 10.6kB/s]" } }, - "47dd26560f0f4f14ae1d6235bf187f43": { + "69e2a61b275146c580a41ab6348f7042": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2297,16 +2354,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_adcd6fe3722a404cbb4578c43dfb8622", - "IPY_MODEL_6ef5403b4f904ab5bad25124da8132c1", - "IPY_MODEL_46c6e93fc99f4795bfc2b82c11b1a4d6" + "IPY_MODEL_2447903dfaee44b8a215d28fcd0894d0", + "IPY_MODEL_fca74c921f8f453fbcb558b29d4ac360", + "IPY_MODEL_690068ad1f6e40f389a17ebdccf79f69" ], - "layout": "IPY_MODEL_fb13c0f13bd34a04a46ba3462d87a3cb", + "layout": "IPY_MODEL_210251b0b4bd433994b27f5bfea877c0", "tabbable": null, "tooltip": null } }, - "4e78d34c35c64a8ba9e42340cface541": { + "6a11590644db4a4c8d23ade9d3773ff6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2359,73 +2416,14 @@ "width": null } }, - "5d9fbb50ea574e9f80a04d65984ec3fb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "5f805e6909fa4676b560df990b1225b3": { - "model_module": "@jupyter-widgets/controls", + "6afb2a72832a456c904eea5d20c8155f": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "60390240aff543d7947dd2f2f3272bde": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7d0ebaac4f0b43a498272ea2a11b15a3", - "placeholder": "​", - "style": "IPY_MODEL_2b379296834846fa97be92e4b2d6df99", - "tabbable": null, - "tooltip": null, - "value": " 466k/466k [00:00<00:00, 14.0MB/s]" - } - }, - "604c0d5a866c4704a89f806366a61376": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -2471,163 +2469,7 @@ "width": null } }, - "617bb9dddcf14ecb9bae9c187156bb6c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "6431ceb1e80c48e09225d44e2f40b86d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0988aba1120e44e194591113d57b63d3", - "max": 48.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8944d62a93fe49f991ff8bacfeac322c", - "tabbable": null, - "tooltip": null, - "value": 48.0 - } - }, - "6692a301895241f7894a3bace80aec4a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_0bfe9f713e2d4b6ba3ef62402a269ffb", - "IPY_MODEL_4493cbda5ade4530a4f0dad6cc558cfa", - "IPY_MODEL_0b20d7b224384162a625ab6b3d0f9b75" - ], - "layout": "IPY_MODEL_18587e575408498d91cc167ce94971e8", - "tabbable": null, - "tooltip": null - } - }, - "69666c8921454e55aebdd3cb6ea39a73": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "6a1338550c7e47f2ace913c6db6703dc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_06bdd0de78584b4d8c963a129ebf2a90", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_076de96beb79463ab6936bf1bcf942f5", - "tabbable": null, - "tooltip": null, - "value": 466062.0 - } - }, - "6b9cbd8a34764f13b850393a56c2c764": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "6ef5403b4f904ab5bad25124da8132c1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_77b2da3b157a4e658101eddffc7a0c02", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2b2f96f6470846f1bd8aae7cb5dbeed7", - "tabbable": null, - "tooltip": null, - "value": 665.0 - } - }, - "70176e26d6ca48bda5d33005335369ce": { + "6d3476c1ac4c49009018513408f6af16": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2680,53 +2522,7 @@ "width": null } }, - "723beeec471a4b999f642afe6f412d4f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0a9bbe4ae4a7460aa182a6b390e2126a", - "placeholder": "​", - "style": "IPY_MODEL_617bb9dddcf14ecb9bae9c187156bb6c", - "tabbable": null, - "tooltip": null, - "value": "tokenizer.json: 100%" - } - }, - "723f6f685bea42f196189855c383ef77": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7b295c74e39947a6b06414e1b23aef24", - "placeholder": "​", - "style": "IPY_MODEL_6b9cbd8a34764f13b850393a56c2c764", - "tabbable": null, - "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 409kB/s]" - } - }, - "77b2da3b157a4e658101eddffc7a0c02": { + "6d87e43d8cbb41adbd9ed53a4108f0e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2779,7 +2575,7 @@ "width": null } }, - "7b295c74e39947a6b06414e1b23aef24": { + "75a0dacc70d541dda27c4dd83c53445a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2832,31 +2628,74 @@ "width": null } }, - "7bf569b1ec4240fbb7f1457722fe46c9": { + "76d4b82d6d554db193d59f617263be71": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f585c8f15e5c4595a88ccd3bd0205013", - "IPY_MODEL_9116b550551046e580f470834f6b5b3a", - "IPY_MODEL_b46d0225e69b453dac54123c7dfe9aa9" - ], - "layout": "IPY_MODEL_16a4915303e24ee999326b95f314a277", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_aed932244d5e41e0bb5741cdf6ada82b", + "placeholder": "​", + "style": "IPY_MODEL_1db1f4745f7c41b09eb7ade25dd80789", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 466k/466k [00:00<00:00, 15.6MB/s]" + } + }, + "7af59be6dc3e43b09a1474a944823a7f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7c158623f2f74c2db6f8f564a05041f8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_866127121f184d4ca996467eaf332a2c", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_130aabfb49f94f088bc47d302aef9d6a", + "tabbable": null, + "tooltip": null, + "value": 231508.0 } }, - "7d0ebaac4f0b43a498272ea2a11b15a3": { + "866127121f184d4ca996467eaf332a2c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2909,46 +2748,33 @@ "width": null } }, - "8944d62a93fe49f991ff8bacfeac322c": { + "86ea59ef120d4f75bcf45ec87650ec5b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "8ad231daa0c44bdbabf3c3cc6a0548db": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3e46cd86409a4a129a6d5d33c5d96e40", - "placeholder": "​", - "style": "IPY_MODEL_69666c8921454e55aebdd3cb6ea39a73", + "layout": "IPY_MODEL_a2d9fdb3a9074bed8b19fea9da56cc98", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d9447c4b33e542f8afe1377bbff3844b", "tabbable": null, "tooltip": null, - "value": "README.md: 100%" + "value": 54245363.0 } }, - "9116b550551046e580f470834f6b5b3a": { + "8c67d4bf505d48cd997624bfe21c4f54": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2964,33 +2790,35 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_e5f98261e33e4e68983283a794b3ce3e", - "max": 391.0, + "layout": "IPY_MODEL_46c99936a3904409a5d2c45a3ae8d429", + "max": 665.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_9c07f5c15bae4ce8b5469d47f4fedca6", + "style": "IPY_MODEL_c52fec6ed7bd445489ebba86786d088b", "tabbable": null, "tooltip": null, - "value": 391.0 + "value": 665.0 } }, - "9c07f5c15bae4ce8b5469d47f4fedca6": { + "9553c5d54da747a5895955890486e2f4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "a8240a8e7877465086dc27694c472f92": { + "95689bec286947c680044acec2a1c7d1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3005,61 +2833,51 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_70176e26d6ca48bda5d33005335369ce", + "layout": "IPY_MODEL_6d3476c1ac4c49009018513408f6af16", "placeholder": "​", - "style": "IPY_MODEL_f8e1af21c2314931b28a2ffdccd38cc1", + "style": "IPY_MODEL_9553c5d54da747a5895955890486e2f4", "tabbable": null, "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 9.68kB/s]" + "value": "pytorch_model.bin: 100%" } }, - "adcd6fe3722a404cbb4578c43dfb8622": { + "99356cb025f14d3c9dc0c6f02206fd28": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_bfef1d0d61ca4f1f8e1818ed08702bea", - "placeholder": "​", - "style": "IPY_MODEL_ec66181f622246e6b126cf01ee0e0c1f", - "tabbable": null, - "tooltip": null, - "value": "config.json: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b46d0225e69b453dac54123c7dfe9aa9": { + "9a897039093d4f668353b3c68a0d6f60": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4e78d34c35c64a8ba9e42340cface541", - "placeholder": "​", - "style": "IPY_MODEL_5d9fbb50ea574e9f80a04d65984ec3fb", - "tabbable": null, - "tooltip": null, - "value": " 391/391 [00:00<00:00, 62.9kB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "bfef1d0d61ca4f1f8e1818ed08702bea": { + "9b3d4267e67742c7a50c68da4c077ce5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3112,7 +2930,7 @@ "width": null } }, - "c1eada512714499a9689453bf4dd17bb": { + "9cda8f7de6054952bbd9e41bd0a4c940": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3165,99 +2983,60 @@ "width": null } }, - "c550a7da6dee4658a5e958b278220075": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_2b51658eec434f6c8284bd799690104b", - "IPY_MODEL_d117d190931044b78c7d6506f9a291f8", - "IPY_MODEL_12a2c923802042bbaf7afcb489317fc2" - ], - "layout": "IPY_MODEL_2b64f899a53543fb8a6355d358ad40dc", - "tabbable": null, - "tooltip": null - } - }, - "cf82eb1ddad343378d886f2d760e8e19": { - "model_module": "@jupyter-widgets/controls", + "a2d9fdb3a9074bed8b19fea9da56cc98": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d117d190931044b78c7d6506f9a291f8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_40e898ed62284e21bf36ba199efb74ca", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_45bfcc5c5c3e4a10b411f71fb3237d72", - "tabbable": null, - "tooltip": null, - "value": 231508.0 - } - }, - "dc4eb1dc64da457a9d83b0bad4f4fd96": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8ad231daa0c44bdbabf3c3cc6a0548db", - "IPY_MODEL_3127352d799744d391cc01252a89de9a", - "IPY_MODEL_723f6f685bea42f196189855c383ef77" - ], - "layout": "IPY_MODEL_e996d9e989544a01a9db1b3ba3e4bdb6", - "tabbable": null, - "tooltip": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "e06dfc4b5e33464b8b271badc88425c4": { + "ab2ae443e9404d0f967c7ea20dc4f167": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3310,7 +3089,7 @@ "width": null } }, - "e5f98261e33e4e68983283a794b3ce3e": { + "ab8ff183a42e48458e19db12e4aff633": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3363,7 +3142,7 @@ "width": null } }, - "e996d9e989544a01a9db1b3ba3e4bdb6": { + "aed932244d5e41e0bb5741cdf6ada82b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3416,25 +3195,60 @@ "width": null } }, - "ec66181f622246e6b126cf01ee0e0c1f": { - "model_module": "@jupyter-widgets/controls", + "b044aec91c9d459baee8d3e51cc6a561": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "eca51dd4d6224552b6d374e90dd7f129": { + "b4f2838fe33048d6bec330f5c4336b77": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3452,7 +3266,7 @@ "text_color": null } }, - "ecf47058d5624d17a0cd5595cc9a9ea1": { + "bcde360a04b34ea2b2044dbccff0ddf2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3505,7 +3319,7 @@ "width": null } }, - "f350410541a947db9efae73d4e6d000d": { + "c022ee6985d447c18dc0f780d2d2ac5f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3523,30 +3337,71 @@ "text_color": null } }, - "f585c8f15e5c4595a88ccd3bd0205013": { + "c2643363bb89468b960a200c90333f96": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_17d77342577f4828b221978f61b1cca1", - "placeholder": "​", - "style": "IPY_MODEL_03473efde97f4db7aeb4bf94a8dcb6ac", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_65620908bd534eb6ae1cc64fa0fa9296", + "IPY_MODEL_16ba14bab33347d3b2f7d63f5296038f", + "IPY_MODEL_d02f35bebaae41a3bb0031a1232a227b" + ], + "layout": "IPY_MODEL_0f9f2396d1cc4fd38cdfbd7189900eeb", "tabbable": null, - "tooltip": null, - "value": ".gitattributes: 100%" + "tooltip": null + } + }, + "c52fec6ed7bd445489ebba86786d088b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "cd2dc8a9dbe04b53b5e3e75f970e79ae": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_95689bec286947c680044acec2a1c7d1", + "IPY_MODEL_86ea59ef120d4f75bcf45ec87650ec5b", + "IPY_MODEL_4055ef4e796f4f8483958016dc00dd55" + ], + "layout": "IPY_MODEL_6d87e43d8cbb41adbd9ed53a4108f0e6", + "tabbable": null, + "tooltip": null } }, - "f85452b3a7ee4bd8b96746e8f9cbd2a8": { + "d02f35bebaae41a3bb0031a1232a227b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3561,15 +3416,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_09d4dbd7007b4cfda79c92844565bfd3", + "layout": "IPY_MODEL_bcde360a04b34ea2b2044dbccff0ddf2", "placeholder": "​", - "style": "IPY_MODEL_eca51dd4d6224552b6d374e90dd7f129", + "style": "IPY_MODEL_06b2f48afcd84d958179886e3ce3025d", "tabbable": null, "tooltip": null, - "value": "tokenizer_config.json: 100%" + "value": " 391/391 [00:00<00:00, 64.4kB/s]" } }, - "f8e1af21c2314931b28a2ffdccd38cc1": { + "d3bd3aa0536e4888966db595d9e76f94": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3587,7 +3442,23 @@ "text_color": null } }, - "fb13c0f13bd34a04a46ba3462d87a3cb": { + "d9447c4b33e542f8afe1377bbff3844b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "debae1e29bd44145bfd9d09c776517b2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3639,6 +3510,135 @@ "visibility": null, "width": null } + }, + "e17be4756dc3431b86f73424c964e106": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4a561e0c35224e959a51706de660c1c6", + "IPY_MODEL_8c67d4bf505d48cd997624bfe21c4f54", + "IPY_MODEL_1994d16f690b4a81afecab34748ae77d" + ], + "layout": "IPY_MODEL_6a11590644db4a4c8d23ade9d3773ff6", + "tabbable": null, + "tooltip": null + } + }, + "e5a4a1b7e0ee4ae8aaa1a07e7da4dae0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "f778d207cc6f436bbff1030f77f858d3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "fbb42843f43544bdbf56b7bdb0c00252": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_030d4a7a49fe4c6a9ad80c7b5359c7ba", + "IPY_MODEL_7c158623f2f74c2db6f8f564a05041f8", + "IPY_MODEL_024c678365fb44c7af3b2c6a09d40eb3" + ], + "layout": "IPY_MODEL_474f62c90ab142829bd60ef8f670c2c8", + "tabbable": null, + "tooltip": null + } + }, + "fc4190f3cdf54bc8a181cf578000ede5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b044aec91c9d459baee8d3e51cc6a561", + "placeholder": "​", + "style": "IPY_MODEL_99356cb025f14d3c9dc0c6f02206fd28", + "tabbable": null, + "tooltip": null, + "value": "README.md: 100%" + } + }, + "fca74c921f8f453fbcb558b29d4ac360": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0c7ae4ee46ee4bc0b896eef3fa94eaa3", + "max": 48.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_59717a8253404f3498093319000f8ed7", + "tabbable": null, + "tooltip": null, + "value": 48.0 + } } }, "version_major": 2, diff --git a/master/tutorials/datalab/audio.html b/master/tutorials/datalab/audio.html index 5e61abbb0..d5d8f9ea6 100644 --- a/master/tutorials/datalab/audio.html +++ b/master/tutorials/datalab/audio.html @@ -1351,7 +1351,7 @@

5. Use cleanlab to find label issues -{"state": {"2d3c1dc1060c4165802a43d8a1506254": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "946fc47d18224c0287afaa056d02fa86": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "c26558f6f8394f7483fa04455db2ead3": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_2d3c1dc1060c4165802a43d8a1506254", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_946fc47d18224c0287afaa056d02fa86", "tabbable": null, "tooltip": null, "value": 2041.0}}, "419fb750a9174f9dbee80f1946cac5e2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "04992c51b0da458e880d33ce9b344519": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "fdc61f1ca4794f988ad26aa36f51cded": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_419fb750a9174f9dbee80f1946cac5e2", "placeholder": "\u200b", "style": "IPY_MODEL_04992c51b0da458e880d33ce9b344519", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "59de9bdd6b4e4c99946071c77765c06e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a77c2cc33dd84816aca2750dfa77985f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "34e4112c8e594ad7811823c85546de7c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_59de9bdd6b4e4c99946071c77765c06e", "placeholder": "\u200b", "style": "IPY_MODEL_a77c2cc33dd84816aca2750dfa77985f", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007485kB/s]"}}, "da43246f2ddc4df9968898f36b1bfc0c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "86394b0091ab4767834e877eb97c7f29": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_fdc61f1ca4794f988ad26aa36f51cded", "IPY_MODEL_c26558f6f8394f7483fa04455db2ead3", "IPY_MODEL_34e4112c8e594ad7811823c85546de7c"], "layout": "IPY_MODEL_da43246f2ddc4df9968898f36b1bfc0c", "tabbable": null, "tooltip": null}}, "e7fc281836224420a165582ad5a1f00e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "367f32394edb4f978af0778e4a33a113": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "54fd655fb2ea4e58ba287b8a699358a4": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e7fc281836224420a165582ad5a1f00e", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_367f32394edb4f978af0778e4a33a113", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "7f6ff60608894304be071471768422d5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "89ca8bee4d29420391f60780a4e5bb37": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "e83d24db49ae4489ac3ce5c785ee2c56": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7f6ff60608894304be071471768422d5", "placeholder": "\u200b", "style": "IPY_MODEL_89ca8bee4d29420391f60780a4e5bb37", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "a7073edae4e4487fb17ec295efaf2195": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "62b1b0b90a684d3488af171459b2e05b": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "fb154ca6183045b2bcadfc5b92658f12": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_a7073edae4e4487fb17ec295efaf2195", "placeholder": "\u200b", "style": "IPY_MODEL_62b1b0b90a684d3488af171459b2e05b", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u200756.5MB/s]"}}, "5704c0dc0c934dab903a45047e08bfa1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "aea522f486884287934824875f65568c": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_e83d24db49ae4489ac3ce5c785ee2c56", "IPY_MODEL_54fd655fb2ea4e58ba287b8a699358a4", "IPY_MODEL_fb154ca6183045b2bcadfc5b92658f12"], "layout": "IPY_MODEL_5704c0dc0c934dab903a45047e08bfa1", "tabbable": null, "tooltip": null}}, "b67e2b3c22e243868a621e764d7615bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3cf8422c429b489fa3609423681b6f05": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "99c304eddb9d4f808e1444eda066b707": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b67e2b3c22e243868a621e764d7615bd", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_3cf8422c429b489fa3609423681b6f05", "tabbable": null, "tooltip": null, "value": 3201.0}}, "f841644ac2b04256b2f3c4ad9a4db82f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "67269dc6c7a64c2a85d31a7de8c68e5a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "78baba5e6db44fa6a19c704bf00fb60c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f841644ac2b04256b2f3c4ad9a4db82f", "placeholder": "\u200b", "style": "IPY_MODEL_67269dc6c7a64c2a85d31a7de8c68e5a", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "8c24d36de7d04ba8a98dbc6243bffa1e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c4f6d1fdbf7f4c11b3b58973c52c1d0d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "be4a812783c5484dbde2feef6424ebe3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8c24d36de7d04ba8a98dbc6243bffa1e", "placeholder": "\u200b", "style": "IPY_MODEL_c4f6d1fdbf7f4c11b3b58973c52c1d0d", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007792kB/s]"}}, "9198ec1e4cbc45a5bbf7c1f0e8976f79": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9001078dd11947c3b743e880d41d89e0": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_78baba5e6db44fa6a19c704bf00fb60c", "IPY_MODEL_99c304eddb9d4f808e1444eda066b707", "IPY_MODEL_be4a812783c5484dbde2feef6424ebe3"], "layout": "IPY_MODEL_9198ec1e4cbc45a5bbf7c1f0e8976f79", "tabbable": null, "tooltip": null}}, "73e424d16cbf4e40ad3206856f075d5b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2e538bcb8e2a4e88a9d22def0a5b4c06": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "db99e931b4c94d2fb4385e2ccfaa8522": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_73e424d16cbf4e40ad3206856f075d5b", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_2e538bcb8e2a4e88a9d22def0a5b4c06", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "d12f8fa702da44f6b81f1b84c1c76383": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6cd21c0f3c7d4b668b81735903eedb1b": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "6aef8917b0084643af4ba12c5101c4a0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d12f8fa702da44f6b81f1b84c1c76383", "placeholder": "\u200b", "style": "IPY_MODEL_6cd21c0f3c7d4b668b81735903eedb1b", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "b831f552387747248fdc25346996cacd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "78c7d82d53664b95a386ec96a27b6453": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "21a62ae51e6d4cf38d4e04afa3c63d08": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b831f552387747248fdc25346996cacd", "placeholder": "\u200b", "style": "IPY_MODEL_78c7d82d53664b95a386ec96a27b6453", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u200775.8MB/s]"}}, "355467884efe4ca683c9294faddd8e67": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4187151778314617bdd65072c0c39e18": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_6aef8917b0084643af4ba12c5101c4a0", "IPY_MODEL_db99e931b4c94d2fb4385e2ccfaa8522", "IPY_MODEL_21a62ae51e6d4cf38d4e04afa3c63d08"], "layout": "IPY_MODEL_355467884efe4ca683c9294faddd8e67", "tabbable": null, "tooltip": null}}, "6801e67ee9a2448e90812fa8fea0d356": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c9789735e7aa44fb8c6bcf3f8ceeebf2": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "4a0472fddab34db383019c14f53513c2": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6801e67ee9a2448e90812fa8fea0d356", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c9789735e7aa44fb8c6bcf3f8ceeebf2", "tabbable": null, "tooltip": null, "value": 128619.0}}, "73080f2ef1784e73947c31dfebce4e08": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fe5e77a095e54cad8e7f53fae0f07637": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "99009d85bc164733af718beec07a0dd3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_73080f2ef1784e73947c31dfebce4e08", "placeholder": "\u200b", "style": "IPY_MODEL_fe5e77a095e54cad8e7f53fae0f07637", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "0ef4fd968d284787a6d3621ab522b0f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fb8ea71a45ce49d1bc0bf64d3de435a1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "467e4df618db47e2aa4db7b4eeb1b07e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0ef4fd968d284787a6d3621ab522b0f4", "placeholder": "\u200b", "style": "IPY_MODEL_fb8ea71a45ce49d1bc0bf64d3de435a1", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u20075.74MB/s]"}}, "14d064897b934c9fa649860e4a0d136f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7aac7a5b86074e509ae85dca28a96569": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_99009d85bc164733af718beec07a0dd3", "IPY_MODEL_4a0472fddab34db383019c14f53513c2", "IPY_MODEL_467e4df618db47e2aa4db7b4eeb1b07e"], "layout": "IPY_MODEL_14d064897b934c9fa649860e4a0d136f", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"3b746dd57abf4e48b840678e157361a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c2fcf5c426794582844ba7fe67f5bf0d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "d26523912977411e89e34e601772c5e7": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3b746dd57abf4e48b840678e157361a0", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c2fcf5c426794582844ba7fe67f5bf0d", "tabbable": null, "tooltip": null, "value": 2041.0}}, "ba9c70c159d145eca40c9ec5f00e10bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c21f58a6f9ef478d93b5d5cbea115ac5": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "e50297cd14aa4e3d8085de0cf85c1ce6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ba9c70c159d145eca40c9ec5f00e10bf", "placeholder": "\u200b", "style": "IPY_MODEL_c21f58a6f9ef478d93b5d5cbea115ac5", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "e54764f46d2443c78a2231683c5e49f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1a28c17d077645289cbece7b582bff38": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f92e410017fd48d2bde8e4186aa4656d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e54764f46d2443c78a2231683c5e49f4", "placeholder": "\u200b", "style": "IPY_MODEL_1a28c17d077645289cbece7b582bff38", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007503kB/s]"}}, "02a1716972414a4ab78ce10f215c6fa5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "55350425aa0d469899137314f3e92f90": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_e50297cd14aa4e3d8085de0cf85c1ce6", "IPY_MODEL_d26523912977411e89e34e601772c5e7", "IPY_MODEL_f92e410017fd48d2bde8e4186aa4656d"], "layout": "IPY_MODEL_02a1716972414a4ab78ce10f215c6fa5", "tabbable": null, "tooltip": null}}, "8c39386dfadc4f00a3b61401340b5027": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7654d1aece2e4deea8f067bf7ed0d75f": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "7d6da2ad1e6d4a239344910f25865973": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8c39386dfadc4f00a3b61401340b5027", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_7654d1aece2e4deea8f067bf7ed0d75f", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "5f6c2b30014d49309342babba9cfdb63": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c224a0b9870f4912bc4c5f01be3742ff": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "616d309d4e8c4af19617013597722c01": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5f6c2b30014d49309342babba9cfdb63", "placeholder": "\u200b", "style": "IPY_MODEL_c224a0b9870f4912bc4c5f01be3742ff", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "e7c85b33b6374f2a9150e7a112ff1eb4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7c4a15b8e628482db62558e471690e48": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "a46bd57db65a49e7877ffb289ab890fe": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e7c85b33b6374f2a9150e7a112ff1eb4", "placeholder": "\u200b", "style": "IPY_MODEL_7c4a15b8e628482db62558e471690e48", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u2007165MB/s]"}}, "9f97091b941744a587797a5691271dbf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "55a2ed10dcf6449ea3f44c228e41649d": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_616d309d4e8c4af19617013597722c01", "IPY_MODEL_7d6da2ad1e6d4a239344910f25865973", "IPY_MODEL_a46bd57db65a49e7877ffb289ab890fe"], "layout": "IPY_MODEL_9f97091b941744a587797a5691271dbf", "tabbable": null, "tooltip": null}}, "55f1a6a628744186b446690a125398cf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e264fac15f0a42ca82068d8589ae8513": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "c0de4821b4b44edba49f5da71cf01e55": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_55f1a6a628744186b446690a125398cf", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e264fac15f0a42ca82068d8589ae8513", "tabbable": null, "tooltip": null, "value": 3201.0}}, "c9aa19ac8f2c4bb2882f0a1b119135c7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "12e05f659e244d019709f897840078ef": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "cced1d36ca1b4ba89c6c319b2c08ea67": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c9aa19ac8f2c4bb2882f0a1b119135c7", "placeholder": "\u200b", "style": "IPY_MODEL_12e05f659e244d019709f897840078ef", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "e8db508c9cbe4cff82ccfea250fed816": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6cd16db61e414bca8bbdaceaf2de90a0": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2ca74ded295e4a819edd1bbc487d7696": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e8db508c9cbe4cff82ccfea250fed816", "placeholder": "\u200b", "style": "IPY_MODEL_6cd16db61e414bca8bbdaceaf2de90a0", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007836kB/s]"}}, "5cdcdf0208d94b57ae29f6868ddaa2fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "afe02737e97b44148be2093a455189ab": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_cced1d36ca1b4ba89c6c319b2c08ea67", "IPY_MODEL_c0de4821b4b44edba49f5da71cf01e55", "IPY_MODEL_2ca74ded295e4a819edd1bbc487d7696"], "layout": "IPY_MODEL_5cdcdf0208d94b57ae29f6868ddaa2fb", "tabbable": null, "tooltip": null}}, "5b9881b17c064cec8704d2ee1db72f73": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "501e850963e5401eb4405609aa4ad10d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "35d7f30182e84d7f8b024664907d3889": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5b9881b17c064cec8704d2ee1db72f73", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_501e850963e5401eb4405609aa4ad10d", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "559f33ec97a248649786464fa03c5925": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "674e8b4769aa4bd49016ded625146793": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "c0d6d13905264432aab9320b81491a38": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_559f33ec97a248649786464fa03c5925", "placeholder": "\u200b", "style": "IPY_MODEL_674e8b4769aa4bd49016ded625146793", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "26a7764fcab840f29375f9a572cd5018": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8ea07e1b16474004afbe66df0064bf05": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "38096e9ea21c4c9b8e4b8b6d37572fb2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_26a7764fcab840f29375f9a572cd5018", "placeholder": "\u200b", "style": "IPY_MODEL_8ea07e1b16474004afbe66df0064bf05", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u2007252MB/s]"}}, "df58ea6b9fe94b8eac7caddc3ed0eec3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cc44d31ef90d4a30bb4869a030b69679": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_c0d6d13905264432aab9320b81491a38", "IPY_MODEL_35d7f30182e84d7f8b024664907d3889", "IPY_MODEL_38096e9ea21c4c9b8e4b8b6d37572fb2"], "layout": "IPY_MODEL_df58ea6b9fe94b8eac7caddc3ed0eec3", "tabbable": null, "tooltip": null}}, "daaaa84b6de34a3199979a7fc2ba28d4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "be555c98d56a4a2bb9ce24b0ea9ad20e": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "c30e5f8aab1d4ca89298dce03b7b3d59": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_daaaa84b6de34a3199979a7fc2ba28d4", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_be555c98d56a4a2bb9ce24b0ea9ad20e", "tabbable": null, "tooltip": null, "value": 128619.0}}, "9146d74cb6c24fbda9fe6abeaa2c4582": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "92fe8ed13c53428991ef0477a9301841": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "29b47291f4fc4f368b11fa57c131f87b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_9146d74cb6c24fbda9fe6abeaa2c4582", "placeholder": "\u200b", "style": "IPY_MODEL_92fe8ed13c53428991ef0477a9301841", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "dd888b159f8d45bca2c99b68ed603651": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7e219f34cc4844769eb7f07f6d5aeb3a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f5195bb586aa43eb960bfdcd5d37024c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_dd888b159f8d45bca2c99b68ed603651", "placeholder": "\u200b", "style": "IPY_MODEL_7e219f34cc4844769eb7f07f6d5aeb3a", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u20078.28MB/s]"}}, "72d8608e73b14e7b97d2d4b59445d152": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ffdbdf86699f41c8bd429712bf422fcd": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_29b47291f4fc4f368b11fa57c131f87b", "IPY_MODEL_c30e5f8aab1d4ca89298dce03b7b3d59", "IPY_MODEL_f5195bb586aa43eb960bfdcd5d37024c"], "layout": "IPY_MODEL_72d8608e73b14e7b97d2d4b59445d152", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/audio.ipynb b/master/tutorials/datalab/audio.ipynb index 25d6d5a74..ee55f25fd 100644 --- a/master/tutorials/datalab/audio.ipynb +++ b/master/tutorials/datalab/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:15.535813Z", - "iopub.status.busy": "2024-09-26T14:47:15.535636Z", - "iopub.status.idle": "2024-09-26T14:47:21.234346Z", - "shell.execute_reply": "2024-09-26T14:47:21.233674Z" + "iopub.execute_input": "2024-09-26T16:43:01.537168Z", + "iopub.status.busy": "2024-09-26T16:43:01.537008Z", + "iopub.status.idle": "2024-09-26T16:43:07.047040Z", + "shell.execute_reply": "2024-09-26T16:43:07.046467Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:47:21.236766Z", - "iopub.status.busy": "2024-09-26T14:47:21.236377Z", - "iopub.status.idle": "2024-09-26T14:47:21.239643Z", - "shell.execute_reply": "2024-09-26T14:47:21.239185Z" + "iopub.execute_input": "2024-09-26T16:43:07.049250Z", + "iopub.status.busy": "2024-09-26T16:43:07.048896Z", + "iopub.status.idle": "2024-09-26T16:43:07.052018Z", + "shell.execute_reply": "2024-09-26T16:43:07.051588Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:21.241324Z", - "iopub.status.busy": "2024-09-26T14:47:21.240999Z", - "iopub.status.idle": "2024-09-26T14:47:21.245772Z", - "shell.execute_reply": "2024-09-26T14:47:21.245316Z" + "iopub.execute_input": "2024-09-26T16:43:07.053565Z", + "iopub.status.busy": "2024-09-26T16:43:07.053275Z", + "iopub.status.idle": "2024-09-26T16:43:07.057955Z", + "shell.execute_reply": "2024-09-26T16:43:07.057408Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-26T14:47:21.247572Z", - "iopub.status.busy": "2024-09-26T14:47:21.247248Z", - "iopub.status.idle": "2024-09-26T14:47:23.090757Z", - "shell.execute_reply": "2024-09-26T14:47:23.089916Z" + "iopub.execute_input": "2024-09-26T16:43:07.059810Z", + "iopub.status.busy": "2024-09-26T16:43:07.059469Z", + "iopub.status.idle": "2024-09-26T16:43:08.914757Z", + "shell.execute_reply": "2024-09-26T16:43:08.914058Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-26T14:47:23.093045Z", - "iopub.status.busy": "2024-09-26T14:47:23.092832Z", - "iopub.status.idle": "2024-09-26T14:47:23.103750Z", - "shell.execute_reply": "2024-09-26T14:47:23.103275Z" + "iopub.execute_input": "2024-09-26T16:43:08.917067Z", + "iopub.status.busy": "2024-09-26T16:43:08.916654Z", + "iopub.status.idle": "2024-09-26T16:43:08.927744Z", + "shell.execute_reply": "2024-09-26T16:43:08.927309Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:23.105636Z", - "iopub.status.busy": "2024-09-26T14:47:23.105213Z", - "iopub.status.idle": "2024-09-26T14:47:23.110875Z", - "shell.execute_reply": "2024-09-26T14:47:23.110416Z" + "iopub.execute_input": "2024-09-26T16:43:08.929451Z", + "iopub.status.busy": "2024-09-26T16:43:08.929125Z", + "iopub.status.idle": "2024-09-26T16:43:08.934658Z", + "shell.execute_reply": "2024-09-26T16:43:08.934201Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-26T14:47:23.112413Z", - "iopub.status.busy": "2024-09-26T14:47:23.112235Z", - "iopub.status.idle": "2024-09-26T14:47:23.593390Z", - "shell.execute_reply": "2024-09-26T14:47:23.592868Z" + "iopub.execute_input": "2024-09-26T16:43:08.936423Z", + "iopub.status.busy": "2024-09-26T16:43:08.936090Z", + "iopub.status.idle": "2024-09-26T16:43:09.382102Z", + "shell.execute_reply": "2024-09-26T16:43:09.381533Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:23.595287Z", - "iopub.status.busy": "2024-09-26T14:47:23.594919Z", - "iopub.status.idle": "2024-09-26T14:47:24.771320Z", - "shell.execute_reply": "2024-09-26T14:47:24.770797Z" + "iopub.execute_input": "2024-09-26T16:43:09.383807Z", + "iopub.status.busy": "2024-09-26T16:43:09.383617Z", + "iopub.status.idle": "2024-09-26T16:43:10.103237Z", + "shell.execute_reply": "2024-09-26T16:43:10.102762Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-09-26T14:47:24.773502Z", - "iopub.status.busy": "2024-09-26T14:47:24.773132Z", - "iopub.status.idle": "2024-09-26T14:47:24.791888Z", - "shell.execute_reply": "2024-09-26T14:47:24.791419Z" + "iopub.execute_input": "2024-09-26T16:43:10.105140Z", + "iopub.status.busy": "2024-09-26T16:43:10.104826Z", + "iopub.status.idle": "2024-09-26T16:43:10.123673Z", + "shell.execute_reply": "2024-09-26T16:43:10.123230Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:24.793633Z", - "iopub.status.busy": "2024-09-26T14:47:24.793274Z", - "iopub.status.idle": "2024-09-26T14:47:24.796512Z", - "shell.execute_reply": "2024-09-26T14:47:24.796062Z" + "iopub.execute_input": "2024-09-26T16:43:10.125295Z", + "iopub.status.busy": "2024-09-26T16:43:10.125117Z", + "iopub.status.idle": "2024-09-26T16:43:10.128126Z", + "shell.execute_reply": "2024-09-26T16:43:10.127690Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:24.798106Z", - "iopub.status.busy": "2024-09-26T14:47:24.797929Z", - "iopub.status.idle": "2024-09-26T14:47:39.925585Z", - "shell.execute_reply": "2024-09-26T14:47:39.924902Z" + "iopub.execute_input": "2024-09-26T16:43:10.129599Z", + "iopub.status.busy": "2024-09-26T16:43:10.129426Z", + "iopub.status.idle": "2024-09-26T16:43:24.063453Z", + "shell.execute_reply": "2024-09-26T16:43:24.062910Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-26T14:47:39.927963Z", - "iopub.status.busy": "2024-09-26T14:47:39.927564Z", - "iopub.status.idle": "2024-09-26T14:47:39.931572Z", - "shell.execute_reply": "2024-09-26T14:47:39.931070Z" + "iopub.execute_input": "2024-09-26T16:43:24.065783Z", + "iopub.status.busy": "2024-09-26T16:43:24.065393Z", + "iopub.status.idle": "2024-09-26T16:43:24.069067Z", + "shell.execute_reply": "2024-09-26T16:43:24.068585Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:39.933402Z", - "iopub.status.busy": "2024-09-26T14:47:39.933168Z", - "iopub.status.idle": "2024-09-26T14:47:40.683130Z", - "shell.execute_reply": "2024-09-26T14:47:40.682533Z" + "iopub.execute_input": "2024-09-26T16:43:24.070801Z", + "iopub.status.busy": "2024-09-26T16:43:24.070429Z", + "iopub.status.idle": "2024-09-26T16:43:24.796997Z", + "shell.execute_reply": "2024-09-26T16:43:24.796401Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-26T14:47:40.685556Z", - "iopub.status.busy": "2024-09-26T14:47:40.684983Z", - "iopub.status.idle": "2024-09-26T14:47:40.690268Z", - "shell.execute_reply": "2024-09-26T14:47:40.689730Z" + "iopub.execute_input": "2024-09-26T16:43:24.799360Z", + "iopub.status.busy": "2024-09-26T16:43:24.798951Z", + "iopub.status.idle": "2024-09-26T16:43:24.804372Z", + "shell.execute_reply": "2024-09-26T16:43:24.803849Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:40.693275Z", - "iopub.status.busy": "2024-09-26T14:47:40.692350Z", - "iopub.status.idle": "2024-09-26T14:47:40.818191Z", - "shell.execute_reply": "2024-09-26T14:47:40.817594Z" + "iopub.execute_input": "2024-09-26T16:43:24.806452Z", + "iopub.status.busy": "2024-09-26T16:43:24.806041Z", + "iopub.status.idle": "2024-09-26T16:43:24.912380Z", + "shell.execute_reply": "2024-09-26T16:43:24.911725Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:40.820305Z", - "iopub.status.busy": "2024-09-26T14:47:40.819910Z", - "iopub.status.idle": "2024-09-26T14:47:40.832452Z", - "shell.execute_reply": "2024-09-26T14:47:40.831961Z" + "iopub.execute_input": "2024-09-26T16:43:24.914386Z", + "iopub.status.busy": "2024-09-26T16:43:24.914176Z", + "iopub.status.idle": "2024-09-26T16:43:24.927266Z", + "shell.execute_reply": "2024-09-26T16:43:24.926792Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:40.834307Z", - "iopub.status.busy": "2024-09-26T14:47:40.833985Z", - "iopub.status.idle": "2024-09-26T14:47:40.842566Z", - "shell.execute_reply": "2024-09-26T14:47:40.842095Z" + "iopub.execute_input": "2024-09-26T16:43:24.929104Z", + "iopub.status.busy": "2024-09-26T16:43:24.928764Z", + "iopub.status.idle": "2024-09-26T16:43:24.936656Z", + "shell.execute_reply": "2024-09-26T16:43:24.936202Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:40.844168Z", - "iopub.status.busy": "2024-09-26T14:47:40.843976Z", - "iopub.status.idle": "2024-09-26T14:47:40.848543Z", - "shell.execute_reply": "2024-09-26T14:47:40.847986Z" + "iopub.execute_input": "2024-09-26T16:43:24.938460Z", + "iopub.status.busy": "2024-09-26T16:43:24.938097Z", + "iopub.status.idle": "2024-09-26T16:43:24.942425Z", + "shell.execute_reply": "2024-09-26T16:43:24.941963Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-09-26T14:47:40.850235Z", - "iopub.status.busy": "2024-09-26T14:47:40.850049Z", - "iopub.status.idle": "2024-09-26T14:47:40.856001Z", - "shell.execute_reply": "2024-09-26T14:47:40.855443Z" + "iopub.execute_input": "2024-09-26T16:43:24.944111Z", + "iopub.status.busy": "2024-09-26T16:43:24.943760Z", + "iopub.status.idle": "2024-09-26T16:43:24.949408Z", + "shell.execute_reply": "2024-09-26T16:43:24.948868Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-26T14:47:40.857876Z", - "iopub.status.busy": "2024-09-26T14:47:40.857600Z", - "iopub.status.idle": "2024-09-26T14:47:40.971332Z", - "shell.execute_reply": "2024-09-26T14:47:40.970730Z" + "iopub.execute_input": "2024-09-26T16:43:24.951257Z", + "iopub.status.busy": "2024-09-26T16:43:24.950912Z", + "iopub.status.idle": "2024-09-26T16:43:25.066944Z", + "shell.execute_reply": "2024-09-26T16:43:25.066472Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1205,10 +1205,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-26T14:47:40.973220Z", - "iopub.status.busy": "2024-09-26T14:47:40.972870Z", - "iopub.status.idle": "2024-09-26T14:47:41.080277Z", - "shell.execute_reply": "2024-09-26T14:47:41.079768Z" + "iopub.execute_input": "2024-09-26T16:43:25.068884Z", + "iopub.status.busy": "2024-09-26T16:43:25.068556Z", + "iopub.status.idle": "2024-09-26T16:43:25.174055Z", + "shell.execute_reply": "2024-09-26T16:43:25.173471Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1253,10 +1253,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-26T14:47:41.082132Z", - "iopub.status.busy": "2024-09-26T14:47:41.081755Z", - "iopub.status.idle": "2024-09-26T14:47:41.187047Z", - "shell.execute_reply": "2024-09-26T14:47:41.186551Z" + "iopub.execute_input": "2024-09-26T16:43:25.175958Z", + "iopub.status.busy": "2024-09-26T16:43:25.175621Z", + "iopub.status.idle": "2024-09-26T16:43:25.276776Z", + "shell.execute_reply": "2024-09-26T16:43:25.276200Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1297,10 +1297,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:41.188688Z", - "iopub.status.busy": "2024-09-26T14:47:41.188508Z", - "iopub.status.idle": "2024-09-26T14:47:41.292182Z", - "shell.execute_reply": "2024-09-26T14:47:41.291705Z" + "iopub.execute_input": "2024-09-26T16:43:25.278582Z", + "iopub.status.busy": "2024-09-26T16:43:25.278368Z", + "iopub.status.idle": "2024-09-26T16:43:25.383010Z", + "shell.execute_reply": "2024-09-26T16:43:25.382463Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:41.294034Z", - "iopub.status.busy": "2024-09-26T14:47:41.293720Z", - "iopub.status.idle": "2024-09-26T14:47:41.297083Z", - "shell.execute_reply": "2024-09-26T14:47:41.296508Z" + "iopub.execute_input": "2024-09-26T16:43:25.385292Z", + "iopub.status.busy": "2024-09-26T16:43:25.384847Z", + "iopub.status.idle": "2024-09-26T16:43:25.388267Z", + "shell.execute_reply": "2024-09-26T16:43:25.387710Z" }, "nbsphinx": "hidden" }, @@ -1392,25 +1392,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "04992c51b0da458e880d33ce9b344519": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0ef4fd968d284787a6d3621ab522b0f4": { + "02a1716972414a4ab78ce10f215c6fa5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1463,7 +1445,43 @@ "width": null } }, - "14d064897b934c9fa649860e4a0d136f": { + "12e05f659e244d019709f897840078ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1a28c17d077645289cbece7b582bff38": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "26a7764fcab840f29375f9a572cd5018": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1516,7 +1534,79 @@ "width": null } }, - "21a62ae51e6d4cf38d4e04afa3c63d08": { + "29b47291f4fc4f368b11fa57c131f87b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9146d74cb6c24fbda9fe6abeaa2c4582", + "placeholder": "​", + "style": "IPY_MODEL_92fe8ed13c53428991ef0477a9301841", + "tabbable": null, + "tooltip": null, + "value": "label_encoder.txt: 100%" + } + }, + "2ca74ded295e4a819edd1bbc487d7696": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e8db508c9cbe4cff82ccfea250fed816", + "placeholder": "​", + "style": "IPY_MODEL_6cd16db61e414bca8bbdaceaf2de90a0", + "tabbable": null, + "tooltip": null, + "value": " 3.20k/3.20k [00:00<00:00, 836kB/s]" + } + }, + "35d7f30182e84d7f8b024664907d3889": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5b9881b17c064cec8704d2ee1db72f73", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_501e850963e5401eb4405609aa4ad10d", + "tabbable": null, + "tooltip": null, + "value": 15856877.0 + } + }, + "38096e9ea21c4c9b8e4b8b6d37572fb2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1531,15 +1621,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b831f552387747248fdc25346996cacd", + "layout": "IPY_MODEL_26a7764fcab840f29375f9a572cd5018", "placeholder": "​", - "style": "IPY_MODEL_78c7d82d53664b95a386ec96a27b6453", + "style": "IPY_MODEL_8ea07e1b16474004afbe66df0064bf05", "tabbable": null, "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 75.8MB/s]" + "value": " 15.9M/15.9M [00:00<00:00, 252MB/s]" } }, - "2d3c1dc1060c4165802a43d8a1506254": { + "3b746dd57abf4e48b840678e157361a0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1592,7 +1682,7 @@ "width": null } }, - "2e538bcb8e2a4e88a9d22def0a5b4c06": { + "501e850963e5401eb4405609aa4ad10d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1608,30 +1698,31 @@ "description_width": "" } }, - "34e4112c8e594ad7811823c85546de7c": { + "55350425aa0d469899137314f3e92f90": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_59de9bdd6b4e4c99946071c77765c06e", - "placeholder": "​", - "style": "IPY_MODEL_a77c2cc33dd84816aca2750dfa77985f", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e50297cd14aa4e3d8085de0cf85c1ce6", + "IPY_MODEL_d26523912977411e89e34e601772c5e7", + "IPY_MODEL_f92e410017fd48d2bde8e4186aa4656d" + ], + "layout": "IPY_MODEL_02a1716972414a4ab78ce10f215c6fa5", "tabbable": null, - "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 485kB/s]" + "tooltip": null } }, - "355467884efe4ca683c9294faddd8e67": { + "559f33ec97a248649786464fa03c5925": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1684,39 +1775,7 @@ "width": null } }, - "367f32394edb4f978af0778e4a33a113": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "3cf8422c429b489fa3609423681b6f05": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4187151778314617bdd65072c0c39e18": { + "55a2ed10dcf6449ea3f44c228e41649d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1731,16 +1790,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_6aef8917b0084643af4ba12c5101c4a0", - "IPY_MODEL_db99e931b4c94d2fb4385e2ccfaa8522", - "IPY_MODEL_21a62ae51e6d4cf38d4e04afa3c63d08" + "IPY_MODEL_616d309d4e8c4af19617013597722c01", + "IPY_MODEL_7d6da2ad1e6d4a239344910f25865973", + "IPY_MODEL_a46bd57db65a49e7877ffb289ab890fe" ], - "layout": "IPY_MODEL_355467884efe4ca683c9294faddd8e67", + "layout": "IPY_MODEL_9f97091b941744a587797a5691271dbf", "tabbable": null, "tooltip": null } }, - "419fb750a9174f9dbee80f1946cac5e2": { + "55f1a6a628744186b446690a125398cf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1793,82 +1852,7 @@ "width": null } }, - "467e4df618db47e2aa4db7b4eeb1b07e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0ef4fd968d284787a6d3621ab522b0f4", - "placeholder": "​", - "style": "IPY_MODEL_fb8ea71a45ce49d1bc0bf64d3de435a1", - "tabbable": null, - "tooltip": null, - "value": " 129k/129k [00:00<00:00, 5.74MB/s]" - } - }, - "4a0472fddab34db383019c14f53513c2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6801e67ee9a2448e90812fa8fea0d356", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c9789735e7aa44fb8c6bcf3f8ceeebf2", - "tabbable": null, - "tooltip": null, - "value": 128619.0 - } - }, - "54fd655fb2ea4e58ba287b8a699358a4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e7fc281836224420a165582ad5a1f00e", - "max": 16887676.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_367f32394edb4f978af0778e4a33a113", - "tabbable": null, - "tooltip": null, - "value": 16887676.0 - } - }, - "5704c0dc0c934dab903a45047e08bfa1": { + "5b9881b17c064cec8704d2ee1db72f73": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1921,7 +1905,7 @@ "width": null } }, - "59de9bdd6b4e4c99946071c77765c06e": { + "5cdcdf0208d94b57ae29f6868ddaa2fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1974,43 +1958,7 @@ "width": null } }, - "62b1b0b90a684d3488af171459b2e05b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "67269dc6c7a64c2a85d31a7de8c68e5a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "6801e67ee9a2448e90812fa8fea0d356": { + "5f6c2b30014d49309342babba9cfdb63": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2063,7 +2011,7 @@ "width": null } }, - "6aef8917b0084643af4ba12c5101c4a0": { + "616d309d4e8c4af19617013597722c01": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2078,15 +2026,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d12f8fa702da44f6b81f1b84c1c76383", + "layout": "IPY_MODEL_5f6c2b30014d49309342babba9cfdb63", "placeholder": "​", - "style": "IPY_MODEL_6cd21c0f3c7d4b668b81735903eedb1b", + "style": "IPY_MODEL_c224a0b9870f4912bc4c5f01be3742ff", "tabbable": null, "tooltip": null, - "value": "classifier.ckpt: 100%" + "value": "embedding_model.ckpt: 100%" } }, - "6cd21c0f3c7d4b668b81735903eedb1b": { + "674e8b4769aa4bd49016ded625146793": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2104,7 +2052,25 @@ "text_color": null } }, - "73080f2ef1784e73947c31dfebce4e08": { + "6cd16db61e414bca8bbdaceaf2de90a0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "72d8608e73b14e7b97d2d4b59445d152": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2157,7 +2123,85 @@ "width": null } }, - "73e424d16cbf4e40ad3206856f075d5b": { + "7654d1aece2e4deea8f067bf7ed0d75f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "7c4a15b8e628482db62558e471690e48": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7d6da2ad1e6d4a239344910f25865973": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8c39386dfadc4f00a3b61401340b5027", + "max": 16887676.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_7654d1aece2e4deea8f067bf7ed0d75f", + "tabbable": null, + "tooltip": null, + "value": 16887676.0 + } + }, + "7e219f34cc4844769eb7f07f6d5aeb3a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8c39386dfadc4f00a3b61401340b5027": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2210,30 +2254,7 @@ "width": null } }, - "78baba5e6db44fa6a19c704bf00fb60c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f841644ac2b04256b2f3c4ad9a4db82f", - "placeholder": "​", - "style": "IPY_MODEL_67269dc6c7a64c2a85d31a7de8c68e5a", - "tabbable": null, - "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "78c7d82d53664b95a386ec96a27b6453": { + "8ea07e1b16474004afbe66df0064bf05": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2251,31 +2272,7 @@ "text_color": null } }, - "7aac7a5b86074e509ae85dca28a96569": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_99009d85bc164733af718beec07a0dd3", - "IPY_MODEL_4a0472fddab34db383019c14f53513c2", - "IPY_MODEL_467e4df618db47e2aa4db7b4eeb1b07e" - ], - "layout": "IPY_MODEL_14d064897b934c9fa649860e4a0d136f", - "tabbable": null, - "tooltip": null - } - }, - "7f6ff60608894304be071471768422d5": { + "9146d74cb6c24fbda9fe6abeaa2c4582": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2328,31 +2325,7 @@ "width": null } }, - "86394b0091ab4767834e877eb97c7f29": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_fdc61f1ca4794f988ad26aa36f51cded", - "IPY_MODEL_c26558f6f8394f7483fa04455db2ead3", - "IPY_MODEL_34e4112c8e594ad7811823c85546de7c" - ], - "layout": "IPY_MODEL_da43246f2ddc4df9968898f36b1bfc0c", - "tabbable": null, - "tooltip": null - } - }, - "89ca8bee4d29420391f60780a4e5bb37": { + "92fe8ed13c53428991ef0477a9301841": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2370,7 +2343,7 @@ "text_color": null } }, - "8c24d36de7d04ba8a98dbc6243bffa1e": { + "9f97091b941744a587797a5691271dbf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2423,7 +2396,30 @@ "width": null } }, - "9001078dd11947c3b743e880d41d89e0": { + "a46bd57db65a49e7877ffb289ab890fe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e7c85b33b6374f2a9150e7a112ff1eb4", + "placeholder": "​", + "style": "IPY_MODEL_7c4a15b8e628482db62558e471690e48", + "tabbable": null, + "tooltip": null, + "value": " 16.9M/16.9M [00:00<00:00, 165MB/s]" + } + }, + "afe02737e97b44148be2093a455189ab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2438,16 +2434,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_78baba5e6db44fa6a19c704bf00fb60c", - "IPY_MODEL_99c304eddb9d4f808e1444eda066b707", - "IPY_MODEL_be4a812783c5484dbde2feef6424ebe3" + "IPY_MODEL_cced1d36ca1b4ba89c6c319b2c08ea67", + "IPY_MODEL_c0de4821b4b44edba49f5da71cf01e55", + "IPY_MODEL_2ca74ded295e4a819edd1bbc487d7696" ], - "layout": "IPY_MODEL_9198ec1e4cbc45a5bbf7c1f0e8976f79", + "layout": "IPY_MODEL_5cdcdf0208d94b57ae29f6868ddaa2fb", "tabbable": null, "tooltip": null } }, - "9198ec1e4cbc45a5bbf7c1f0e8976f79": { + "ba9c70c159d145eca40c9ec5f00e10bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2500,7 +2496,7 @@ "width": null } }, - "946fc47d18224c0287afaa056d02fa86": { + "be555c98d56a4a2bb9ce24b0ea9ad20e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2516,7 +2512,7 @@ "description_width": "" } }, - "99009d85bc164733af718beec07a0dd3": { + "c0d6d13905264432aab9320b81491a38": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2531,15 +2527,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_73080f2ef1784e73947c31dfebce4e08", + "layout": "IPY_MODEL_559f33ec97a248649786464fa03c5925", "placeholder": "​", - "style": "IPY_MODEL_fe5e77a095e54cad8e7f53fae0f07637", + "style": "IPY_MODEL_674e8b4769aa4bd49016ded625146793", "tabbable": null, "tooltip": null, - "value": "label_encoder.txt: 100%" + "value": "classifier.ckpt: 100%" } }, - "99c304eddb9d4f808e1444eda066b707": { + "c0de4821b4b44edba49f5da71cf01e55": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2555,17 +2551,95 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b67e2b3c22e243868a621e764d7615bd", + "layout": "IPY_MODEL_55f1a6a628744186b446690a125398cf", "max": 3201.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_3cf8422c429b489fa3609423681b6f05", + "style": "IPY_MODEL_e264fac15f0a42ca82068d8589ae8513", "tabbable": null, "tooltip": null, "value": 3201.0 } }, - "a7073edae4e4487fb17ec295efaf2195": { + "c21f58a6f9ef478d93b5d5cbea115ac5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "c224a0b9870f4912bc4c5f01be3742ff": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "c2fcf5c426794582844ba7fe67f5bf0d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c30e5f8aab1d4ca89298dce03b7b3d59": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_daaaa84b6de34a3199979a7fc2ba28d4", + "max": 128619.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_be555c98d56a4a2bb9ce24b0ea9ad20e", + "tabbable": null, + "tooltip": null, + "value": 128619.0 + } + }, + "c9aa19ac8f2c4bb2882f0a1b119135c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2618,49 +2692,80 @@ "width": null } }, - "a77c2cc33dd84816aca2750dfa77985f": { + "cc44d31ef90d4a30bb4869a030b69679": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_c0d6d13905264432aab9320b81491a38", + "IPY_MODEL_35d7f30182e84d7f8b024664907d3889", + "IPY_MODEL_38096e9ea21c4c9b8e4b8b6d37572fb2" + ], + "layout": "IPY_MODEL_df58ea6b9fe94b8eac7caddc3ed0eec3", + "tabbable": null, + "tooltip": null } }, - "aea522f486884287934824875f65568c": { + "cced1d36ca1b4ba89c6c319b2c08ea67": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_e83d24db49ae4489ac3ce5c785ee2c56", - "IPY_MODEL_54fd655fb2ea4e58ba287b8a699358a4", - "IPY_MODEL_fb154ca6183045b2bcadfc5b92658f12" - ], - "layout": "IPY_MODEL_5704c0dc0c934dab903a45047e08bfa1", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c9aa19ac8f2c4bb2882f0a1b119135c7", + "placeholder": "​", + "style": "IPY_MODEL_12e05f659e244d019709f897840078ef", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "d26523912977411e89e34e601772c5e7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3b746dd57abf4e48b840678e157361a0", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c2fcf5c426794582844ba7fe67f5bf0d", + "tabbable": null, + "tooltip": null, + "value": 2041.0 } }, - "b67e2b3c22e243868a621e764d7615bd": { + "daaaa84b6de34a3199979a7fc2ba28d4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2713,7 +2818,7 @@ "width": null } }, - "b831f552387747248fdc25346996cacd": { + "dd888b159f8d45bca2c99b68ed603651": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2766,90 +2871,7 @@ "width": null } }, - "be4a812783c5484dbde2feef6424ebe3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8c24d36de7d04ba8a98dbc6243bffa1e", - "placeholder": "​", - "style": "IPY_MODEL_c4f6d1fdbf7f4c11b3b58973c52c1d0d", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 792kB/s]" - } - }, - "c26558f6f8394f7483fa04455db2ead3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2d3c1dc1060c4165802a43d8a1506254", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_946fc47d18224c0287afaa056d02fa86", - "tabbable": null, - "tooltip": null, - "value": 2041.0 - } - }, - "c4f6d1fdbf7f4c11b3b58973c52c1d0d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c9789735e7aa44fb8c6bcf3f8ceeebf2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "d12f8fa702da44f6b81f1b84c1c76383": { + "df58ea6b9fe94b8eac7caddc3ed0eec3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2902,7 +2924,46 @@ "width": null } }, - "da43246f2ddc4df9968898f36b1bfc0c": { + "e264fac15f0a42ca82068d8589ae8513": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e50297cd14aa4e3d8085de0cf85c1ce6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ba9c70c159d145eca40c9ec5f00e10bf", + "placeholder": "​", + "style": "IPY_MODEL_c21f58a6f9ef478d93b5d5cbea115ac5", + "tabbable": null, + "tooltip": null, + "value": "hyperparams.yaml: 100%" + } + }, + "e54764f46d2443c78a2231683c5e49f4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2955,33 +3016,7 @@ "width": null } }, - "db99e931b4c94d2fb4385e2ccfaa8522": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_73e424d16cbf4e40ad3206856f075d5b", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2e538bcb8e2a4e88a9d22def0a5b4c06", - "tabbable": null, - "tooltip": null, - "value": 15856877.0 - } - }, - "e7fc281836224420a165582ad5a1f00e": { + "e7c85b33b6374f2a9150e7a112ff1eb4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3034,30 +3069,7 @@ "width": null } }, - "e83d24db49ae4489ac3ce5c785ee2c56": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7f6ff60608894304be071471768422d5", - "placeholder": "​", - "style": "IPY_MODEL_89ca8bee4d29420391f60780a4e5bb37", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" - } - }, - "f841644ac2b04256b2f3c4ad9a4db82f": { + "e8db508c9cbe4cff82ccfea250fed816": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3110,7 +3122,7 @@ "width": null } }, - "fb154ca6183045b2bcadfc5b92658f12": { + "f5195bb586aa43eb960bfdcd5d37024c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3125,33 +3137,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a7073edae4e4487fb17ec295efaf2195", + "layout": "IPY_MODEL_dd888b159f8d45bca2c99b68ed603651", "placeholder": "​", - "style": "IPY_MODEL_62b1b0b90a684d3488af171459b2e05b", + "style": "IPY_MODEL_7e219f34cc4844769eb7f07f6d5aeb3a", "tabbable": null, "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 56.5MB/s]" - } - }, - "fb8ea71a45ce49d1bc0bf64d3de435a1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": " 129k/129k [00:00<00:00, 8.28MB/s]" } }, - "fdc61f1ca4794f988ad26aa36f51cded": { + "f92e410017fd48d2bde8e4186aa4656d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3166,30 +3160,36 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_419fb750a9174f9dbee80f1946cac5e2", + "layout": "IPY_MODEL_e54764f46d2443c78a2231683c5e49f4", "placeholder": "​", - "style": "IPY_MODEL_04992c51b0da458e880d33ce9b344519", + "style": "IPY_MODEL_1a28c17d077645289cbece7b582bff38", "tabbable": null, "tooltip": null, - "value": "hyperparams.yaml: 100%" + "value": " 2.04k/2.04k [00:00<00:00, 503kB/s]" } }, - "fe5e77a095e54cad8e7f53fae0f07637": { + "ffdbdf86699f41c8bd429712bf422fcd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_29b47291f4fc4f368b11fa57c131f87b", + "IPY_MODEL_c30e5f8aab1d4ca89298dce03b7b3d59", + "IPY_MODEL_f5195bb586aa43eb960bfdcd5d37024c" + ], + "layout": "IPY_MODEL_72d8608e73b14e7b97d2d4b59445d152", + "tabbable": null, + "tooltip": null } } }, diff --git a/master/tutorials/datalab/datalab_advanced.html b/master/tutorials/datalab/datalab_advanced.html index db4995cbb..56fea4674 100644 --- a/master/tutorials/datalab/datalab_advanced.html +++ b/master/tutorials/datalab/datalab_advanced.html @@ -1295,7 +1295,7 @@

Functionality 3: Save and load Datalab objects

-
+
@@ -1570,7 +1570,7 @@

Functionality 4: Adding a custom IssueManager -{"state": {"ed4df9f1de274611a1946fa0e269d33e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e56307db7d2e450c9f2c4b97981eee9d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "ec22ebaf4e91499784ef8ac8e966a147": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ed4df9f1de274611a1946fa0e269d33e", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e56307db7d2e450c9f2c4b97981eee9d", "tabbable": null, "tooltip": null, "value": 132.0}}, "37952b8d6afc47f2b9ed08ee7e66d264": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "da7692e003c04b5f8f1de02ea7e688c1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b144d01abdc746e495df91b32d9d09e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_37952b8d6afc47f2b9ed08ee7e66d264", "placeholder": "\u200b", "style": "IPY_MODEL_da7692e003c04b5f8f1de02ea7e688c1", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "c51595d098f645f0a7967c34f586fa27": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "48eeb0e9ec1b440e9b49518e8338af15": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f4567689e9ee49d28457ae1b5cdc262a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c51595d098f645f0a7967c34f586fa27", "placeholder": "\u200b", "style": "IPY_MODEL_48eeb0e9ec1b440e9b49518e8338af15", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200711171.72\u2007examples/s]"}}, "9602309f72214bd0b17e007cb02789b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "84640571afb64f84bab623cedce4b8ca": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b144d01abdc746e495df91b32d9d09e8", "IPY_MODEL_ec22ebaf4e91499784ef8ac8e966a147", "IPY_MODEL_f4567689e9ee49d28457ae1b5cdc262a"], "layout": "IPY_MODEL_9602309f72214bd0b17e007cb02789b7", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"122cfc78db0a4d82be987746e7db55b3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5e788a24c13e40e7a58b20de79687c48": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "4283161175a04a38b573c00757a351ba": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_122cfc78db0a4d82be987746e7db55b3", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5e788a24c13e40e7a58b20de79687c48", "tabbable": null, "tooltip": null, "value": 132.0}}, "3b4bec3544d34091b1668397d8008487": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "30145da279a94774ac26137f8664e606": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "80d3e88ffd774c08b8c824d90797af4f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3b4bec3544d34091b1668397d8008487", "placeholder": "\u200b", "style": "IPY_MODEL_30145da279a94774ac26137f8664e606", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "01f570f11f6d4adfb5e283481fb6873f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e2937e5188c94f8e988bb9b08182b86f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "7c944fa7492f4e6ab25aa07afe9d2ae8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_01f570f11f6d4adfb5e283481fb6873f", "placeholder": "\u200b", "style": "IPY_MODEL_e2937e5188c94f8e988bb9b08182b86f", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200712766.58\u2007examples/s]"}}, "62a0eed107c14269970ecb6452671d92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "36943bd08bbc4f0cac7cd02c6477166a": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_80d3e88ffd774c08b8c824d90797af4f", "IPY_MODEL_4283161175a04a38b573c00757a351ba", "IPY_MODEL_7c944fa7492f4e6ab25aa07afe9d2ae8"], "layout": "IPY_MODEL_62a0eed107c14269970ecb6452671d92", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb index f581760ef..eff812b06 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": "2024-09-26T14:47:45.611697Z", - "iopub.status.busy": "2024-09-26T14:47:45.611515Z", - "iopub.status.idle": "2024-09-26T14:47:46.872000Z", - "shell.execute_reply": "2024-09-26T14:47:46.871368Z" + "iopub.execute_input": "2024-09-26T16:43:29.508025Z", + "iopub.status.busy": "2024-09-26T16:43:29.507623Z", + "iopub.status.idle": "2024-09-26T16:43:30.725308Z", + "shell.execute_reply": "2024-09-26T16:43:30.724758Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:47:46.874219Z", - "iopub.status.busy": "2024-09-26T14:47:46.873943Z", - "iopub.status.idle": "2024-09-26T14:47:46.877182Z", - "shell.execute_reply": "2024-09-26T14:47:46.876630Z" + "iopub.execute_input": "2024-09-26T16:43:30.727546Z", + "iopub.status.busy": "2024-09-26T16:43:30.727178Z", + "iopub.status.idle": "2024-09-26T16:43:30.730147Z", + "shell.execute_reply": "2024-09-26T16:43:30.729712Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:46.878965Z", - "iopub.status.busy": "2024-09-26T14:47:46.878784Z", - "iopub.status.idle": "2024-09-26T14:47:46.887523Z", - "shell.execute_reply": "2024-09-26T14:47:46.887072Z" + "iopub.execute_input": "2024-09-26T16:43:30.732044Z", + "iopub.status.busy": "2024-09-26T16:43:30.731715Z", + "iopub.status.idle": "2024-09-26T16:43:30.740353Z", + "shell.execute_reply": "2024-09-26T16:43:30.739825Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:46.889337Z", - "iopub.status.busy": "2024-09-26T14:47:46.889146Z", - "iopub.status.idle": "2024-09-26T14:47:46.893734Z", - "shell.execute_reply": "2024-09-26T14:47:46.893242Z" + "iopub.execute_input": "2024-09-26T16:43:30.742150Z", + "iopub.status.busy": "2024-09-26T16:43:30.741810Z", + "iopub.status.idle": "2024-09-26T16:43:30.746094Z", + "shell.execute_reply": "2024-09-26T16:43:30.745654Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:46.895681Z", - "iopub.status.busy": "2024-09-26T14:47:46.895273Z", - "iopub.status.idle": "2024-09-26T14:47:47.085029Z", - "shell.execute_reply": "2024-09-26T14:47:47.084376Z" + "iopub.execute_input": "2024-09-26T16:43:30.747886Z", + "iopub.status.busy": "2024-09-26T16:43:30.747554Z", + "iopub.status.idle": "2024-09-26T16:43:30.929651Z", + "shell.execute_reply": "2024-09-26T16:43:30.929117Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:47.087608Z", - "iopub.status.busy": "2024-09-26T14:47:47.087115Z", - "iopub.status.idle": "2024-09-26T14:47:47.421574Z", - "shell.execute_reply": "2024-09-26T14:47:47.420964Z" + "iopub.execute_input": "2024-09-26T16:43:30.931728Z", + "iopub.status.busy": "2024-09-26T16:43:30.931364Z", + "iopub.status.idle": "2024-09-26T16:43:31.309765Z", + "shell.execute_reply": "2024-09-26T16:43:31.309213Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:47.423413Z", - "iopub.status.busy": "2024-09-26T14:47:47.423220Z", - "iopub.status.idle": "2024-09-26T14:47:47.447494Z", - "shell.execute_reply": "2024-09-26T14:47:47.447007Z" + "iopub.execute_input": "2024-09-26T16:43:31.311751Z", + "iopub.status.busy": "2024-09-26T16:43:31.311386Z", + "iopub.status.idle": "2024-09-26T16:43:31.334944Z", + "shell.execute_reply": "2024-09-26T16:43:31.334515Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:47.449677Z", - "iopub.status.busy": "2024-09-26T14:47:47.449174Z", - "iopub.status.idle": "2024-09-26T14:47:47.542304Z", - "shell.execute_reply": "2024-09-26T14:47:47.541668Z" + "iopub.execute_input": "2024-09-26T16:43:31.336685Z", + "iopub.status.busy": "2024-09-26T16:43:31.336333Z", + "iopub.status.idle": "2024-09-26T16:43:31.347781Z", + "shell.execute_reply": "2024-09-26T16:43:31.347237Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:47.544443Z", - "iopub.status.busy": "2024-09-26T14:47:47.544249Z", - "iopub.status.idle": "2024-09-26T14:47:49.580917Z", - "shell.execute_reply": "2024-09-26T14:47:49.580350Z" + "iopub.execute_input": "2024-09-26T16:43:31.349731Z", + "iopub.status.busy": "2024-09-26T16:43:31.349414Z", + "iopub.status.idle": "2024-09-26T16:43:33.361740Z", + "shell.execute_reply": "2024-09-26T16:43:33.361164Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:49.583198Z", - "iopub.status.busy": "2024-09-26T14:47:49.582700Z", - "iopub.status.idle": "2024-09-26T14:47:49.606247Z", - "shell.execute_reply": "2024-09-26T14:47:49.605757Z" + "iopub.execute_input": "2024-09-26T16:43:33.363626Z", + "iopub.status.busy": "2024-09-26T16:43:33.363347Z", + "iopub.status.idle": "2024-09-26T16:43:33.384245Z", + "shell.execute_reply": "2024-09-26T16:43:33.383795Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:49.608105Z", - "iopub.status.busy": "2024-09-26T14:47:49.607920Z", - "iopub.status.idle": "2024-09-26T14:47:49.626258Z", - "shell.execute_reply": "2024-09-26T14:47:49.625780Z" + "iopub.execute_input": "2024-09-26T16:43:33.386200Z", + "iopub.status.busy": "2024-09-26T16:43:33.385739Z", + "iopub.status.idle": "2024-09-26T16:43:33.403247Z", + "shell.execute_reply": "2024-09-26T16:43:33.402681Z" } }, "outputs": [ @@ -937,10 +937,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:49.628144Z", - "iopub.status.busy": "2024-09-26T14:47:49.627801Z", - "iopub.status.idle": "2024-09-26T14:47:49.641555Z", - "shell.execute_reply": "2024-09-26T14:47:49.641075Z" + "iopub.execute_input": "2024-09-26T16:43:33.404892Z", + "iopub.status.busy": "2024-09-26T16:43:33.404719Z", + "iopub.status.idle": "2024-09-26T16:43:33.419441Z", + "shell.execute_reply": "2024-09-26T16:43:33.418878Z" } }, "outputs": [ @@ -1075,17 +1075,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:49.643368Z", - "iopub.status.busy": "2024-09-26T14:47:49.643024Z", - "iopub.status.idle": "2024-09-26T14:47:49.664797Z", - "shell.execute_reply": "2024-09-26T14:47:49.664316Z" + "iopub.execute_input": "2024-09-26T16:43:33.421030Z", + "iopub.status.busy": "2024-09-26T16:43:33.420861Z", + "iopub.status.idle": "2024-09-26T16:43:33.441321Z", + "shell.execute_reply": "2024-09-26T16:43:33.440862Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84640571afb64f84bab623cedce4b8ca", + "model_id": "36943bd08bbc4f0cac7cd02c6477166a", "version_major": 2, "version_minor": 0 }, @@ -1121,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:49.666763Z", - "iopub.status.busy": "2024-09-26T14:47:49.666313Z", - "iopub.status.idle": "2024-09-26T14:47:49.681745Z", - "shell.execute_reply": "2024-09-26T14:47:49.681144Z" + "iopub.execute_input": "2024-09-26T16:43:33.442984Z", + "iopub.status.busy": "2024-09-26T16:43:33.442657Z", + "iopub.status.idle": "2024-09-26T16:43:33.457166Z", + "shell.execute_reply": "2024-09-26T16:43:33.456606Z" } }, "outputs": [ @@ -1247,10 +1247,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:49.683800Z", - "iopub.status.busy": "2024-09-26T14:47:49.683338Z", - "iopub.status.idle": "2024-09-26T14:47:49.689263Z", - "shell.execute_reply": "2024-09-26T14:47:49.688776Z" + "iopub.execute_input": "2024-09-26T16:43:33.458939Z", + "iopub.status.busy": "2024-09-26T16:43:33.458532Z", + "iopub.status.idle": "2024-09-26T16:43:33.464332Z", + "shell.execute_reply": "2024-09-26T16:43:33.463793Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:49.691065Z", - "iopub.status.busy": "2024-09-26T14:47:49.690751Z", - "iopub.status.idle": "2024-09-26T14:47:49.709104Z", - "shell.execute_reply": "2024-09-26T14:47:49.708511Z" + "iopub.execute_input": "2024-09-26T16:43:33.466070Z", + "iopub.status.busy": "2024-09-26T16:43:33.465739Z", + "iopub.status.idle": "2024-09-26T16:43:33.482934Z", + "shell.execute_reply": "2024-09-26T16:43:33.482448Z" } }, "outputs": [ @@ -1447,7 +1447,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "37952b8d6afc47f2b9ed08ee7e66d264": { + "01f570f11f6d4adfb5e283481fb6873f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1500,49 +1500,7 @@ "width": null } }, - "48eeb0e9ec1b440e9b49518e8338af15": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "84640571afb64f84bab623cedce4b8ca": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b144d01abdc746e495df91b32d9d09e8", - "IPY_MODEL_ec22ebaf4e91499784ef8ac8e966a147", - "IPY_MODEL_f4567689e9ee49d28457ae1b5cdc262a" - ], - "layout": "IPY_MODEL_9602309f72214bd0b17e007cb02789b7", - "tabbable": null, - "tooltip": null - } - }, - "9602309f72214bd0b17e007cb02789b7": { + "122cfc78db0a4d82be987746e7db55b3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1595,30 +1553,49 @@ "width": null } }, - "b144d01abdc746e495df91b32d9d09e8": { + "30145da279a94774ac26137f8664e606": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "36943bd08bbc4f0cac7cd02c6477166a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_37952b8d6afc47f2b9ed08ee7e66d264", - "placeholder": "​", - "style": "IPY_MODEL_da7692e003c04b5f8f1de02ea7e688c1", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_80d3e88ffd774c08b8c824d90797af4f", + "IPY_MODEL_4283161175a04a38b573c00757a351ba", + "IPY_MODEL_7c944fa7492f4e6ab25aa07afe9d2ae8" + ], + "layout": "IPY_MODEL_62a0eed107c14269970ecb6452671d92", "tabbable": null, - "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" + "tooltip": null } }, - "c51595d098f645f0a7967c34f586fa27": { + "3b4bec3544d34091b1668397d8008487": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1671,25 +1648,33 @@ "width": null } }, - "da7692e003c04b5f8f1de02ea7e688c1": { + "4283161175a04a38b573c00757a351ba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_122cfc78db0a4d82be987746e7db55b3", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5e788a24c13e40e7a58b20de79687c48", + "tabbable": null, + "tooltip": null, + "value": 132.0 } }, - "e56307db7d2e450c9f2c4b97981eee9d": { + "5e788a24c13e40e7a58b20de79687c48": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1705,33 +1690,7 @@ "description_width": "" } }, - "ec22ebaf4e91499784ef8ac8e966a147": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ed4df9f1de274611a1946fa0e269d33e", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e56307db7d2e450c9f2c4b97981eee9d", - "tabbable": null, - "tooltip": null, - "value": 132.0 - } - }, - "ed4df9f1de274611a1946fa0e269d33e": { + "62a0eed107c14269970ecb6452671d92": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1784,7 +1743,7 @@ "width": null } }, - "f4567689e9ee49d28457ae1b5cdc262a": { + "7c944fa7492f4e6ab25aa07afe9d2ae8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1799,12 +1758,53 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c51595d098f645f0a7967c34f586fa27", + "layout": "IPY_MODEL_01f570f11f6d4adfb5e283481fb6873f", "placeholder": "​", - "style": "IPY_MODEL_48eeb0e9ec1b440e9b49518e8338af15", + "style": "IPY_MODEL_e2937e5188c94f8e988bb9b08182b86f", "tabbable": null, "tooltip": null, - "value": " 132/132 [00:00<00:00, 11171.72 examples/s]" + "value": " 132/132 [00:00<00:00, 12766.58 examples/s]" + } + }, + "80d3e88ffd774c08b8c824d90797af4f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3b4bec3544d34091b1668397d8008487", + "placeholder": "​", + "style": "IPY_MODEL_30145da279a94774ac26137f8664e606", + "tabbable": null, + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "e2937e5188c94f8e988bb9b08182b86f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } } }, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index 767bffe7d..a8a247d9a 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": "2024-09-26T14:47:52.531871Z", - "iopub.status.busy": "2024-09-26T14:47:52.531690Z", - "iopub.status.idle": "2024-09-26T14:47:53.801759Z", - "shell.execute_reply": "2024-09-26T14:47:53.801164Z" + "iopub.execute_input": "2024-09-26T16:43:36.231369Z", + "iopub.status.busy": "2024-09-26T16:43:36.231188Z", + "iopub.status.idle": "2024-09-26T16:43:37.455384Z", + "shell.execute_reply": "2024-09-26T16:43:37.454817Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:47:53.804080Z", - "iopub.status.busy": "2024-09-26T14:47:53.803489Z", - "iopub.status.idle": "2024-09-26T14:47:53.806671Z", - "shell.execute_reply": "2024-09-26T14:47:53.806214Z" + "iopub.execute_input": "2024-09-26T16:43:37.457493Z", + "iopub.status.busy": "2024-09-26T16:43:37.457070Z", + "iopub.status.idle": "2024-09-26T16:43:37.460124Z", + "shell.execute_reply": "2024-09-26T16:43:37.459682Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:53.808602Z", - "iopub.status.busy": "2024-09-26T14:47:53.808274Z", - "iopub.status.idle": "2024-09-26T14:47:53.817439Z", - "shell.execute_reply": "2024-09-26T14:47:53.816846Z" + "iopub.execute_input": "2024-09-26T16:43:37.461922Z", + "iopub.status.busy": "2024-09-26T16:43:37.461583Z", + "iopub.status.idle": "2024-09-26T16:43:37.470547Z", + "shell.execute_reply": "2024-09-26T16:43:37.470085Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:53.819218Z", - "iopub.status.busy": "2024-09-26T14:47:53.818813Z", - "iopub.status.idle": "2024-09-26T14:47:53.823869Z", - "shell.execute_reply": "2024-09-26T14:47:53.823416Z" + "iopub.execute_input": "2024-09-26T16:43:37.472280Z", + "iopub.status.busy": "2024-09-26T16:43:37.471948Z", + "iopub.status.idle": "2024-09-26T16:43:37.476442Z", + "shell.execute_reply": "2024-09-26T16:43:37.476031Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:53.825624Z", - "iopub.status.busy": "2024-09-26T14:47:53.825446Z", - "iopub.status.idle": "2024-09-26T14:47:54.012981Z", - "shell.execute_reply": "2024-09-26T14:47:54.012359Z" + "iopub.execute_input": "2024-09-26T16:43:37.478143Z", + "iopub.status.busy": "2024-09-26T16:43:37.477812Z", + "iopub.status.idle": "2024-09-26T16:43:37.667222Z", + "shell.execute_reply": "2024-09-26T16:43:37.666731Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:54.015114Z", - "iopub.status.busy": "2024-09-26T14:47:54.014923Z", - "iopub.status.idle": "2024-09-26T14:47:54.396149Z", - "shell.execute_reply": "2024-09-26T14:47:54.395584Z" + "iopub.execute_input": "2024-09-26T16:43:37.669296Z", + "iopub.status.busy": "2024-09-26T16:43:37.669002Z", + "iopub.status.idle": "2024-09-26T16:43:38.044708Z", + "shell.execute_reply": "2024-09-26T16:43:38.044126Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:54.398064Z", - "iopub.status.busy": "2024-09-26T14:47:54.397698Z", - "iopub.status.idle": "2024-09-26T14:47:54.400577Z", - "shell.execute_reply": "2024-09-26T14:47:54.400116Z" + "iopub.execute_input": "2024-09-26T16:43:38.046630Z", + "iopub.status.busy": "2024-09-26T16:43:38.046254Z", + "iopub.status.idle": "2024-09-26T16:43:38.049081Z", + "shell.execute_reply": "2024-09-26T16:43:38.048630Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:54.402366Z", - "iopub.status.busy": "2024-09-26T14:47:54.402019Z", - "iopub.status.idle": "2024-09-26T14:47:54.437650Z", - "shell.execute_reply": "2024-09-26T14:47:54.437009Z" + "iopub.execute_input": "2024-09-26T16:43:38.050827Z", + "iopub.status.busy": "2024-09-26T16:43:38.050490Z", + "iopub.status.idle": "2024-09-26T16:43:38.085409Z", + "shell.execute_reply": "2024-09-26T16:43:38.084752Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:54.439903Z", - "iopub.status.busy": "2024-09-26T14:47:54.439564Z", - "iopub.status.idle": "2024-09-26T14:47:56.611360Z", - "shell.execute_reply": "2024-09-26T14:47:56.610777Z" + "iopub.execute_input": "2024-09-26T16:43:38.087407Z", + "iopub.status.busy": "2024-09-26T16:43:38.087052Z", + "iopub.status.idle": "2024-09-26T16:43:40.126759Z", + "shell.execute_reply": "2024-09-26T16:43:40.126062Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:56.613533Z", - "iopub.status.busy": "2024-09-26T14:47:56.612962Z", - "iopub.status.idle": "2024-09-26T14:47:56.632035Z", - "shell.execute_reply": "2024-09-26T14:47:56.631583Z" + "iopub.execute_input": "2024-09-26T16:43:40.128952Z", + "iopub.status.busy": "2024-09-26T16:43:40.128351Z", + "iopub.status.idle": "2024-09-26T16:43:40.147691Z", + "shell.execute_reply": "2024-09-26T16:43:40.147237Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:56.633836Z", - "iopub.status.busy": "2024-09-26T14:47:56.633486Z", - "iopub.status.idle": "2024-09-26T14:47:56.639903Z", - "shell.execute_reply": "2024-09-26T14:47:56.639463Z" + "iopub.execute_input": "2024-09-26T16:43:40.149312Z", + "iopub.status.busy": "2024-09-26T16:43:40.149132Z", + "iopub.status.idle": "2024-09-26T16:43:40.155454Z", + "shell.execute_reply": "2024-09-26T16:43:40.155012Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:56.641707Z", - "iopub.status.busy": "2024-09-26T14:47:56.641368Z", - "iopub.status.idle": "2024-09-26T14:47:56.647367Z", - "shell.execute_reply": "2024-09-26T14:47:56.646803Z" + "iopub.execute_input": "2024-09-26T16:43:40.156917Z", + "iopub.status.busy": "2024-09-26T16:43:40.156749Z", + "iopub.status.idle": "2024-09-26T16:43:40.162653Z", + "shell.execute_reply": "2024-09-26T16:43:40.162165Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:56.649030Z", - "iopub.status.busy": "2024-09-26T14:47:56.648855Z", - "iopub.status.idle": "2024-09-26T14:47:56.659402Z", - "shell.execute_reply": "2024-09-26T14:47:56.658957Z" + "iopub.execute_input": "2024-09-26T16:43:40.164341Z", + "iopub.status.busy": "2024-09-26T16:43:40.164030Z", + "iopub.status.idle": "2024-09-26T16:43:40.174191Z", + "shell.execute_reply": "2024-09-26T16:43:40.173742Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:56.661038Z", - "iopub.status.busy": "2024-09-26T14:47:56.660772Z", - "iopub.status.idle": "2024-09-26T14:47:56.669970Z", - "shell.execute_reply": "2024-09-26T14:47:56.669408Z" + "iopub.execute_input": "2024-09-26T16:43:40.175842Z", + "iopub.status.busy": "2024-09-26T16:43:40.175532Z", + "iopub.status.idle": "2024-09-26T16:43:40.184412Z", + "shell.execute_reply": "2024-09-26T16:43:40.183860Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:56.671720Z", - "iopub.status.busy": "2024-09-26T14:47:56.671331Z", - "iopub.status.idle": "2024-09-26T14:47:56.678074Z", - "shell.execute_reply": "2024-09-26T14:47:56.677629Z" + "iopub.execute_input": "2024-09-26T16:43:40.186230Z", + "iopub.status.busy": "2024-09-26T16:43:40.185878Z", + "iopub.status.idle": "2024-09-26T16:43:40.192808Z", + "shell.execute_reply": "2024-09-26T16:43:40.192244Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:56.679875Z", - "iopub.status.busy": "2024-09-26T14:47:56.679423Z", - "iopub.status.idle": "2024-09-26T14:47:56.689042Z", - "shell.execute_reply": "2024-09-26T14:47:56.688474Z" + "iopub.execute_input": "2024-09-26T16:43:40.194543Z", + "iopub.status.busy": "2024-09-26T16:43:40.194209Z", + "iopub.status.idle": "2024-09-26T16:43:40.203170Z", + "shell.execute_reply": "2024-09-26T16:43:40.202619Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:56.690832Z", - "iopub.status.busy": "2024-09-26T14:47:56.690435Z", - "iopub.status.idle": "2024-09-26T14:47:56.707572Z", - "shell.execute_reply": "2024-09-26T14:47:56.706989Z" + "iopub.execute_input": "2024-09-26T16:43:40.204940Z", + "iopub.status.busy": "2024-09-26T16:43:40.204621Z", + "iopub.status.idle": "2024-09-26T16:43:40.220809Z", + "shell.execute_reply": "2024-09-26T16:43:40.220387Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index 832682f21..c12fc0945 100644 --- a/master/tutorials/datalab/image.html +++ b/master/tutorials/datalab/image.html @@ -731,31 +731,31 @@

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.

@@ -1068,7 +1068,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1100,7 +1100,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1132,7 +1132,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -2046,35 +2046,35 @@

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 @@ -2102,7 +2102,7 @@

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

diff --git a/master/tutorials/datalab/image.ipynb b/master/tutorials/datalab/image.ipynb index 81904fb5e..bf06c2d22 100644 --- a/master/tutorials/datalab/image.ipynb +++ b/master/tutorials/datalab/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:47:59.485196Z", - "iopub.status.busy": "2024-09-26T14:47:59.485011Z", - "iopub.status.idle": "2024-09-26T14:48:02.622875Z", - "shell.execute_reply": "2024-09-26T14:48:02.622306Z" + "iopub.execute_input": "2024-09-26T16:43:42.887693Z", + "iopub.status.busy": "2024-09-26T16:43:42.887525Z", + "iopub.status.idle": "2024-09-26T16:43:45.949917Z", + "shell.execute_reply": "2024-09-26T16:43:45.949252Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:48:02.625172Z", - "iopub.status.busy": "2024-09-26T14:48:02.624685Z", - "iopub.status.idle": "2024-09-26T14:48:02.628360Z", - "shell.execute_reply": "2024-09-26T14:48:02.627898Z" + "iopub.execute_input": "2024-09-26T16:43:45.952120Z", + "iopub.status.busy": "2024-09-26T16:43:45.951822Z", + "iopub.status.idle": "2024-09-26T16:43:45.955750Z", + "shell.execute_reply": "2024-09-26T16:43:45.955269Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:48:02.630127Z", - "iopub.status.busy": "2024-09-26T14:48:02.629815Z", - "iopub.status.idle": "2024-09-26T14:48:05.745420Z", - "shell.execute_reply": "2024-09-26T14:48:05.744938Z" + "iopub.execute_input": "2024-09-26T16:43:45.957491Z", + "iopub.status.busy": "2024-09-26T16:43:45.957140Z", + "iopub.status.idle": "2024-09-26T16:43:48.623900Z", + "shell.execute_reply": "2024-09-26T16:43:48.623404Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "517b83c613bb49c9ab0cd319caf77fa4", + "model_id": "50b5887bf4c344448a941ef696740bfb", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "157b0b5de92c4dd39861100e0048c0b7", + "model_id": "f16276c8203a4d9f9a7457266c1e666c", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dc9f46273db144d982f466b186d6ea8d", + "model_id": "788c289a49054234a89ef72aa47dbb50", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "77e4c9ec41b8452c8963af0b77b5555f", + "model_id": "330d908dc536412297733ff7acf50107", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd9cd1e986f74424b60db3510b43826d", + "model_id": "a7c9ce9c07064710be69384ca6979edc", "version_major": 2, "version_minor": 0 }, @@ -260,10 +260,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:48:05.747100Z", - "iopub.status.busy": "2024-09-26T14:48:05.746916Z", - "iopub.status.idle": "2024-09-26T14:48:05.750921Z", - "shell.execute_reply": "2024-09-26T14:48:05.750358Z" + "iopub.execute_input": "2024-09-26T16:43:48.625811Z", + "iopub.status.busy": "2024-09-26T16:43:48.625484Z", + "iopub.status.idle": "2024-09-26T16:43:48.629364Z", + "shell.execute_reply": "2024-09-26T16:43:48.628797Z" } }, "outputs": [ @@ -288,17 +288,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:48:05.752502Z", - "iopub.status.busy": "2024-09-26T14:48:05.752202Z", - "iopub.status.idle": "2024-09-26T14:48:17.148896Z", - "shell.execute_reply": "2024-09-26T14:48:17.148237Z" + "iopub.execute_input": "2024-09-26T16:43:48.631182Z", + "iopub.status.busy": "2024-09-26T16:43:48.630791Z", + "iopub.status.idle": "2024-09-26T16:44:00.135959Z", + "shell.execute_reply": "2024-09-26T16:44:00.135295Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5e114e4103d94679910f2192af574f94", + "model_id": "06b16b9d750441d08a55ba65990da75c", "version_major": 2, "version_minor": 0 }, @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:48:17.151191Z", - "iopub.status.busy": "2024-09-26T14:48:17.150948Z", - "iopub.status.idle": "2024-09-26T14:48:35.261693Z", - "shell.execute_reply": "2024-09-26T14:48:35.261069Z" + "iopub.execute_input": "2024-09-26T16:44:00.138207Z", + "iopub.status.busy": "2024-09-26T16:44:00.137966Z", + "iopub.status.idle": "2024-09-26T16:44:18.764517Z", + "shell.execute_reply": "2024-09-26T16:44:18.763972Z" } }, "outputs": [], @@ -372,10 +372,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:48:35.264037Z", - "iopub.status.busy": "2024-09-26T14:48:35.263651Z", - "iopub.status.idle": "2024-09-26T14:48:35.269633Z", - "shell.execute_reply": "2024-09-26T14:48:35.269145Z" + "iopub.execute_input": "2024-09-26T16:44:18.766838Z", + "iopub.status.busy": "2024-09-26T16:44:18.766425Z", + "iopub.status.idle": "2024-09-26T16:44:18.772477Z", + "shell.execute_reply": "2024-09-26T16:44:18.772002Z" } }, "outputs": [], @@ -413,10 +413,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:48:35.271336Z", - "iopub.status.busy": "2024-09-26T14:48:35.270995Z", - "iopub.status.idle": "2024-09-26T14:48:35.274864Z", - "shell.execute_reply": "2024-09-26T14:48:35.274455Z" + "iopub.execute_input": "2024-09-26T16:44:18.774054Z", + "iopub.status.busy": "2024-09-26T16:44:18.773734Z", + "iopub.status.idle": "2024-09-26T16:44:18.777960Z", + "shell.execute_reply": "2024-09-26T16:44:18.777422Z" }, "nbsphinx": "hidden" }, @@ -553,10 +553,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:48:35.276663Z", - "iopub.status.busy": "2024-09-26T14:48:35.276341Z", - "iopub.status.idle": "2024-09-26T14:48:35.285167Z", - "shell.execute_reply": "2024-09-26T14:48:35.284719Z" + "iopub.execute_input": "2024-09-26T16:44:18.779803Z", + "iopub.status.busy": "2024-09-26T16:44:18.779488Z", + "iopub.status.idle": "2024-09-26T16:44:18.788477Z", + "shell.execute_reply": "2024-09-26T16:44:18.787927Z" }, "nbsphinx": "hidden" }, @@ -681,10 +681,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:48:35.286904Z", - "iopub.status.busy": "2024-09-26T14:48:35.286584Z", - "iopub.status.idle": "2024-09-26T14:48:35.314453Z", - "shell.execute_reply": "2024-09-26T14:48:35.313973Z" + "iopub.execute_input": "2024-09-26T16:44:18.790046Z", + "iopub.status.busy": "2024-09-26T16:44:18.789871Z", + "iopub.status.idle": "2024-09-26T16:44:18.818468Z", + "shell.execute_reply": "2024-09-26T16:44:18.818041Z" } }, "outputs": [], @@ -721,10 +721,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:48:35.316272Z", - "iopub.status.busy": "2024-09-26T14:48:35.315932Z", - "iopub.status.idle": "2024-09-26T14:49:09.563750Z", - "shell.execute_reply": "2024-09-26T14:49:09.563113Z" + "iopub.execute_input": "2024-09-26T16:44:18.820115Z", + "iopub.status.busy": "2024-09-26T16:44:18.819938Z", + "iopub.status.idle": "2024-09-26T16:44:52.032031Z", + "shell.execute_reply": "2024-09-26T16:44:52.031416Z" } }, "outputs": [ @@ -740,21 +740,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.965\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.900\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.763\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.739\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c99cd03c2204dd69d220e1911ef407b", + "model_id": "0e3bcece048b4a29932526acdaec4e78", "version_major": 2, "version_minor": 0 }, @@ -775,7 +775,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ccc0d279330845b8b34f60a57e76743f", + "model_id": "56a181d584f247a798b2fa5b261b7f49", "version_major": 2, "version_minor": 0 }, @@ -798,21 +798,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.062\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.958\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.901\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.603\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c01e10af9cd04c4c90430d0afbaa6da0", + "model_id": "c16a8793cbbe40a1ac2b15a9037fabd9", "version_major": 2, "version_minor": 0 }, @@ -833,7 +833,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60160531292f49f6912a5e7fa5c1cd4a", + "model_id": "552d50148a6144d18fe7fdebcb4c2a2f", "version_major": 2, "version_minor": 0 }, @@ -856,21 +856,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.009\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.938\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.800\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.605\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e20ec2fb456e4bb9bfb446110e53d341", + "model_id": "152c7e3ade014d50839d1407d6baeb13", "version_major": 2, "version_minor": 0 }, @@ -891,7 +891,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c97151f1d7f4a49a3e2278cddb3c604", + "model_id": "4c5c9bab82cf4d7bb1787ca880626761", "version_major": 2, "version_minor": 0 }, @@ -970,10 +970,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:49:09.565771Z", - "iopub.status.busy": "2024-09-26T14:49:09.565547Z", - "iopub.status.idle": "2024-09-26T14:49:09.582928Z", - "shell.execute_reply": "2024-09-26T14:49:09.582459Z" + "iopub.execute_input": "2024-09-26T16:44:52.034212Z", + "iopub.status.busy": "2024-09-26T16:44:52.033798Z", + "iopub.status.idle": "2024-09-26T16:44:52.050731Z", + "shell.execute_reply": "2024-09-26T16:44:52.050161Z" } }, "outputs": [], @@ -998,10 +998,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:49:09.584801Z", - "iopub.status.busy": "2024-09-26T14:49:09.584617Z", - "iopub.status.idle": "2024-09-26T14:49:10.073024Z", - "shell.execute_reply": "2024-09-26T14:49:10.072472Z" + "iopub.execute_input": "2024-09-26T16:44:52.052564Z", + "iopub.status.busy": "2024-09-26T16:44:52.052248Z", + "iopub.status.idle": "2024-09-26T16:44:52.527936Z", + "shell.execute_reply": "2024-09-26T16:44:52.527384Z" } }, "outputs": [], @@ -1021,10 +1021,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:49:10.074957Z", - "iopub.status.busy": "2024-09-26T14:49:10.074773Z", - "iopub.status.idle": "2024-09-26T14:51:03.811106Z", - "shell.execute_reply": "2024-09-26T14:51:03.810393Z" + "iopub.execute_input": "2024-09-26T16:44:52.530022Z", + "iopub.status.busy": "2024-09-26T16:44:52.529664Z", + "iopub.status.idle": "2024-09-26T16:46:42.282766Z", + "shell.execute_reply": "2024-09-26T16:46:42.282126Z" } }, "outputs": [ @@ -1063,7 +1063,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48ba164541954d109266e55290018b2b", + "model_id": "3a77faf9cb2646f79de794a3636a0bdf", "version_major": 2, "version_minor": 0 }, @@ -1109,10 +1109,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:03.813379Z", - "iopub.status.busy": "2024-09-26T14:51:03.812973Z", - "iopub.status.idle": "2024-09-26T14:51:04.287434Z", - "shell.execute_reply": "2024-09-26T14:51:04.286542Z" + "iopub.execute_input": "2024-09-26T16:46:42.284944Z", + "iopub.status.busy": "2024-09-26T16:46:42.284573Z", + "iopub.status.idle": "2024-09-26T16:46:42.738985Z", + "shell.execute_reply": "2024-09-26T16:46:42.738425Z" } }, "outputs": [ @@ -1258,10 +1258,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:04.289772Z", - "iopub.status.busy": "2024-09-26T14:51:04.289550Z", - "iopub.status.idle": "2024-09-26T14:51:04.352405Z", - "shell.execute_reply": "2024-09-26T14:51:04.351866Z" + "iopub.execute_input": "2024-09-26T16:46:42.740906Z", + "iopub.status.busy": "2024-09-26T16:46:42.740509Z", + "iopub.status.idle": "2024-09-26T16:46:42.802017Z", + "shell.execute_reply": "2024-09-26T16:46:42.801547Z" } }, "outputs": [ @@ -1365,10 +1365,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:04.354105Z", - "iopub.status.busy": "2024-09-26T14:51:04.353929Z", - "iopub.status.idle": "2024-09-26T14:51:04.362590Z", - "shell.execute_reply": "2024-09-26T14:51:04.362148Z" + "iopub.execute_input": "2024-09-26T16:46:42.803789Z", + "iopub.status.busy": "2024-09-26T16:46:42.803482Z", + "iopub.status.idle": "2024-09-26T16:46:42.811838Z", + "shell.execute_reply": "2024-09-26T16:46:42.811270Z" } }, "outputs": [ @@ -1498,10 +1498,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:04.364304Z", - "iopub.status.busy": "2024-09-26T14:51:04.364127Z", - "iopub.status.idle": "2024-09-26T14:51:04.368705Z", - "shell.execute_reply": "2024-09-26T14:51:04.368265Z" + "iopub.execute_input": "2024-09-26T16:46:42.813542Z", + "iopub.status.busy": "2024-09-26T16:46:42.813244Z", + "iopub.status.idle": "2024-09-26T16:46:42.819130Z", + "shell.execute_reply": "2024-09-26T16:46:42.818578Z" }, "nbsphinx": "hidden" }, @@ -1547,10 +1547,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:04.370241Z", - "iopub.status.busy": "2024-09-26T14:51:04.370067Z", - "iopub.status.idle": "2024-09-26T14:51:04.889724Z", - "shell.execute_reply": "2024-09-26T14:51:04.889048Z" + "iopub.execute_input": "2024-09-26T16:46:42.820757Z", + "iopub.status.busy": "2024-09-26T16:46:42.820453Z", + "iopub.status.idle": "2024-09-26T16:46:43.324072Z", + "shell.execute_reply": "2024-09-26T16:46:43.323436Z" } }, "outputs": [ @@ -1585,10 +1585,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:04.892026Z", - "iopub.status.busy": "2024-09-26T14:51:04.891753Z", - "iopub.status.idle": "2024-09-26T14:51:04.901156Z", - "shell.execute_reply": "2024-09-26T14:51:04.900641Z" + "iopub.execute_input": "2024-09-26T16:46:43.326219Z", + "iopub.status.busy": "2024-09-26T16:46:43.325807Z", + "iopub.status.idle": "2024-09-26T16:46:43.334519Z", + "shell.execute_reply": "2024-09-26T16:46:43.333933Z" } }, "outputs": [ @@ -1755,10 +1755,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:04.903329Z", - "iopub.status.busy": "2024-09-26T14:51:04.902914Z", - "iopub.status.idle": "2024-09-26T14:51:04.911571Z", - "shell.execute_reply": "2024-09-26T14:51:04.910975Z" + "iopub.execute_input": "2024-09-26T16:46:43.336486Z", + "iopub.status.busy": "2024-09-26T16:46:43.336107Z", + "iopub.status.idle": "2024-09-26T16:46:43.343319Z", + "shell.execute_reply": "2024-09-26T16:46:43.342758Z" }, "nbsphinx": "hidden" }, @@ -1834,10 +1834,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:04.913701Z", - "iopub.status.busy": "2024-09-26T14:51:04.913094Z", - "iopub.status.idle": "2024-09-26T14:51:05.388394Z", - "shell.execute_reply": "2024-09-26T14:51:05.387783Z" + "iopub.execute_input": "2024-09-26T16:46:43.344865Z", + "iopub.status.busy": "2024-09-26T16:46:43.344691Z", + "iopub.status.idle": "2024-09-26T16:46:44.105601Z", + "shell.execute_reply": "2024-09-26T16:46:44.105031Z" } }, "outputs": [ @@ -1874,10 +1874,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:05.390453Z", - "iopub.status.busy": "2024-09-26T14:51:05.390012Z", - "iopub.status.idle": "2024-09-26T14:51:05.406956Z", - "shell.execute_reply": "2024-09-26T14:51:05.406349Z" + "iopub.execute_input": "2024-09-26T16:46:44.107481Z", + "iopub.status.busy": "2024-09-26T16:46:44.107301Z", + "iopub.status.idle": "2024-09-26T16:46:44.122754Z", + "shell.execute_reply": "2024-09-26T16:46:44.122288Z" } }, "outputs": [ @@ -2034,10 +2034,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:05.408881Z", - "iopub.status.busy": "2024-09-26T14:51:05.408597Z", - "iopub.status.idle": "2024-09-26T14:51:05.415372Z", - "shell.execute_reply": "2024-09-26T14:51:05.414805Z" + "iopub.execute_input": "2024-09-26T16:46:44.124568Z", + "iopub.status.busy": "2024-09-26T16:46:44.124387Z", + "iopub.status.idle": "2024-09-26T16:46:44.130189Z", + "shell.execute_reply": "2024-09-26T16:46:44.129752Z" }, "nbsphinx": "hidden" }, @@ -2082,10 +2082,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:05.417178Z", - "iopub.status.busy": "2024-09-26T14:51:05.416863Z", - "iopub.status.idle": "2024-09-26T14:51:06.140962Z", - "shell.execute_reply": "2024-09-26T14:51:06.140485Z" + "iopub.execute_input": "2024-09-26T16:46:44.131659Z", + "iopub.status.busy": "2024-09-26T16:46:44.131487Z", + "iopub.status.idle": "2024-09-26T16:46:44.603534Z", + "shell.execute_reply": "2024-09-26T16:46:44.602960Z" } }, "outputs": [ @@ -2167,10 +2167,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:06.143001Z", - "iopub.status.busy": "2024-09-26T14:51:06.142640Z", - "iopub.status.idle": "2024-09-26T14:51:06.151972Z", - "shell.execute_reply": "2024-09-26T14:51:06.151393Z" + "iopub.execute_input": "2024-09-26T16:46:44.606037Z", + "iopub.status.busy": "2024-09-26T16:46:44.605545Z", + "iopub.status.idle": "2024-09-26T16:46:44.615105Z", + "shell.execute_reply": "2024-09-26T16:46:44.614534Z" } }, "outputs": [ @@ -2298,10 +2298,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:06.154615Z", - "iopub.status.busy": "2024-09-26T14:51:06.153791Z", - "iopub.status.idle": "2024-09-26T14:51:06.159002Z", - "shell.execute_reply": "2024-09-26T14:51:06.158561Z" + "iopub.execute_input": "2024-09-26T16:46:44.617099Z", + "iopub.status.busy": "2024-09-26T16:46:44.616903Z", + "iopub.status.idle": "2024-09-26T16:46:44.622925Z", + "shell.execute_reply": "2024-09-26T16:46:44.622344Z" }, "nbsphinx": "hidden" }, @@ -2338,10 +2338,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:06.160864Z", - "iopub.status.busy": "2024-09-26T14:51:06.160545Z", - "iopub.status.idle": "2024-09-26T14:51:06.329043Z", - "shell.execute_reply": "2024-09-26T14:51:06.328546Z" + "iopub.execute_input": "2024-09-26T16:46:44.624682Z", + "iopub.status.busy": "2024-09-26T16:46:44.624489Z", + "iopub.status.idle": "2024-09-26T16:46:44.829712Z", + "shell.execute_reply": "2024-09-26T16:46:44.829014Z" } }, "outputs": [ @@ -2383,10 +2383,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:06.331161Z", - "iopub.status.busy": "2024-09-26T14:51:06.330733Z", - "iopub.status.idle": "2024-09-26T14:51:06.339030Z", - "shell.execute_reply": "2024-09-26T14:51:06.338452Z" + "iopub.execute_input": "2024-09-26T16:46:44.831991Z", + "iopub.status.busy": "2024-09-26T16:46:44.831631Z", + "iopub.status.idle": "2024-09-26T16:46:44.840131Z", + "shell.execute_reply": "2024-09-26T16:46:44.839560Z" } }, "outputs": [ @@ -2411,47 +2411,47 @@ " \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, @@ -2472,10 +2472,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:06.340715Z", - "iopub.status.busy": "2024-09-26T14:51:06.340434Z", - "iopub.status.idle": "2024-09-26T14:51:06.519948Z", - "shell.execute_reply": "2024-09-26T14:51:06.519447Z" + "iopub.execute_input": "2024-09-26T16:46:44.842097Z", + "iopub.status.busy": "2024-09-26T16:46:44.841647Z", + "iopub.status.idle": "2024-09-26T16:46:45.015713Z", + "shell.execute_reply": "2024-09-26T16:46:45.015126Z" } }, "outputs": [ @@ -2515,10 +2515,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:06.522000Z", - "iopub.status.busy": "2024-09-26T14:51:06.521573Z", - "iopub.status.idle": "2024-09-26T14:51:06.526439Z", - "shell.execute_reply": "2024-09-26T14:51:06.525860Z" + "iopub.execute_input": "2024-09-26T16:46:45.017578Z", + "iopub.status.busy": "2024-09-26T16:46:45.017162Z", + "iopub.status.idle": "2024-09-26T16:46:45.021861Z", + "shell.execute_reply": "2024-09-26T16:46:45.021311Z" }, "nbsphinx": "hidden" }, @@ -2555,46 +2555,84 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01a52525efc245dbbd179fb0a46c9b73": { - "model_module": "@jupyter-widgets/controls", + "0615152255504030adeb967c707e7eca": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "047ffcbda41946e19a08ad3b61bf841f": { + "06b16b9d750441d08a55ba65990da75c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_39045bc38857424e8dd68bde536fe596", - "placeholder": "​", - "style": "IPY_MODEL_edbabb45d56b40b7a43858d7ec51dcae", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4e064a950004422e9793c7aa70deeb69", + "IPY_MODEL_0d9825bba69d4c9a90b0f711f057aa91", + "IPY_MODEL_e6eeb0f18d5342c7bfb2162167342fdb" + ], + "layout": "IPY_MODEL_20a37aa330ef40879ccd861ffeb2d77e", "tabbable": null, - "tooltip": null, - "value": "100%" + "tooltip": null } }, - "0944754e93274569967afe6d608ae5bd": { + "07f0cc4d1f4c48e99b208c9766cdd9dd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2612,33 +2650,48 @@ "text_color": null } }, - "0ec72a2a0065496b8b3b6ff239cb643d": { + "08583d3e3a0c448fb3615b125f05ee1e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0bed948828f141bca1ebc19b7c3ee31a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_22cbf5fe8ee943da90f0599eabdc47e9", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_1fd121ab479e40598a964ef1fe640df7", + "layout": "IPY_MODEL_d7f8a17831a8490385be98caaf69cc9e", + "placeholder": "​", + "style": "IPY_MODEL_6c87c46fe33948dd8347a75315145f72", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "100%" } }, - "0fc0d5f5c5fd4be8b001f46a67af29e6": { + "0cc4d92fb4044144b93e36fc005c9b57": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2691,7 +2744,25 @@ "width": null } }, - "0fefa1fafea24c19b8f8733109f29d90": { + "0d7259ccb2014d6e89b9d45abcaffdb4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0d9825bba69d4c9a90b0f711f057aa91": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2707,53 +2778,41 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_be38efba8d3b4a918b3d7486ef513b34", - "max": 2.0, + "layout": "IPY_MODEL_e13ece2f21e645f39a334fc1327f6255", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_2a6efe50b16d4bb4b79ecb580262a865", + "style": "IPY_MODEL_653426bf8e1847bf9f5498114910616f", "tabbable": null, "tooltip": null, - "value": 2.0 - } - }, - "10f1631d161c449eb8c661cb78376e34": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": 60000.0 } }, - "122c68d343034072a896523a1a6f59da": { + "0e3bcece048b4a29932526acdaec4e78": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4b07743ac3674137b27e138913647bd4", + "IPY_MODEL_8498411df0f144e2bef7bcba62fe928e", + "IPY_MODEL_17c264c8f3b9497392f244af3798dedf" + ], + "layout": "IPY_MODEL_3b027b03623b43ac92f91ec0db28cd09", + "tabbable": null, + "tooltip": null } }, - "141a122f5b984ea4b8d354438a45f4ac": { + "0e7b785e782d4294ab4157d8d7f8ed83": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2806,31 +2865,7 @@ "width": null } }, - "157b0b5de92c4dd39861100e0048c0b7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_46affc3b26cb4f05ba2a6b4d66e4a233", - "IPY_MODEL_e504d10254784e0fb8866b5539c1da25", - "IPY_MODEL_5e794a3967d7453396cb620ce1a5277f" - ], - "layout": "IPY_MODEL_b707e815ff5841ea8e23fc04fa7ff454", - "tabbable": null, - "tooltip": null - } - }, - "1af334042da749e9a2368c61aae4462f": { + "0efa586eba474bde8e872c4e1050d5f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2883,23 +2918,30 @@ "width": null } }, - "1af3d41b10be4701a4e58df18c1a38f8": { + "0f00fb4673354831ac246ea7823f56c9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_db5fae1c4ed24b4d84db9673debe1849", + "placeholder": "​", + "style": "IPY_MODEL_73d2026e64ed488c833fda372426273e", + "tabbable": null, + "tooltip": null, + "value": "Computing checksums: 100%" } }, - "1b540d6d72a748d0ac6d30324dc37e51": { + "10a58866c782478991eebcd95239d6d9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2917,30 +2959,33 @@ "text_color": null } }, - "1ee09afeb3644ffaa25df19feb6d7756": { + "13c48dc4b3ea4c4e9f4129d1fc35a62d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a199cd7e7b8c46dea72b37e895857807", - "placeholder": "​", - "style": "IPY_MODEL_d0d8518db2324e00900b4cc97bf83cba", + "layout": "IPY_MODEL_c7140224e2014a9f86018b2a80db9349", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1919595d2502420583ae6188632c1d18", "tabbable": null, "tooltip": null, - "value": " 10000/10000 [00:00<00:00, 246828.30 examples/s]" + "value": 10000.0 } }, - "1f4aff5948cf4d0186015296605c7196": { + "150680861ad44d349907508491bfe1e4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2993,41 +3038,54 @@ "width": null } }, - "1fd121ab479e40598a964ef1fe640df7": { + "152c7e3ade014d50839d1407d6baeb13": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f46fc8a56a0140628ce3b129a42d4d00", + "IPY_MODEL_cc44de893c454e9984744278aef97f45", + "IPY_MODEL_978a26bf6ea54d28bb363924fea9ad68" + ], + "layout": "IPY_MODEL_7b372ec9dbea4c5eab84315544b3186d", + "tabbable": null, + "tooltip": null } }, - "20c8eedf90bb40d392876c954589ff68": { + "156da93ea6724a9891da8967c6848587": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_473ee3df22174639aa2fb5a0bd0f2bf2", + "placeholder": "​", + "style": "IPY_MODEL_bd2b67ddccbc4fdaaea0035ffeb51e52", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:49<00:00, 1124.68it/s]" } }, - "22cbf5fe8ee943da90f0599eabdc47e9": { + "165f0e32eb9940e59f9f946b22aaa1bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3080,7 +3138,7 @@ "width": null } }, - "23438682b36c4a25982847f0236795a6": { + "16b101e9d5204f4983a48d53bfb6ef2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3133,32 +3191,14 @@ "width": null } }, - "26577822b8d84e4f976ede8bcd034275": { - "model_module": "@jupyter-widgets/controls", + "16b329ee1b904b20929be1a410d80ca3": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "27d69304db224bf69131404f9ec805dd": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", @@ -3204,41 +3244,48 @@ "width": null } }, - "2a62f69b0c2547d89c82d6582f304d32": { + "17c264c8f3b9497392f244af3798dedf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_16b329ee1b904b20929be1a410d80ca3", + "placeholder": "​", + "style": "IPY_MODEL_76521fcf4499458b8f8f1e422e450d73", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 63.78it/s]" } }, - "2a6efe50b16d4bb4b79ecb580262a865": { + "1916266332094832a5967cb9bf13df10": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2b2f57b523f849bbb599cc612b663ba6": { + "1919595d2502420583ae6188632c1d18": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3254,43 +3301,83 @@ "description_width": "" } }, - "2f87fcdad766443d8c4bca6afb07dd5a": { + "1b69dec79acc4f3993aa0744d8c32ef2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_62e9094b2bb5424fb496d88b57648bc2", + "placeholder": "​", + "style": "IPY_MODEL_60b4c0b99d334f02aad9eb20e84a4cf8", + "tabbable": null, + "tooltip": null, + "value": " 5.18M/5.18M [00:00<00:00, 28.6MB/s]" } }, - "309f00f07977438688cb2e2a138942ee": { - "model_module": "@jupyter-widgets/controls", + "20a37aa330ef40879ccd861ffeb2d77e": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "31aedd70a4c245c99b7449fba431ce39": { + "256a75aad92141fdbbc2bfacc1ec5a66": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3308,7 +3395,7 @@ "text_color": null } }, - "37cb8925a1ba4ade99a9002b6b26e8df": { + "259a364c31bf46d482e138d0d9607b1d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3323,15 +3410,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_86ca6bb1495e4902b7bab635f193c7b5", + "layout": "IPY_MODEL_a67d87241bce4f91bb13e7d23a32a198", "placeholder": "​", - "style": "IPY_MODEL_c8b6019d0f094ef6b63a722264c996a6", + "style": "IPY_MODEL_5a44bdf59d20444eb868f8e2c100e178", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 61.07it/s]" + "value": " 30.9M/30.9M [00:00<00:00, 42.0MB/s]" } }, - "3814212d72544fd898cb1f84a4da144b": { + "2675edd16bff40b0b42c8daeff5f5bf0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3347,33 +3434,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a7d9118a0a624247afaec6d0c70354df", - "max": 9015.0, + "layout": "IPY_MODEL_38c3afc197464cc09af95bd9849771c3", + "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_01a52525efc245dbbd179fb0a46c9b73", + "style": "IPY_MODEL_c626e7c1959e4543bf621cd4e48a04b5", "tabbable": null, "tooltip": null, - "value": 9015.0 - } - }, - "38e12e1caf544c0db7e643ca4333c637": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "value": 40.0 } }, - "39045bc38857424e8dd68bde536fe596": { + "2b3b3a12683342b7b1f3e4a7c16ff834": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3426,7 +3497,98 @@ "width": null } }, - "40c57ff589b84713af0069686d77f3b8": { + "304c8861034b44bbba4a12de67849cfb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3260b35b84244fe185eec3555f28011b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_510509e050314274aae1fd611e9e0055", + "placeholder": "​", + "style": "IPY_MODEL_fc0dbc5ad67f44d1970ac151d319ba0e", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 61.99it/s]" + } + }, + "330d908dc536412297733ff7acf50107": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_9f02a1dd3c664bfba8e685b4d2f82b34", + "IPY_MODEL_ddd88fe1f1a14aeabe63b7876fcebfbe", + "IPY_MODEL_d40d4080e5904c30908beea6c18b3f84" + ], + "layout": "IPY_MODEL_d3e45ec46ce344f091c4ff564f4fc634", + "tabbable": null, + "tooltip": null + } + }, + "33b768a7f4294ffeab6f702ca4b71d4c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d688f38225b4426ca69152695ceda860", + "max": 30931277.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_bf698e9ccdfc47d5a94fb2de5f578593", + "tabbable": null, + "tooltip": null, + "value": 30931277.0 + } + }, + "388bd680a9a044b6b3414838cfd94a55": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3479,7 +3641,7 @@ "width": null } }, - "41752bd19eff4ad7bd7f964db91e4397": { + "38c3afc197464cc09af95bd9849771c3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3532,7 +3694,7 @@ "width": null } }, - "43636151bcde46648bb7aaa89ccb8a51": { + "3908248ea2ec46ac809cab2a57825a21": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3585,30 +3747,63 @@ "width": null } }, - "438f1faf1add4ca0bf523ed0c6de324e": { + "39ecdb0609d94bfcb77249dccd8e7b0a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3a01430bac104582a0152f66469ff522": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3a77faf9cb2646f79de794a3636a0bdf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7a0e1f1ff0db4d01b7ee26a773a3e838", - "placeholder": "​", - "style": "IPY_MODEL_122c68d343034072a896523a1a6f59da", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a511b79056d34849a3da28979d986687", + "IPY_MODEL_5f213eb6e62d4ae091344ea7807998c5", + "IPY_MODEL_156da93ea6724a9891da8967c6848587" + ], + "layout": "IPY_MODEL_c3bd4b3cde4a416e8291af2ebeb6721e", "tabbable": null, - "tooltip": null, - "value": "Downloading readme: 100%" + "tooltip": null } }, - "442d008a589d43729e86f45feea617a0": { + "3abf54c4e5704bc5aef6294d9a245943": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3661,7 +3856,7 @@ "width": null } }, - "44350a751399489a9b7ac94b4aa0544e": { + "3b027b03623b43ac92f91ec0db28cd09": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3714,97 +3909,7 @@ "width": null } }, - "4472b003fd7d40f4b054d6725929c2e0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6c8fe2efdded46d4846420593a00bcd9", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_acc0b8fed3ec45c2b61e4cbdd87c0d3f", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "466806dd912b4f9da060546451df881b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5171002b8b9b40e58bc90a21fd7d4fce", - "placeholder": "​", - "style": "IPY_MODEL_a1491d109915441ca6418a110cd3b606", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "4685982b10924f9aaf548d58e65e7fff": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "46affc3b26cb4f05ba2a6b4d66e4a233": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1af334042da749e9a2368c61aae4462f", - "placeholder": "​", - "style": "IPY_MODEL_8fbebe50e8c94c68b5af42bcaa77c458", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "46d058b0dc32483b92ffc747203adfbb": { + "3f1f383ad1b34764bca27b83cec7251c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3822,71 +3927,7 @@ "text_color": null } }, - "48ba164541954d109266e55290018b2b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_cb02675b1ffa460abaf2766e247e9e76", - "IPY_MODEL_f5bdc90f13ac4085a6c87c3f16c1db23", - "IPY_MODEL_8e5227fef7bb4af398c777c479c081d2" - ], - "layout": "IPY_MODEL_442d008a589d43729e86f45feea617a0", - "tabbable": null, - "tooltip": null - } - }, - "49a7ec98f7764b44b81eb30169e07a30": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_cf2c49458b23448bbd90dd22355dc406", - "IPY_MODEL_0fefa1fafea24c19b8f8733109f29d90", - "IPY_MODEL_e1863acf55ff485abde9e3dd42efb403" - ], - "layout": "IPY_MODEL_6f45d2ba1fe14a26b1dd8d8cde2b3404", - "tabbable": null, - "tooltip": null - } - }, - "49ff26f36c4046ccaa23af5a0e1bd79a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4afd56e1be684a4ea49cf9bd94feb822": { + "43015204ba52471381708cc6a3897af0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3939,74 +3980,7 @@ "width": null } }, - "4c60fd9ec0a14e40806e9a0ac5d515de": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4d78c4c4290346e58aeff13d1e2863f2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e1f00c2ba7fa42879c2ae82deb9ae36b", - "placeholder": "​", - "style": "IPY_MODEL_10f1631d161c449eb8c661cb78376e34", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.27it/s]" - } - }, - "4e00ed69c8994d9990e59ec728ec2a31": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e5d17f8bb48c489d84f85f8b0e82285e", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2b2f57b523f849bbb599cc612b663ba6", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "4e60241fc8fc4199a601cbd4c617c5e6": { + "4334d6adca6d419fa7e126021ea11a03": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4059,30 +4033,7 @@ "width": null } }, - "4f30a386d77a45318659e949f29dd3b0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b1371d623aa5489ca6e8e84cd59f53d7", - "placeholder": "​", - "style": "IPY_MODEL_31aedd70a4c245c99b7449fba431ce39", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "5171002b8b9b40e58bc90a21fd7d4fce": { + "473ee3df22174639aa2fb5a0bd0f2bf2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4132,349 +4083,10 @@ "right": null, "top": null, "visibility": null, - "width": null - } - }, - "517b83c613bb49c9ab0cd319caf77fa4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_438f1faf1add4ca0bf523ed0c6de324e", - "IPY_MODEL_3814212d72544fd898cb1f84a4da144b", - "IPY_MODEL_bf819d4aebc746d595ddad41e330f6dd" - ], - "layout": "IPY_MODEL_959554efc55740f0a1054b9bb02b35ab", - "tabbable": null, - "tooltip": null - } - }, - "52f3a03aa4994380b8b138695e6993e3": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "5407b52abb3543878bac58e171136ce5": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "545ab14d8bdc4758954170fd6a6c7f2e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d16d5292876e41bbb948c7172eb1b022", - "placeholder": "​", - "style": "IPY_MODEL_9d21835857024eaca20e6c75bc8f65eb", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "54d86e58cead41e6ba4d5f91406ed1b6": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "554fe55159064f65be3faeef3e587efa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_44350a751399489a9b7ac94b4aa0544e", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_68ce90ceb8c5418fa6b01cf5ea89177d", - "tabbable": null, - "tooltip": null, - "value": 10000.0 - } - }, - "5568c1b377ee44e2baadcd2713472be4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "5a0315cb61bf4e1b9c52127d29ddf405": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fd18df91f07f4dd2951fcd6f5d643d2e", - "placeholder": "​", - "style": "IPY_MODEL_2a62f69b0c2547d89c82d6582f304d32", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 64.27it/s]" - } - }, - "5a31bc5cecd243f3b3fc5adfc57abc50": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "5e114e4103d94679910f2192af574f94": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c2007b76905c425eb8c5793d5064dd23", - "IPY_MODEL_eb1eb741913649deae955849ad2ff5e8", - "IPY_MODEL_8bf2df37d8174d2faa0d068cc68a2fef" - ], - "layout": "IPY_MODEL_dd4c58e0fd6948c49326bb0bb920e8c2", - "tabbable": null, - "tooltip": null - } - }, - "5e37b90b12544b039698dff9bf1b1167": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b832eb74e64e4a219cb1a39e71503899", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6cad31ec6e694b91b3e194e785d6cc13", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "width": null } }, - "5e794a3967d7453396cb620ce1a5277f": { + "4b07743ac3674137b27e138913647bd4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4489,33 +4101,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_aee54ae8933b42d09ef6de4b7c8e9cee", + "layout": "IPY_MODEL_165f0e32eb9940e59f9f946b22aaa1bf", "placeholder": "​", - "style": "IPY_MODEL_919e7f7fa04b447e977f5978a6008575", + "style": "IPY_MODEL_8c60825feef64aa4a292fcb73efacdd9", "tabbable": null, "tooltip": null, - "value": " 30.9M/30.9M [00:00<00:00, 48.5MB/s]" - } - }, - "5f57350bd59941d3b3be376c1389de24": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": "100%" } }, - "60160531292f49f6912a5e7fa5c1cd4a": { + "4c5c9bab82cf4d7bb1787ca880626761": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4530,94 +4124,86 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_047ffcbda41946e19a08ad3b61bf841f", - "IPY_MODEL_4472b003fd7d40f4b054d6725929c2e0", - "IPY_MODEL_bdf9a01c9a4d4a6186410cdd21003fa3" + "IPY_MODEL_c9b78c79f9ec4c21b9443be9011f71b8", + "IPY_MODEL_d994309e4ee24291984f6e27719ed798", + "IPY_MODEL_543ec1550b174116a83c81d026cc25fc" ], - "layout": "IPY_MODEL_27d69304db224bf69131404f9ec805dd", + "layout": "IPY_MODEL_43015204ba52471381708cc6a3897af0", "tabbable": null, "tooltip": null } }, - "60b1a0d2d540411383054bcc09a9902a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "62dcb057ad134472b2f7163715a51417": { + "4e064a950004422e9793c7aa70deeb69": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_23438682b36c4a25982847f0236795a6", - "max": 5175617.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5a31bc5cecd243f3b3fc5adfc57abc50", + "layout": "IPY_MODEL_725cf6b9493c4d97a794d0c679aa4b36", + "placeholder": "​", + "style": "IPY_MODEL_256a75aad92141fdbbc2bfacc1ec5a66", "tabbable": null, "tooltip": null, - "value": 5175617.0 + "value": "Map (num_proc=4): 100%" } }, - "63fd06eb481e40ff939a4af2d7ec11e1": { + "501fbbc332d142e1a890a35622ae19d7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_16b101e9d5204f4983a48d53bfb6ef2f", + "placeholder": "​", + "style": "IPY_MODEL_10a58866c782478991eebcd95239d6d9", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 60.32it/s]" } }, - "68ce90ceb8c5418fa6b01cf5ea89177d": { + "50b5887bf4c344448a941ef696740bfb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ac45d17c69d34562abc6d440b8927476", + "IPY_MODEL_8880ccfb8cfb4b79bd29ebf7d60fe54d", + "IPY_MODEL_d6576c007df04cb69b189749179d99bc" + ], + "layout": "IPY_MODEL_f1d92e4fad884e60ae51f8e53d0a9fe6", + "tabbable": null, + "tooltip": null } }, - "6bad8602531348f08c403c73528818d1": { + "510509e050314274aae1fd611e9e0055": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4670,7 +4256,53 @@ "width": null } }, - "6c8fe2efdded46d4846420593a00bcd9": { + "514ea03743d344d09972d77a32c7fe4e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5513cd1c30484a6f8b2a97e19a651ab0", + "placeholder": "​", + "style": "IPY_MODEL_782fbc8cafdc4589b274f5da20c87d79", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" + } + }, + "543ec1550b174116a83c81d026cc25fc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3abf54c4e5704bc5aef6294d9a245943", + "placeholder": "​", + "style": "IPY_MODEL_7a545c6253c44f2f9055a180a3261ecf", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 67.40it/s]" + } + }, + "5513cd1c30484a6f8b2a97e19a651ab0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4723,23 +4355,31 @@ "width": null } }, - "6cad31ec6e694b91b3e194e785d6cc13": { + "552d50148a6144d18fe7fdebcb4c2a2f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d36f9da2d76f49ca94d84d03416aa07a", + "IPY_MODEL_6eaefb705976491799c97d41d27e2318", + "IPY_MODEL_3260b35b84244fe185eec3555f28011b" + ], + "layout": "IPY_MODEL_5a848363cac841518da6ebb2300325d3", + "tabbable": null, + "tooltip": null } }, - "6f45d2ba1fe14a26b1dd8d8cde2b3404": { + "556e4ef19cb94604aa405d62dbf7da70": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4792,7 +4432,7 @@ "width": null } }, - "77e4c9ec41b8452c8963af0b77b5555f": { + "56a181d584f247a798b2fa5b261b7f49": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4807,92 +4447,52 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_8838da60b00243caa8a1c207f1f8be22", - "IPY_MODEL_b934aa90c70b4cfdae426a9ca50e61a4", - "IPY_MODEL_793b99f043774f62a311dedf0c93aba0" + "IPY_MODEL_a53cf182147b41ca9bc22e924e74e42c", + "IPY_MODEL_2675edd16bff40b0b42c8daeff5f5bf0", + "IPY_MODEL_501fbbc332d142e1a890a35622ae19d7" ], - "layout": "IPY_MODEL_83e0553f47d84eca8ffc9341907dd047", + "layout": "IPY_MODEL_91d2a863861b4e0f903b9413d3ed6b39", "tabbable": null, "tooltip": null } }, - "793b99f043774f62a311dedf0c93aba0": { + "5898a17eff104f3b8b3b7c0757f5ae54": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ce14eb0a9f94406f9670f2a1dc1345c7", - "placeholder": "​", - "style": "IPY_MODEL_aafc8b9a1a3a4506b5483ae08647da91", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:00<00:00, 282979.24 examples/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "7a0e1f1ff0db4d01b7ee26a773a3e838": { - "model_module": "@jupyter-widgets/base", + "5a44bdf59d20444eb868f8e2c100e178": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "7de683e49bbf4f65959e300da53047ed": { + "5a848363cac841518da6ebb2300325d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4945,30 +4545,69 @@ "width": null } }, - "7ef0c36f65034c0f9d5b161441b6e47c": { + "5dca4c3a272f4e5b98860d48cd7a7768": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "5f213eb6e62d4ae091344ea7807998c5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c91d42b9d1c342cf8093443f4eaa5204", - "placeholder": "​", - "style": "IPY_MODEL_20c8eedf90bb40d392876c954589ff68", + "layout": "IPY_MODEL_0e7b785e782d4294ab4157d8d7f8ed83", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_876f5638038442208da1fc1fcc89b85e", "tabbable": null, "tooltip": null, - "value": "Generating test split: 100%" + "value": 60000.0 + } + }, + "60b4c0b99d334f02aad9eb20e84a4cf8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "83e0553f47d84eca8ffc9341907dd047": { + "62e9094b2bb5424fb496d88b57648bc2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5021,7 +4660,49 @@ "width": null } }, - "86ca6bb1495e4902b7bab635f193c7b5": { + "6402088307ca4ebeb0e880a6da30aa44": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a961cd5b3a9f46fab76d3b67117504c2", + "max": 5175617.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c775fa235666491088a75b6255f32e3f", + "tabbable": null, + "tooltip": null, + "value": 5175617.0 + } + }, + "653426bf8e1847bf9f5498114910616f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "683dd50849a54efd82f6bcbfedf01e71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5074,123 +4755,25 @@ "width": null } }, - "8838da60b00243caa8a1c207f1f8be22": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e34ca479b1cd4338a1b034c9bdb6cdde", - "placeholder": "​", - "style": "IPY_MODEL_5f57350bd59941d3b3be376c1389de24", - "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" - } - }, - "8bf2df37d8174d2faa0d068cc68a2fef": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9a129d278f5249b2ba66adbc4c7981b4", - "placeholder": "​", - "style": "IPY_MODEL_cbb874850a0f4b8585830f781d02f6c7", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 6394.51 examples/s]" - } - }, - "8c99cd03c2204dd69d220e1911ef407b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4f30a386d77a45318659e949f29dd3b0", - "IPY_MODEL_e0c002bd6e8c4598837703a0b54dfdd7", - "IPY_MODEL_37cb8925a1ba4ade99a9002b6b26e8df" - ], - "layout": "IPY_MODEL_5407b52abb3543878bac58e171136ce5", - "tabbable": null, - "tooltip": null - } - }, - "8e5227fef7bb4af398c777c479c081d2": { + "6bdc6710af8142aba7c58c6798148a76": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_40c57ff589b84713af0069686d77f3b8", - "placeholder": "​", - "style": "IPY_MODEL_1b540d6d72a748d0ac6d30324dc37e51", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:53<00:00, 1198.28it/s]" - } - }, - "8f801276525d4e939b2f6a4ae43acb97": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_dd7812d3359f4747a8d626fa5a9094fb", - "placeholder": "​", - "style": "IPY_MODEL_26577822b8d84e4f976ede8bcd034275", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 59.80it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "8fbebe50e8c94c68b5af42bcaa77c458": { + "6c87c46fe33948dd8347a75315145f72": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5208,25 +4791,33 @@ "text_color": null } }, - "919e7f7fa04b447e977f5978a6008575": { + "6eaefb705976491799c97d41d27e2318": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_683dd50849a54efd82f6bcbfedf01e71", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3a01430bac104582a0152f66469ff522", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "959554efc55740f0a1054b9bb02b35ab": { + "725cf6b9493c4d97a794d0c679aa4b36": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5279,7 +4870,25 @@ "width": null } }, - "9a129d278f5249b2ba66adbc4c7981b4": { + "72e2143985fe46f499ca9310aa86d7cf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "73cad09577cd4329a162f8f43e7f6529": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5332,54 +4941,7 @@ "width": null } }, - "9b43cbe4d995486aa4d260f3e1778a5b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ccdd8d1027864d7697480a9f400c528c", - "placeholder": "​", - "style": "IPY_MODEL_2f87fcdad766443d8c4bca6afb07dd5a", - "tabbable": null, - "tooltip": null, - "value": " 5.18M/5.18M [00:00<00:00, 35.9MB/s]" - } - }, - "9c97151f1d7f4a49a3e2278cddb3c604": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_fb35e5a84e5942e59aa0f8e4a6d4b045", - "IPY_MODEL_0ec72a2a0065496b8b3b6ff239cb643d", - "IPY_MODEL_4d78c4c4290346e58aeff13d1e2863f2" - ], - "layout": "IPY_MODEL_e2ddf40e09984deca4602e0c72e8bfd6", - "tabbable": null, - "tooltip": null - } - }, - "9d21835857024eaca20e6c75bc8f65eb": { + "73d2026e64ed488c833fda372426273e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5397,33 +4959,7 @@ "text_color": null } }, - "9d4ba4757b8d48bba46623ad8f4b39a3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_54d86e58cead41e6ba4d5f91406ed1b6", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_38e12e1caf544c0db7e643ca4333c637", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "a02cbe5a9ae843ff95023851f56408fe": { + "74187c11715545dfbb571e697ad3074b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5476,7 +5012,67 @@ "width": null } }, - "a1491d109915441ca6418a110cd3b606": { + "76521fcf4499458b8f8f1e422e450d73": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "782fbc8cafdc4589b274f5da20c87d79": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "788c289a49054234a89ef72aa47dbb50": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b4ade25b47e1457e9269900f0df62d61", + "IPY_MODEL_6402088307ca4ebeb0e880a6da30aa44", + "IPY_MODEL_1b69dec79acc4f3993aa0744d8c32ef2" + ], + "layout": "IPY_MODEL_0615152255504030adeb967c707e7eca", + "tabbable": null, + "tooltip": null + } + }, + "7a545c6253c44f2f9055a180a3261ecf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5494,7 +5090,7 @@ "text_color": null } }, - "a199cd7e7b8c46dea72b37e895857807": { + "7b372ec9dbea4c5eab84315544b3186d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5547,25 +5143,7 @@ "width": null } }, - "a3d204cf9a5a441d9ce00a62738821f3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a4367ae0bcb046beb0aa7fa55ae59b6d": { + "7fd68800618a439c92872f907d74563e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5618,7 +5196,33 @@ "width": null } }, - "a7d9118a0a624247afaec6d0c70354df": { + "8498411df0f144e2bef7bcba62fe928e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_150680861ad44d349907508491bfe1e4", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_eb036f5b69cb4a91adf36df7f4d466a6", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "84c334be19a8458598d52bab4fff8168": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5671,7 +5275,7 @@ "width": null } }, - "aafc8b9a1a3a4506b5483ae08647da91": { + "858ad6e310a44429a4ccbefd0a93534b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5689,7 +5293,7 @@ "text_color": null } }, - "acc0b8fed3ec45c2b61e4cbdd87c0d3f": { + "876f5638038442208da1fc1fcc89b85e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -5705,7 +5309,33 @@ "description_width": "" } }, - "aee54ae8933b42d09ef6de4b7c8e9cee": { + "8880ccfb8cfb4b79bd29ebf7d60fe54d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a7425ae868e14eafae4bfb0468cec374", + "max": 9015.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_defede3efe8b41b3ba6660edba087f85", + "tabbable": null, + "tooltip": null, + "value": 9015.0 + } + }, + "8957a61b05ae4e12aca0eb2a5e277a0b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5758,30 +5388,25 @@ "width": null } }, - "afdc4d351896438cb7e9760348393ddc": { + "8c60825feef64aa4a292fcb73efacdd9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6bad8602531348f08c403c73528818d1", - "placeholder": "​", - "style": "IPY_MODEL_0944754e93274569967afe6d608ae5bd", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b1371d623aa5489ca6e8e84cd59f53d7": { + "900f06ca89244862b1783effd7d731d6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5834,60 +5459,33 @@ "width": null } }, - "b42c916027f0431eb2b8bccc86357bf7": { - "model_module": "@jupyter-widgets/base", + "909c9effd0a04202a36f69334a1adb92": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_db7b4d566c2c4c74a93bf056d2eed6a7", + "max": 2.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f6c771c77fcc45a4a4178d8748beeb3c", + "tabbable": null, + "tooltip": null, + "value": 2.0 } }, - "b707e815ff5841ea8e23fc04fa7ff454": { + "91d2a863861b4e0f903b9413d3ed6b39": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5940,7 +5538,30 @@ "width": null } }, - "b826bec48c45487bbcae63716ac684fb": { + "9345bef96daa4abfb55fa1601eccb32b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9b26696cd88e4138bbe052cd32cbbd4f", + "placeholder": "​", + "style": "IPY_MODEL_3f1f383ad1b34764bca27b83cec7251c", + "tabbable": null, + "tooltip": null, + "value": " 10000/10000 [00:00<00:00, 246652.67 examples/s]" + } + }, + "959ae504cb01405781572c8c3e5f8f73": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5993,86 +5614,48 @@ "width": null } }, - "b832eb74e64e4a219cb1a39e71503899": { - "model_module": "@jupyter-widgets/base", + "95dff653e0bb4198be1ecfaf08164b44": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b934aa90c70b4cfdae426a9ca50e61a4": { + "978a26bf6ea54d28bb363924fea9ad68": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1f4aff5948cf4d0186015296605c7196", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_bf1593492574460ca63450ff1667241f", + "layout": "IPY_MODEL_adb5aee751224ee8a1f228414df1dd9c", + "placeholder": "​", + "style": "IPY_MODEL_08583d3e3a0c448fb3615b125f05ee1e", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": " 40/40 [00:00<00:00, 63.38it/s]" } }, - "badd7c549d834045856ae809d48d9fa6": { + "981864ffd419481399deca7093834a2a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6125,30 +5708,7 @@ "width": null } }, - "bdf9a01c9a4d4a6186410cdd21003fa3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_41752bd19eff4ad7bd7f964db91e4397", - "placeholder": "​", - "style": "IPY_MODEL_60b1a0d2d540411383054bcc09a9902a", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 58.52it/s]" - } - }, - "be38efba8d3b4a918b3d7486ef513b34": { + "9b26696cd88e4138bbe052cd32cbbd4f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6201,23 +5761,25 @@ "width": null } }, - "bf1593492574460ca63450ff1667241f": { + "9db32a78998341e490d0bfe9165b1cde": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "bf819d4aebc746d595ddad41e330f6dd": { + "9f02a1dd3c664bfba8e685b4d2f82b34": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6232,39 +5794,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d89cbf3e824f490b81fa19d70eb0784a", + "layout": "IPY_MODEL_0efa586eba474bde8e872c4e1050d5f7", "placeholder": "​", - "style": "IPY_MODEL_63fd06eb481e40ff939a4af2d7ec11e1", + "style": "IPY_MODEL_1916266332094832a5967cb9bf13df10", "tabbable": null, "tooltip": null, - "value": " 9.02k/9.02k [00:00<00:00, 960kB/s]" + "value": "Generating train split: 100%" } }, - "c01e10af9cd04c4c90430d0afbaa6da0": { + "a442f3280e014780ac27c505772845cd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_466806dd912b4f9da060546451df881b", - "IPY_MODEL_5e37b90b12544b039698dff9bf1b1167", - "IPY_MODEL_8f801276525d4e939b2f6a4ae43acb97" - ], - "layout": "IPY_MODEL_ca16aad0772747da8b6cf03b6abc3643", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "c2007b76905c425eb8c5793d5064dd23": { + "a511b79056d34849a3da28979d986687": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6279,15 +5833,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a4367ae0bcb046beb0aa7fa55ae59b6d", + "layout": "IPY_MODEL_3908248ea2ec46ac809cab2a57825a21", "placeholder": "​", - "style": "IPY_MODEL_309f00f07977438688cb2e2a138942ee", + "style": "IPY_MODEL_07f0cc4d1f4c48e99b208c9766cdd9dd", "tabbable": null, "tooltip": null, - "value": "Map (num_proc=4): 100%" + "value": "100%" } }, - "c57636ad3dc54dc0926bb56946b10ab1": { + "a53cf182147b41ca9bc22e924e74e42c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6302,33 +5856,145 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b42c916027f0431eb2b8bccc86357bf7", + "layout": "IPY_MODEL_556e4ef19cb94604aa405d62dbf7da70", "placeholder": "​", - "style": "IPY_MODEL_4685982b10924f9aaf548d58e65e7fff", + "style": "IPY_MODEL_72e2143985fe46f499ca9310aa86d7cf", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 54.36it/s]" + "value": "100%" + } + }, + "a67d87241bce4f91bb13e7d23a32a198": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a7425ae868e14eafae4bfb0468cec374": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "c5b4b8f4759d4bac96dbdbe270c10816": { + "a7c9ce9c07064710be69384ca6979edc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_514ea03743d344d09972d77a32c7fe4e", + "IPY_MODEL_13c48dc4b3ea4c4e9f4129d1fc35a62d", + "IPY_MODEL_9345bef96daa4abfb55fa1601eccb32b" + ], + "layout": "IPY_MODEL_c182372db841430d9f4c29cdb63c0416", + "tabbable": null, + "tooltip": null } }, - "c8b6019d0f094ef6b63a722264c996a6": { + "a88fbadaab274cb49b7ed896c888e4b6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6346,7 +6012,7 @@ "text_color": null } }, - "c91d42b9d1c342cf8093443f4eaa5204": { + "a961cd5b3a9f46fab76d3b67117504c2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6399,7 +6065,7 @@ "width": null } }, - "c970c34254fb445a91f6a8570b2ee0a6": { + "ac45d17c69d34562abc6d440b8927476": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6414,15 +6080,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_43636151bcde46648bb7aaa89ccb8a51", + "layout": "IPY_MODEL_73cad09577cd4329a162f8f43e7f6529", "placeholder": "​", - "style": "IPY_MODEL_4c60fd9ec0a14e40806e9a0ac5d515de", + "style": "IPY_MODEL_858ad6e310a44429a4ccbefd0a93534b", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": "Downloading readme: 100%" } }, - "ca16aad0772747da8b6cf03b6abc3643": { + "adb5aee751224ee8a1f228414df1dd9c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6475,7 +6141,7 @@ "width": null } }, - "cb02675b1ffa460abaf2766e247e9e76": { + "b4ade25b47e1457e9269900f0df62d61": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6490,15 +6156,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_52f3a03aa4994380b8b138695e6993e3", + "layout": "IPY_MODEL_388bd680a9a044b6b3414838cfd94a55", "placeholder": "​", - "style": "IPY_MODEL_46d058b0dc32483b92ffc747203adfbb", + "style": "IPY_MODEL_5898a17eff104f3b8b3b7c0757f5ae54", "tabbable": null, "tooltip": null, - "value": "100%" + "value": "Downloading data: 100%" + } + }, + "b672bc6dd3974984b7a7b6bebe8959ce": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4334d6adca6d419fa7e126021ea11a03", + "placeholder": "​", + "style": "IPY_MODEL_0d7259ccb2014d6e89b9d45abcaffdb4", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 57.07it/s]" } }, - "cbb874850a0f4b8585830f781d02f6c7": { + "bd2b67ddccbc4fdaaea0035ffeb51e52": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6516,31 +6205,126 @@ "text_color": null } }, - "ccc0d279330845b8b34f60a57e76743f": { + "bf698e9ccdfc47d5a94fb2de5f578593": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c16a8793cbbe40a1ac2b15a9037fabd9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0bed948828f141bca1ebc19b7c3ee31a", + "IPY_MODEL_c2dd24dc859f4dcb9453449bac09534d", + "IPY_MODEL_b672bc6dd3974984b7a7b6bebe8959ce" + ], + "layout": "IPY_MODEL_900f06ca89244862b1783effd7d731d6", + "tabbable": null, + "tooltip": null + } + }, + "c182372db841430d9f4c29cdb63c0416": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c2dd24dc859f4dcb9453449bac09534d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_afdc4d351896438cb7e9760348393ddc", - "IPY_MODEL_9d4ba4757b8d48bba46623ad8f4b39a3", - "IPY_MODEL_5a0315cb61bf4e1b9c52127d29ddf405" - ], - "layout": "IPY_MODEL_e23cc3c422934dabbc7fa7851f6ab787", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_981864ffd419481399deca7093834a2a", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a442f3280e014780ac27c505772845cd", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 40.0 } }, - "ccdd8d1027864d7697480a9f400c528c": { + "c3bd4b3cde4a416e8291af2ebeb6721e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6593,31 +6377,23 @@ "width": null } }, - "cd9cd1e986f74424b60db3510b43826d": { + "c626e7c1959e4543bf621cd4e48a04b5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7ef0c36f65034c0f9d5b161441b6e47c", - "IPY_MODEL_554fe55159064f65be3faeef3e587efa", - "IPY_MODEL_1ee09afeb3644ffaa25df19feb6d7756" - ], - "layout": "IPY_MODEL_f8bd324861dd4e2ca40f79d16ca6ca8a", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "ce14eb0a9f94406f9670f2a1dc1345c7": { + "c7140224e2014a9f86018b2a80db9349": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6670,7 +6446,23 @@ "width": null } }, - "cf2c49458b23448bbd90dd22355dc406": { + "c775fa235666491088a75b6255f32e3f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c9b78c79f9ec4c21b9443be9011f71b8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6685,15 +6477,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0fc0d5f5c5fd4be8b001f46a67af29e6", + "layout": "IPY_MODEL_84c334be19a8458598d52bab4fff8168", "placeholder": "​", - "style": "IPY_MODEL_5568c1b377ee44e2baadcd2713472be4", + "style": "IPY_MODEL_ca3356f8aa3549c988e57f881ebb4f26", "tabbable": null, "tooltip": null, - "value": "Computing checksums: 100%" + "value": "100%" } }, - "d0d8518db2324e00900b4cc97bf83cba": { + "ca3356f8aa3549c988e57f881ebb4f26": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6711,7 +6503,7 @@ "text_color": null } }, - "d16d5292876e41bbb948c7172eb1b022": { + "cae6f31af2a640f6bcb2c87e7ed66781": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6764,7 +6556,95 @@ "width": null } }, - "d89cbf3e824f490b81fa19d70eb0784a": { + "cc44de893c454e9984744278aef97f45": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_dac045d1446b465fbaf4b6bf28f028b1", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d26c5cba80084451802d565928745bf7", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "d26c5cba80084451802d565928745bf7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "d28f34dd188a4bedb78aadba614f3e96": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ee7dcfe3d76a4bafa576315937b01a16", + "placeholder": "​", + "style": "IPY_MODEL_304c8861034b44bbba4a12de67849cfb", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "d36f9da2d76f49ca94d84d03416aa07a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2b3b3a12683342b7b1f3e4a7c16ff834", + "placeholder": "​", + "style": "IPY_MODEL_95dff653e0bb4198be1ecfaf08164b44", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "d3e45ec46ce344f091c4ff564f4fc634": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6817,7 +6697,30 @@ "width": null } }, - "dc9f46273db144d982f466b186d6ea8d": { + "d40d4080e5904c30908beea6c18b3f84": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f7c7e57c728147049f52ba691e408da8", + "placeholder": "​", + "style": "IPY_MODEL_9db32a78998341e490d0bfe9165b1cde", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:00<00:00, 287205.28 examples/s]" + } + }, + "d4db058689ca42958386a0f64cf412d9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -6832,16 +6735,57 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_c970c34254fb445a91f6a8570b2ee0a6", - "IPY_MODEL_62dcb057ad134472b2f7163715a51417", - "IPY_MODEL_9b43cbe4d995486aa4d260f3e1778a5b" + "IPY_MODEL_0f00fb4673354831ac246ea7823f56c9", + "IPY_MODEL_909c9effd0a04202a36f69334a1adb92", + "IPY_MODEL_efe22845beb449a58f2cc4d0a55a4321" ], - "layout": "IPY_MODEL_badd7c549d834045856ae809d48d9fa6", + "layout": "IPY_MODEL_cae6f31af2a640f6bcb2c87e7ed66781", "tabbable": null, "tooltip": null } }, - "dd4c58e0fd6948c49326bb0bb920e8c2": { + "d6576c007df04cb69b189749179d99bc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7fd68800618a439c92872f907d74563e", + "placeholder": "​", + "style": "IPY_MODEL_5dca4c3a272f4e5b98860d48cd7a7768", + "tabbable": null, + "tooltip": null, + "value": " 9.02k/9.02k [00:00<00:00, 1.10MB/s]" + } + }, + "d66678e7de914cb099b5b837d46979bf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d688f38225b4426ca69152695ceda860": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6894,7 +6838,7 @@ "width": null } }, - "dd7812d3359f4747a8d626fa5a9094fb": { + "d7f8a17831a8490385be98caaf69cc9e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6947,7 +6891,7 @@ "width": null } }, - "e0c002bd6e8c4598837703a0b54dfdd7": { + "d994309e4ee24291984f6e27719ed798": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -6963,40 +6907,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b826bec48c45487bbcae63716ac684fb", + "layout": "IPY_MODEL_74187c11715545dfbb571e697ad3074b", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_f241c4541bfc4e678ceec3ff46602200", + "style": "IPY_MODEL_39ecdb0609d94bfcb77249dccd8e7b0a", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "e1863acf55ff485abde9e3dd42efb403": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fc81a44ecf7c4ad1b851ab50894a6d71", - "placeholder": "​", - "style": "IPY_MODEL_c5b4b8f4759d4bac96dbdbe270c10816", - "tabbable": null, - "tooltip": null, - "value": " 2/2 [00:00<00:00, 584.78it/s]" - } - }, - "e1f00c2ba7fa42879c2ae82deb9ae36b": { + "dac045d1446b465fbaf4b6bf28f028b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7038,42 +6959,18 @@ "max_width": null, "min_height": null, "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "e20ec2fb456e4bb9bfb446110e53d341": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_545ab14d8bdc4758954170fd6a6c7f2e", - "IPY_MODEL_4e00ed69c8994d9990e59ec728ec2a31", - "IPY_MODEL_c57636ad3dc54dc0926bb56946b10ab1" - ], - "layout": "IPY_MODEL_141a122f5b984ea4b8d354438a45f4ac", - "tabbable": null, - "tooltip": null + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "e23cc3c422934dabbc7fa7851f6ab787": { + "db5fae1c4ed24b4d84db9673debe1849": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7126,7 +7023,7 @@ "width": null } }, - "e2ddf40e09984deca4602e0c72e8bfd6": { + "db7b4d566c2c4c74a93bf056d2eed6a7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7179,7 +7076,49 @@ "width": null } }, - "e34ca479b1cd4338a1b034c9bdb6cdde": { + "ddd88fe1f1a14aeabe63b7876fcebfbe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_959ae504cb01405781572c8c3e5f8f73", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f914aa3a0cc1445b81656d22ac9574b0", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "defede3efe8b41b3ba6660edba087f85": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e13ece2f21e645f39a334fc1327f6255": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7232,33 +7171,30 @@ "width": null } }, - "e504d10254784e0fb8866b5539c1da25": { + "e6eeb0f18d5342c7bfb2162167342fdb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4afd56e1be684a4ea49cf9bd94feb822", - "max": 30931277.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_1af3d41b10be4701a4e58df18c1a38f8", + "layout": "IPY_MODEL_e99ece1363c342c79e173d40cf560918", + "placeholder": "​", + "style": "IPY_MODEL_a88fbadaab274cb49b7ed896c888e4b6", "tabbable": null, "tooltip": null, - "value": 30931277.0 + "value": " 60000/60000 [00:11<00:00, 6407.92 examples/s]" } }, - "e5d17f8bb48c489d84f85f8b0e82285e": { + "e99ece1363c342c79e173d40cf560918": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7311,7 +7247,7 @@ "width": null } }, - "ea25312b42fa433294a739c7e0d5c2a9": { + "eb036f5b69cb4a91adf36df7f4d466a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -7327,93 +7263,107 @@ "description_width": "" } }, - "eb1eb741913649deae955849ad2ff5e8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a02cbe5a9ae843ff95023851f56408fe", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ea25312b42fa433294a739c7e0d5c2a9", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "edbabb45d56b40b7a43858d7ec51dcae": { - "model_module": "@jupyter-widgets/controls", + "ee7dcfe3d76a4bafa576315937b01a16": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "f241c4541bfc4e678ceec3ff46602200": { + "efe22845beb449a58f2cc4d0a55a4321": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8957a61b05ae4e12aca0eb2a5e277a0b", + "placeholder": "​", + "style": "IPY_MODEL_6bdc6710af8142aba7c58c6798148a76", + "tabbable": null, + "tooltip": null, + "value": " 2/2 [00:00<00:00, 591.46it/s]" } }, - "f5bdc90f13ac4085a6c87c3f16c1db23": { + "f16276c8203a4d9f9a7457266c1e666c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7de683e49bbf4f65959e300da53047ed", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_49ff26f36c4046ccaa23af5a0e1bd79a", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d28f34dd188a4bedb78aadba614f3e96", + "IPY_MODEL_33b768a7f4294ffeab6f702ca4b71d4c", + "IPY_MODEL_259a364c31bf46d482e138d0d9607b1d" + ], + "layout": "IPY_MODEL_f3866e54cbb646e0aa91281de9bbd202", "tabbable": null, - "tooltip": null, - "value": 60000.0 + "tooltip": null } }, - "f8bd324861dd4e2ca40f79d16ca6ca8a": { + "f1d92e4fad884e60ae51f8e53d0a9fe6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7466,30 +7416,7 @@ "width": null } }, - "fb35e5a84e5942e59aa0f8e4a6d4b045": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4e60241fc8fc4199a601cbd4c617c5e6", - "placeholder": "​", - "style": "IPY_MODEL_a3d204cf9a5a441d9ce00a62738821f3", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "fc81a44ecf7c4ad1b851ab50894a6d71": { + "f3866e54cbb646e0aa91281de9bbd202": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7542,7 +7469,46 @@ "width": null } }, - "fd18df91f07f4dd2951fcd6f5d643d2e": { + "f46fc8a56a0140628ce3b129a42d4d00": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0cc4d92fb4044144b93e36fc005c9b57", + "placeholder": "​", + "style": "IPY_MODEL_d66678e7de914cb099b5b837d46979bf", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "f6c771c77fcc45a4a4178d8748beeb3c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "f7c7e57c728147049f52ba691e408da8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7594,6 +7560,40 @@ "visibility": null, "width": null } + }, + "f914aa3a0cc1445b81656d22ac9574b0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "fc0dbc5ad67f44d1970ac151d319ba0e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } } }, "version_major": 2, diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 688f732c4..7e053c006 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -73,10 +73,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:11.092091Z", - "iopub.status.busy": "2024-09-26T14:51:11.091687Z", - "iopub.status.idle": "2024-09-26T14:51:12.301495Z", - "shell.execute_reply": "2024-09-26T14:51:12.300906Z" + "iopub.execute_input": "2024-09-26T16:46:49.490061Z", + "iopub.status.busy": "2024-09-26T16:46:49.489608Z", + "iopub.status.idle": "2024-09-26T16:46:50.662598Z", + "shell.execute_reply": "2024-09-26T16:46:50.662034Z" }, "nbsphinx": "hidden" }, @@ -86,7 +86,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.303829Z", - "iopub.status.busy": "2024-09-26T14:51:12.303363Z", - "iopub.status.idle": "2024-09-26T14:51:12.322353Z", - "shell.execute_reply": "2024-09-26T14:51:12.321902Z" + "iopub.execute_input": "2024-09-26T16:46:50.664743Z", + "iopub.status.busy": "2024-09-26T16:46:50.664376Z", + "iopub.status.idle": "2024-09-26T16:46:50.682132Z", + "shell.execute_reply": "2024-09-26T16:46:50.681695Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.324450Z", - "iopub.status.busy": "2024-09-26T14:51:12.324010Z", - "iopub.status.idle": "2024-09-26T14:51:12.348557Z", - "shell.execute_reply": "2024-09-26T14:51:12.348062Z" + "iopub.execute_input": "2024-09-26T16:46:50.683962Z", + "iopub.status.busy": "2024-09-26T16:46:50.683639Z", + "iopub.status.idle": "2024-09-26T16:46:50.719357Z", + "shell.execute_reply": "2024-09-26T16:46:50.718929Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.350597Z", - "iopub.status.busy": "2024-09-26T14:51:12.350164Z", - "iopub.status.idle": "2024-09-26T14:51:12.353712Z", - "shell.execute_reply": "2024-09-26T14:51:12.353237Z" + "iopub.execute_input": "2024-09-26T16:46:50.721064Z", + "iopub.status.busy": "2024-09-26T16:46:50.720643Z", + "iopub.status.idle": "2024-09-26T16:46:50.724183Z", + "shell.execute_reply": "2024-09-26T16:46:50.723622Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.355535Z", - "iopub.status.busy": "2024-09-26T14:51:12.355193Z", - "iopub.status.idle": "2024-09-26T14:51:12.364277Z", - "shell.execute_reply": "2024-09-26T14:51:12.363833Z" + "iopub.execute_input": "2024-09-26T16:46:50.726085Z", + "iopub.status.busy": "2024-09-26T16:46:50.725669Z", + "iopub.status.idle": "2024-09-26T16:46:50.733226Z", + "shell.execute_reply": "2024-09-26T16:46:50.732816Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.366192Z", - "iopub.status.busy": "2024-09-26T14:51:12.365860Z", - "iopub.status.idle": "2024-09-26T14:51:12.368238Z", - "shell.execute_reply": "2024-09-26T14:51:12.367806Z" + "iopub.execute_input": "2024-09-26T16:46:50.735243Z", + "iopub.status.busy": "2024-09-26T16:46:50.734917Z", + "iopub.status.idle": "2024-09-26T16:46:50.737575Z", + "shell.execute_reply": "2024-09-26T16:46:50.737017Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:12.369910Z", - "iopub.status.busy": "2024-09-26T14:51:12.369584Z", - "iopub.status.idle": "2024-09-26T14:51:15.473892Z", - "shell.execute_reply": "2024-09-26T14:51:15.473328Z" + "iopub.execute_input": "2024-09-26T16:46:50.739261Z", + "iopub.status.busy": "2024-09-26T16:46:50.738945Z", + "iopub.status.idle": "2024-09-26T16:46:53.810159Z", + "shell.execute_reply": "2024-09-26T16:46:53.809623Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:15.476275Z", - "iopub.status.busy": "2024-09-26T14:51:15.475917Z", - "iopub.status.idle": "2024-09-26T14:51:15.485407Z", - "shell.execute_reply": "2024-09-26T14:51:15.484796Z" + "iopub.execute_input": "2024-09-26T16:46:53.812406Z", + "iopub.status.busy": "2024-09-26T16:46:53.812011Z", + "iopub.status.idle": "2024-09-26T16:46:53.821759Z", + "shell.execute_reply": "2024-09-26T16:46:53.821333Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:15.487349Z", - "iopub.status.busy": "2024-09-26T14:51:15.487005Z", - "iopub.status.idle": "2024-09-26T14:51:17.515517Z", - "shell.execute_reply": "2024-09-26T14:51:17.514901Z" + "iopub.execute_input": "2024-09-26T16:46:53.823459Z", + "iopub.status.busy": "2024-09-26T16:46:53.823124Z", + "iopub.status.idle": "2024-09-26T16:46:55.741477Z", + "shell.execute_reply": "2024-09-26T16:46:55.740911Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.517807Z", - "iopub.status.busy": "2024-09-26T14:51:17.517109Z", - "iopub.status.idle": "2024-09-26T14:51:17.536120Z", - "shell.execute_reply": "2024-09-26T14:51:17.535624Z" + "iopub.execute_input": "2024-09-26T16:46:55.743632Z", + "iopub.status.busy": "2024-09-26T16:46:55.743134Z", + "iopub.status.idle": "2024-09-26T16:46:55.761258Z", + "shell.execute_reply": "2024-09-26T16:46:55.760774Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.537976Z", - "iopub.status.busy": "2024-09-26T14:51:17.537611Z", - "iopub.status.idle": "2024-09-26T14:51:17.545869Z", - "shell.execute_reply": "2024-09-26T14:51:17.545319Z" + "iopub.execute_input": "2024-09-26T16:46:55.762948Z", + "iopub.status.busy": "2024-09-26T16:46:55.762638Z", + "iopub.status.idle": "2024-09-26T16:46:55.770290Z", + "shell.execute_reply": "2024-09-26T16:46:55.769866Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.547622Z", - "iopub.status.busy": "2024-09-26T14:51:17.547301Z", - "iopub.status.idle": "2024-09-26T14:51:17.556250Z", - "shell.execute_reply": "2024-09-26T14:51:17.555755Z" + "iopub.execute_input": "2024-09-26T16:46:55.771961Z", + "iopub.status.busy": "2024-09-26T16:46:55.771630Z", + "iopub.status.idle": "2024-09-26T16:46:55.780201Z", + "shell.execute_reply": "2024-09-26T16:46:55.779777Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.557888Z", - "iopub.status.busy": "2024-09-26T14:51:17.557705Z", - "iopub.status.idle": "2024-09-26T14:51:17.565685Z", - "shell.execute_reply": "2024-09-26T14:51:17.565225Z" + "iopub.execute_input": "2024-09-26T16:46:55.781863Z", + "iopub.status.busy": "2024-09-26T16:46:55.781594Z", + "iopub.status.idle": "2024-09-26T16:46:55.789510Z", + "shell.execute_reply": "2024-09-26T16:46:55.788979Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.567291Z", - "iopub.status.busy": "2024-09-26T14:51:17.567107Z", - "iopub.status.idle": "2024-09-26T14:51:17.576362Z", - "shell.execute_reply": "2024-09-26T14:51:17.575909Z" + "iopub.execute_input": "2024-09-26T16:46:55.791202Z", + "iopub.status.busy": "2024-09-26T16:46:55.790892Z", + "iopub.status.idle": "2024-09-26T16:46:55.799367Z", + "shell.execute_reply": "2024-09-26T16:46:55.798943Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.577990Z", - "iopub.status.busy": "2024-09-26T14:51:17.577812Z", - "iopub.status.idle": "2024-09-26T14:51:17.585393Z", - "shell.execute_reply": "2024-09-26T14:51:17.584817Z" + "iopub.execute_input": "2024-09-26T16:46:55.800996Z", + "iopub.status.busy": "2024-09-26T16:46:55.800824Z", + "iopub.status.idle": "2024-09-26T16:46:55.808181Z", + "shell.execute_reply": "2024-09-26T16:46:55.807731Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.587245Z", - "iopub.status.busy": "2024-09-26T14:51:17.586929Z", - "iopub.status.idle": "2024-09-26T14:51:17.594347Z", - "shell.execute_reply": "2024-09-26T14:51:17.593795Z" + "iopub.execute_input": "2024-09-26T16:46:55.809783Z", + "iopub.status.busy": "2024-09-26T16:46:55.809613Z", + "iopub.status.idle": "2024-09-26T16:46:55.816908Z", + "shell.execute_reply": "2024-09-26T16:46:55.816477Z" } }, "outputs": [ @@ -1306,10 +1306,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:17.596172Z", - "iopub.status.busy": "2024-09-26T14:51:17.595784Z", - "iopub.status.idle": "2024-09-26T14:51:17.604165Z", - "shell.execute_reply": "2024-09-26T14:51:17.603720Z" + "iopub.execute_input": "2024-09-26T16:46:55.818594Z", + "iopub.status.busy": "2024-09-26T16:46:55.818403Z", + "iopub.status.idle": "2024-09-26T16:46:55.827049Z", + "shell.execute_reply": "2024-09-26T16:46:55.826476Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index cb95da0ec..bfb61f875 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -795,7 +795,7 @@

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

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

diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 5b5c4a565..38b874be2 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": "2024-09-26T14:51:20.550084Z", - "iopub.status.busy": "2024-09-26T14:51:20.549919Z", - "iopub.status.idle": "2024-09-26T14:51:23.546779Z", - "shell.execute_reply": "2024-09-26T14:51:23.546140Z" + "iopub.execute_input": "2024-09-26T16:46:58.410038Z", + "iopub.status.busy": "2024-09-26T16:46:58.409552Z", + "iopub.status.idle": "2024-09-26T16:47:01.293395Z", + "shell.execute_reply": "2024-09-26T16:47:01.292721Z" }, "nbsphinx": "hidden" }, @@ -96,7 +96,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -121,10 +121,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.549062Z", - "iopub.status.busy": "2024-09-26T14:51:23.548756Z", - "iopub.status.idle": "2024-09-26T14:51:23.551996Z", - "shell.execute_reply": "2024-09-26T14:51:23.551554Z" + "iopub.execute_input": "2024-09-26T16:47:01.295853Z", + "iopub.status.busy": "2024-09-26T16:47:01.295379Z", + "iopub.status.idle": "2024-09-26T16:47:01.298895Z", + "shell.execute_reply": "2024-09-26T16:47:01.298449Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.553571Z", - "iopub.status.busy": "2024-09-26T14:51:23.553396Z", - "iopub.status.idle": "2024-09-26T14:51:23.556530Z", - "shell.execute_reply": "2024-09-26T14:51:23.556072Z" + "iopub.execute_input": "2024-09-26T16:47:01.300604Z", + "iopub.status.busy": "2024-09-26T16:47:01.300270Z", + "iopub.status.idle": "2024-09-26T16:47:01.303351Z", + "shell.execute_reply": "2024-09-26T16:47:01.302879Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.558190Z", - "iopub.status.busy": "2024-09-26T14:51:23.558016Z", - "iopub.status.idle": "2024-09-26T14:51:23.584373Z", - "shell.execute_reply": "2024-09-26T14:51:23.583877Z" + "iopub.execute_input": "2024-09-26T16:47:01.305038Z", + "iopub.status.busy": "2024-09-26T16:47:01.304702Z", + "iopub.status.idle": "2024-09-26T16:47:01.343430Z", + "shell.execute_reply": "2024-09-26T16:47:01.342872Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.586327Z", - "iopub.status.busy": "2024-09-26T14:51:23.585980Z", - "iopub.status.idle": "2024-09-26T14:51:23.589627Z", - "shell.execute_reply": "2024-09-26T14:51:23.589147Z" + "iopub.execute_input": "2024-09-26T16:47:01.345069Z", + "iopub.status.busy": "2024-09-26T16:47:01.344889Z", + "iopub.status.idle": "2024-09-26T16:47:01.348954Z", + "shell.execute_reply": "2024-09-26T16:47:01.348459Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'supported_cards_and_currencies', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'change_pin', 'beneficiary_not_allowed', 'card_about_to_expire', 'getting_spare_card', 'apple_pay_or_google_pay', 'cancel_transfer'}\n" + "Classes: {'cancel_transfer', 'card_about_to_expire', 'visa_or_mastercard', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'change_pin', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'getting_spare_card'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.591183Z", - "iopub.status.busy": "2024-09-26T14:51:23.591009Z", - "iopub.status.idle": "2024-09-26T14:51:23.594239Z", - "shell.execute_reply": "2024-09-26T14:51:23.593788Z" + "iopub.execute_input": "2024-09-26T16:47:01.350721Z", + "iopub.status.busy": "2024-09-26T16:47:01.350354Z", + "iopub.status.idle": "2024-09-26T16:47:01.353211Z", + "shell.execute_reply": "2024-09-26T16:47:01.352759Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:23.595893Z", - "iopub.status.busy": "2024-09-26T14:51:23.595586Z", - "iopub.status.idle": "2024-09-26T14:51:27.775987Z", - "shell.execute_reply": "2024-09-26T14:51:27.775330Z" + "iopub.execute_input": "2024-09-26T16:47:01.355002Z", + "iopub.status.busy": "2024-09-26T16:47:01.354700Z", + "iopub.status.idle": "2024-09-26T16:47:05.074430Z", + "shell.execute_reply": "2024-09-26T16:47:05.073775Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:27.778341Z", - "iopub.status.busy": "2024-09-26T14:51:27.777966Z", - "iopub.status.idle": "2024-09-26T14:51:28.697834Z", - "shell.execute_reply": "2024-09-26T14:51:28.697228Z" + "iopub.execute_input": "2024-09-26T16:47:05.076632Z", + "iopub.status.busy": "2024-09-26T16:47:05.076427Z", + "iopub.status.idle": "2024-09-26T16:47:05.983078Z", + "shell.execute_reply": "2024-09-26T16:47:05.982471Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:28.700329Z", - "iopub.status.busy": "2024-09-26T14:51:28.699942Z", - "iopub.status.idle": "2024-09-26T14:51:28.702874Z", - "shell.execute_reply": "2024-09-26T14:51:28.702381Z" + "iopub.execute_input": "2024-09-26T16:47:05.985551Z", + "iopub.status.busy": "2024-09-26T16:47:05.985169Z", + "iopub.status.idle": "2024-09-26T16:47:05.988117Z", + "shell.execute_reply": "2024-09-26T16:47:05.987611Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:28.704853Z", - "iopub.status.busy": "2024-09-26T14:51:28.704499Z", - "iopub.status.idle": "2024-09-26T14:51:30.723899Z", - "shell.execute_reply": "2024-09-26T14:51:30.723229Z" + "iopub.execute_input": "2024-09-26T16:47:05.990255Z", + "iopub.status.busy": "2024-09-26T16:47:05.989893Z", + "iopub.status.idle": "2024-09-26T16:47:07.971023Z", + "shell.execute_reply": "2024-09-26T16:47:07.970323Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.727734Z", - "iopub.status.busy": "2024-09-26T14:51:30.726555Z", - "iopub.status.idle": "2024-09-26T14:51:30.752360Z", - "shell.execute_reply": "2024-09-26T14:51:30.751847Z" + "iopub.execute_input": "2024-09-26T16:47:07.974754Z", + "iopub.status.busy": "2024-09-26T16:47:07.973728Z", + "iopub.status.idle": "2024-09-26T16:47:07.999496Z", + "shell.execute_reply": "2024-09-26T16:47:07.998965Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.755440Z", - "iopub.status.busy": "2024-09-26T14:51:30.754576Z", - "iopub.status.idle": "2024-09-26T14:51:30.764760Z", - "shell.execute_reply": "2024-09-26T14:51:30.764347Z" + "iopub.execute_input": "2024-09-26T16:47:08.002480Z", + "iopub.status.busy": "2024-09-26T16:47:08.001687Z", + "iopub.status.idle": "2024-09-26T16:47:08.011823Z", + "shell.execute_reply": "2024-09-26T16:47:08.011250Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.767190Z", - "iopub.status.busy": "2024-09-26T14:51:30.766574Z", - "iopub.status.idle": "2024-09-26T14:51:30.771522Z", - "shell.execute_reply": "2024-09-26T14:51:30.771112Z" + "iopub.execute_input": "2024-09-26T16:47:08.013635Z", + "iopub.status.busy": "2024-09-26T16:47:08.013359Z", + "iopub.status.idle": "2024-09-26T16:47:08.017732Z", + "shell.execute_reply": "2024-09-26T16:47:08.017145Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.773863Z", - "iopub.status.busy": "2024-09-26T14:51:30.773237Z", - "iopub.status.idle": "2024-09-26T14:51:30.780343Z", - "shell.execute_reply": "2024-09-26T14:51:30.779939Z" + "iopub.execute_input": "2024-09-26T16:47:08.019401Z", + "iopub.status.busy": "2024-09-26T16:47:08.019129Z", + "iopub.status.idle": "2024-09-26T16:47:08.025649Z", + "shell.execute_reply": "2024-09-26T16:47:08.025085Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.782231Z", - "iopub.status.busy": "2024-09-26T14:51:30.782055Z", - "iopub.status.idle": "2024-09-26T14:51:30.788970Z", - "shell.execute_reply": "2024-09-26T14:51:30.788375Z" + "iopub.execute_input": "2024-09-26T16:47:08.027291Z", + "iopub.status.busy": "2024-09-26T16:47:08.027019Z", + "iopub.status.idle": "2024-09-26T16:47:08.033453Z", + "shell.execute_reply": "2024-09-26T16:47:08.032925Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.790778Z", - "iopub.status.busy": "2024-09-26T14:51:30.790601Z", - "iopub.status.idle": "2024-09-26T14:51:30.796446Z", - "shell.execute_reply": "2024-09-26T14:51:30.795882Z" + "iopub.execute_input": "2024-09-26T16:47:08.035074Z", + "iopub.status.busy": "2024-09-26T16:47:08.034761Z", + "iopub.status.idle": "2024-09-26T16:47:08.040415Z", + "shell.execute_reply": "2024-09-26T16:47:08.039969Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.798232Z", - "iopub.status.busy": "2024-09-26T14:51:30.797967Z", - "iopub.status.idle": "2024-09-26T14:51:30.806498Z", - "shell.execute_reply": "2024-09-26T14:51:30.805933Z" + "iopub.execute_input": "2024-09-26T16:47:08.042047Z", + "iopub.status.busy": "2024-09-26T16:47:08.041737Z", + "iopub.status.idle": "2024-09-26T16:47:08.050191Z", + "shell.execute_reply": "2024-09-26T16:47:08.049613Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.808353Z", - "iopub.status.busy": "2024-09-26T14:51:30.808081Z", - "iopub.status.idle": "2024-09-26T14:51:30.813342Z", - "shell.execute_reply": "2024-09-26T14:51:30.812825Z" + "iopub.execute_input": "2024-09-26T16:47:08.051928Z", + "iopub.status.busy": "2024-09-26T16:47:08.051586Z", + "iopub.status.idle": "2024-09-26T16:47:08.056924Z", + "shell.execute_reply": "2024-09-26T16:47:08.056363Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.814997Z", - "iopub.status.busy": "2024-09-26T14:51:30.814668Z", - "iopub.status.idle": "2024-09-26T14:51:30.819982Z", - "shell.execute_reply": "2024-09-26T14:51:30.819532Z" + "iopub.execute_input": "2024-09-26T16:47:08.058659Z", + "iopub.status.busy": "2024-09-26T16:47:08.058314Z", + "iopub.status.idle": "2024-09-26T16:47:08.063716Z", + "shell.execute_reply": "2024-09-26T16:47:08.063139Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.821669Z", - "iopub.status.busy": "2024-09-26T14:51:30.821337Z", - "iopub.status.idle": "2024-09-26T14:51:30.824940Z", - "shell.execute_reply": "2024-09-26T14:51:30.824366Z" + "iopub.execute_input": "2024-09-26T16:47:08.065538Z", + "iopub.status.busy": "2024-09-26T16:47:08.065225Z", + "iopub.status.idle": "2024-09-26T16:47:08.068887Z", + "shell.execute_reply": "2024-09-26T16:47:08.068337Z" } }, "outputs": [ @@ -1449,10 +1449,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:30.826780Z", - "iopub.status.busy": "2024-09-26T14:51:30.826459Z", - "iopub.status.idle": "2024-09-26T14:51:30.831493Z", - "shell.execute_reply": "2024-09-26T14:51:30.831041Z" + "iopub.execute_input": "2024-09-26T16:47:08.070894Z", + "iopub.status.busy": "2024-09-26T16:47:08.070331Z", + "iopub.status.idle": "2024-09-26T16:47:08.075632Z", + "shell.execute_reply": "2024-09-26T16:47:08.075187Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index ad7b0db5d..47798b4e5 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -837,7 +837,7 @@

4. Identify Data Issues Using Datalab @@ -883,13 +883,13 @@

4. Identify Data Issues Using Datalab - +
- - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
@@ -3507,16 +3507,16 @@

1. Load the Dataset
---2024-09-26 14:51:50--  https://s.cleanlab.ai/CIFAR-10-subset.zip
+--2024-09-26 16:47:27--  https://s.cleanlab.ai/CIFAR-10-subset.zip
 Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...
 Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 986707 (964K) [application/zip]
 Saving to: ‘CIFAR-10-subset.zip’
 
-CIFAR-10-subset.zip 100%[===================>] 963.58K  --.-KB/s    in 0.009s
+CIFAR-10-subset.zip 100%[===================>] 963.58K  --.-KB/s    in 0.03s
 
-2024-09-26 14:51:50 (107 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]
+2024-09-26 16:47:27 (37.5 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]
 
 
@@ -3586,7 +3586,7 @@

2. Run Datalab Analysis
-
+
@@ -3930,7 +3930,7 @@

3. Interpret the ResultsFrog class (Class 0 in the plot) have been darkened, while 100 images from the Truck class (Class 1 in the plot) remain unchanged, as in the CIFAR-10 dataset. This creates a clear spurious correlation between the ‘darkness’ feature and the class labels: Frog images are dark, whereas Truck images are not. We can see that the dark_score values between the two classes are non-overlapping. This characteristic of the dataset is identified by Datalab.

diff --git a/master/tutorials/datalab/workflows.ipynb b/master/tutorials/datalab/workflows.ipynb index 9404f1540..07fff34e8 100644 --- a/master/tutorials/datalab/workflows.ipynb +++ b/master/tutorials/datalab/workflows.ipynb @@ -38,10 +38,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:34.296488Z", - "iopub.status.busy": "2024-09-26T14:51:34.296076Z", - "iopub.status.idle": "2024-09-26T14:51:35.016105Z", - "shell.execute_reply": "2024-09-26T14:51:35.015553Z" + "iopub.execute_input": "2024-09-26T16:47:11.525433Z", + "iopub.status.busy": "2024-09-26T16:47:11.525274Z", + "iopub.status.idle": "2024-09-26T16:47:12.226983Z", + "shell.execute_reply": "2024-09-26T16:47:12.226406Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:35.018426Z", - "iopub.status.busy": "2024-09-26T14:51:35.017967Z", - "iopub.status.idle": "2024-09-26T14:51:35.151580Z", - "shell.execute_reply": "2024-09-26T14:51:35.151068Z" + "iopub.execute_input": "2024-09-26T16:47:12.229251Z", + "iopub.status.busy": "2024-09-26T16:47:12.228753Z", + "iopub.status.idle": "2024-09-26T16:47:12.360301Z", + "shell.execute_reply": "2024-09-26T16:47:12.359800Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:35.153697Z", - "iopub.status.busy": "2024-09-26T14:51:35.153277Z", - "iopub.status.idle": "2024-09-26T14:51:35.177588Z", - "shell.execute_reply": "2024-09-26T14:51:35.176982Z" + "iopub.execute_input": "2024-09-26T16:47:12.362311Z", + "iopub.status.busy": "2024-09-26T16:47:12.361886Z", + "iopub.status.idle": "2024-09-26T16:47:12.385294Z", + "shell.execute_reply": "2024-09-26T16:47:12.384666Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:35.179788Z", - "iopub.status.busy": "2024-09-26T14:51:35.179361Z", - "iopub.status.idle": "2024-09-26T14:51:37.765581Z", - "shell.execute_reply": "2024-09-26T14:51:37.764993Z" + "iopub.execute_input": "2024-09-26T16:47:12.387377Z", + "iopub.status.busy": "2024-09-26T16:47:12.387130Z", + "iopub.status.idle": "2024-09-26T16:47:14.927165Z", + "shell.execute_reply": "2024-09-26T16:47:14.926608Z" } }, "outputs": [ @@ -235,7 +235,7 @@ "Finding class_imbalance issues ...\n", "Finding underperforming_group issues ...\n", "\n", - "Audit complete. 524 issues found in the dataset.\n" + "Audit complete. 523 issues found in the dataset.\n" ] }, { @@ -280,13 +280,13 @@ " \n", " 2\n", " outlier\n", - " 0.356924\n", - " 363\n", + " 0.356958\n", + " 362\n", " \n", " \n", " 3\n", " near_duplicate\n", - " 0.619581\n", + " 0.619565\n", " 108\n", " \n", " \n", @@ -315,8 +315,8 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356924 363\n", - "3 near_duplicate 0.619581 108\n", + "2 outlier 0.356958 362\n", + "3 near_duplicate 0.619565 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", "6 underperforming_group 0.651838 0" @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:37.767993Z", - "iopub.status.busy": "2024-09-26T14:51:37.767425Z", - "iopub.status.idle": "2024-09-26T14:51:46.526023Z", - "shell.execute_reply": "2024-09-26T14:51:46.525421Z" + "iopub.execute_input": "2024-09-26T16:47:14.929608Z", + "iopub.status.busy": "2024-09-26T16:47:14.929044Z", + "iopub.status.idle": "2024-09-26T16:47:23.657825Z", + "shell.execute_reply": "2024-09-26T16:47:23.657226Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:46.528043Z", - "iopub.status.busy": "2024-09-26T14:51:46.527681Z", - "iopub.status.idle": "2024-09-26T14:51:46.730683Z", - "shell.execute_reply": "2024-09-26T14:51:46.730045Z" + "iopub.execute_input": "2024-09-26T16:47:23.659768Z", + "iopub.status.busy": "2024-09-26T16:47:23.659418Z", + "iopub.status.idle": "2024-09-26T16:47:23.835993Z", + "shell.execute_reply": "2024-09-26T16:47:23.835468Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:46.732793Z", - "iopub.status.busy": "2024-09-26T14:51:46.732448Z", - "iopub.status.idle": "2024-09-26T14:51:48.255623Z", - "shell.execute_reply": "2024-09-26T14:51:48.255118Z" + "iopub.execute_input": "2024-09-26T16:47:23.838171Z", + "iopub.status.busy": "2024-09-26T16:47:23.837805Z", + "iopub.status.idle": "2024-09-26T16:47:25.345917Z", + "shell.execute_reply": "2024-09-26T16:47:25.345419Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:48.257484Z", - "iopub.status.busy": "2024-09-26T14:51:48.257119Z", - "iopub.status.idle": "2024-09-26T14:51:48.773736Z", - "shell.execute_reply": "2024-09-26T14:51:48.773135Z" + "iopub.execute_input": "2024-09-26T16:47:25.347933Z", + "iopub.status.busy": "2024-09-26T16:47:25.347511Z", + "iopub.status.idle": "2024-09-26T16:47:25.745787Z", + "shell.execute_reply": "2024-09-26T16:47:25.745185Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:48.775864Z", - "iopub.status.busy": "2024-09-26T14:51:48.775323Z", - "iopub.status.idle": "2024-09-26T14:51:48.790103Z", - "shell.execute_reply": "2024-09-26T14:51:48.789626Z" + "iopub.execute_input": "2024-09-26T16:47:25.748172Z", + "iopub.status.busy": "2024-09-26T16:47:25.747663Z", + "iopub.status.idle": "2024-09-26T16:47:25.761333Z", + "shell.execute_reply": "2024-09-26T16:47:25.760829Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:48.791833Z", - "iopub.status.busy": "2024-09-26T14:51:48.791503Z", - "iopub.status.idle": "2024-09-26T14:51:48.810723Z", - "shell.execute_reply": "2024-09-26T14:51:48.810137Z" + "iopub.execute_input": "2024-09-26T16:47:25.763141Z", + "iopub.status.busy": "2024-09-26T16:47:25.762730Z", + "iopub.status.idle": "2024-09-26T16:47:25.781283Z", + "shell.execute_reply": "2024-09-26T16:47:25.780726Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:48.812726Z", - "iopub.status.busy": "2024-09-26T14:51:48.812340Z", - "iopub.status.idle": "2024-09-26T14:51:49.055015Z", - "shell.execute_reply": "2024-09-26T14:51:49.054398Z" + "iopub.execute_input": "2024-09-26T16:47:25.783262Z", + "iopub.status.busy": "2024-09-26T16:47:25.782919Z", + "iopub.status.idle": "2024-09-26T16:47:26.000731Z", + "shell.execute_reply": "2024-09-26T16:47:26.000107Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.057480Z", - "iopub.status.busy": "2024-09-26T14:51:49.057052Z", - "iopub.status.idle": "2024-09-26T14:51:49.076673Z", - "shell.execute_reply": "2024-09-26T14:51:49.076195Z" + "iopub.execute_input": "2024-09-26T16:47:26.002990Z", + "iopub.status.busy": "2024-09-26T16:47:26.002605Z", + "iopub.status.idle": "2024-09-26T16:47:26.022425Z", + "shell.execute_reply": "2024-09-26T16:47:26.021840Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.078495Z", - "iopub.status.busy": "2024-09-26T14:51:49.078146Z", - "iopub.status.idle": "2024-09-26T14:51:49.248180Z", - "shell.execute_reply": "2024-09-26T14:51:49.247594Z" + "iopub.execute_input": "2024-09-26T16:47:26.024424Z", + "iopub.status.busy": "2024-09-26T16:47:26.024030Z", + "iopub.status.idle": "2024-09-26T16:47:26.193727Z", + "shell.execute_reply": "2024-09-26T16:47:26.193198Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.250291Z", - "iopub.status.busy": "2024-09-26T14:51:49.249923Z", - "iopub.status.idle": "2024-09-26T14:51:49.260161Z", - "shell.execute_reply": "2024-09-26T14:51:49.259683Z" + "iopub.execute_input": "2024-09-26T16:47:26.195878Z", + "iopub.status.busy": "2024-09-26T16:47:26.195408Z", + "iopub.status.idle": "2024-09-26T16:47:26.205589Z", + "shell.execute_reply": "2024-09-26T16:47:26.205122Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.261950Z", - "iopub.status.busy": "2024-09-26T14:51:49.261604Z", - "iopub.status.idle": "2024-09-26T14:51:49.271258Z", - "shell.execute_reply": "2024-09-26T14:51:49.270689Z" + "iopub.execute_input": "2024-09-26T16:47:26.207533Z", + "iopub.status.busy": "2024-09-26T16:47:26.207079Z", + "iopub.status.idle": "2024-09-26T16:47:26.217141Z", + "shell.execute_reply": "2024-09-26T16:47:26.216678Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.272963Z", - "iopub.status.busy": "2024-09-26T14:51:49.272785Z", - "iopub.status.idle": "2024-09-26T14:51:49.300283Z", - "shell.execute_reply": "2024-09-26T14:51:49.299657Z" + "iopub.execute_input": "2024-09-26T16:47:26.219046Z", + "iopub.status.busy": "2024-09-26T16:47:26.218645Z", + "iopub.status.idle": "2024-09-26T16:47:26.245410Z", + "shell.execute_reply": "2024-09-26T16:47:26.244835Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.302435Z", - "iopub.status.busy": "2024-09-26T14:51:49.302020Z", - "iopub.status.idle": "2024-09-26T14:51:49.304853Z", - "shell.execute_reply": "2024-09-26T14:51:49.304388Z" + "iopub.execute_input": "2024-09-26T16:47:26.247193Z", + "iopub.status.busy": "2024-09-26T16:47:26.246877Z", + "iopub.status.idle": "2024-09-26T16:47:26.249497Z", + "shell.execute_reply": "2024-09-26T16:47:26.249056Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.306559Z", - "iopub.status.busy": "2024-09-26T14:51:49.306373Z", - "iopub.status.idle": "2024-09-26T14:51:49.326211Z", - "shell.execute_reply": "2024-09-26T14:51:49.325620Z" + "iopub.execute_input": "2024-09-26T16:47:26.251215Z", + "iopub.status.busy": "2024-09-26T16:47:26.250900Z", + "iopub.status.idle": "2024-09-26T16:47:26.269963Z", + "shell.execute_reply": "2024-09-26T16:47:26.269532Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.328491Z", - "iopub.status.busy": "2024-09-26T14:51:49.327912Z", - "iopub.status.idle": "2024-09-26T14:51:49.332250Z", - "shell.execute_reply": "2024-09-26T14:51:49.331798Z" + "iopub.execute_input": "2024-09-26T16:47:26.271736Z", + "iopub.status.busy": "2024-09-26T16:47:26.271407Z", + "iopub.status.idle": "2024-09-26T16:47:26.275486Z", + "shell.execute_reply": "2024-09-26T16:47:26.275041Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.334080Z", - "iopub.status.busy": "2024-09-26T14:51:49.333676Z", - "iopub.status.idle": "2024-09-26T14:51:49.363534Z", - "shell.execute_reply": "2024-09-26T14:51:49.362928Z" + "iopub.execute_input": "2024-09-26T16:47:26.277173Z", + "iopub.status.busy": "2024-09-26T16:47:26.276779Z", + "iopub.status.idle": "2024-09-26T16:47:26.304468Z", + "shell.execute_reply": "2024-09-26T16:47:26.303913Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.365331Z", - "iopub.status.busy": "2024-09-26T14:51:49.365032Z", - "iopub.status.idle": "2024-09-26T14:51:49.727339Z", - "shell.execute_reply": "2024-09-26T14:51:49.726743Z" + "iopub.execute_input": "2024-09-26T16:47:26.306278Z", + "iopub.status.busy": "2024-09-26T16:47:26.305888Z", + "iopub.status.idle": "2024-09-26T16:47:26.684139Z", + "shell.execute_reply": "2024-09-26T16:47:26.683504Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.729270Z", - "iopub.status.busy": "2024-09-26T14:51:49.729071Z", - "iopub.status.idle": "2024-09-26T14:51:49.732072Z", - "shell.execute_reply": "2024-09-26T14:51:49.731620Z" + "iopub.execute_input": "2024-09-26T16:47:26.686274Z", + "iopub.status.busy": "2024-09-26T16:47:26.685797Z", + "iopub.status.idle": "2024-09-26T16:47:26.689345Z", + "shell.execute_reply": "2024-09-26T16:47:26.688769Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.733810Z", - "iopub.status.busy": "2024-09-26T14:51:49.733632Z", - "iopub.status.idle": "2024-09-26T14:51:49.747657Z", - "shell.execute_reply": "2024-09-26T14:51:49.747198Z" + "iopub.execute_input": "2024-09-26T16:47:26.691398Z", + "iopub.status.busy": "2024-09-26T16:47:26.691056Z", + "iopub.status.idle": "2024-09-26T16:47:26.705013Z", + "shell.execute_reply": "2024-09-26T16:47:26.704453Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.749243Z", - "iopub.status.busy": "2024-09-26T14:51:49.749065Z", - "iopub.status.idle": "2024-09-26T14:51:49.763193Z", - "shell.execute_reply": "2024-09-26T14:51:49.762714Z" + "iopub.execute_input": "2024-09-26T16:47:26.706832Z", + "iopub.status.busy": "2024-09-26T16:47:26.706495Z", + "iopub.status.idle": "2024-09-26T16:47:26.719653Z", + "shell.execute_reply": "2024-09-26T16:47:26.719181Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.764801Z", - "iopub.status.busy": "2024-09-26T14:51:49.764624Z", - "iopub.status.idle": "2024-09-26T14:51:49.775091Z", - "shell.execute_reply": "2024-09-26T14:51:49.774491Z" + "iopub.execute_input": "2024-09-26T16:47:26.721290Z", + "iopub.status.busy": "2024-09-26T16:47:26.720960Z", + "iopub.status.idle": "2024-09-26T16:47:26.731265Z", + "shell.execute_reply": "2024-09-26T16:47:26.730702Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.777122Z", - "iopub.status.busy": "2024-09-26T14:51:49.776798Z", - "iopub.status.idle": "2024-09-26T14:51:49.786610Z", - "shell.execute_reply": "2024-09-26T14:51:49.786151Z" + "iopub.execute_input": "2024-09-26T16:47:26.732949Z", + "iopub.status.busy": "2024-09-26T16:47:26.732616Z", + "iopub.status.idle": "2024-09-26T16:47:26.741870Z", + "shell.execute_reply": "2024-09-26T16:47:26.741406Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.788278Z", - "iopub.status.busy": "2024-09-26T14:51:49.788101Z", - "iopub.status.idle": "2024-09-26T14:51:49.791818Z", - "shell.execute_reply": "2024-09-26T14:51:49.791364Z" + "iopub.execute_input": "2024-09-26T16:47:26.743488Z", + "iopub.status.busy": "2024-09-26T16:47:26.743171Z", + "iopub.status.idle": "2024-09-26T16:47:26.746914Z", + "shell.execute_reply": "2024-09-26T16:47:26.746344Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.793563Z", - "iopub.status.busy": "2024-09-26T14:51:49.793225Z", - "iopub.status.idle": "2024-09-26T14:51:49.849225Z", - "shell.execute_reply": "2024-09-26T14:51:49.848755Z" + "iopub.execute_input": "2024-09-26T16:47:26.748699Z", + "iopub.status.busy": "2024-09-26T16:47:26.748389Z", + "iopub.status.idle": "2024-09-26T16:47:26.799836Z", + "shell.execute_reply": "2024-09-26T16:47:26.799365Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.851334Z", - "iopub.status.busy": "2024-09-26T14:51:49.850848Z", - "iopub.status.idle": "2024-09-26T14:51:49.856692Z", - "shell.execute_reply": "2024-09-26T14:51:49.856243Z" + "iopub.execute_input": "2024-09-26T16:47:26.801753Z", + "iopub.status.busy": "2024-09-26T16:47:26.801330Z", + "iopub.status.idle": "2024-09-26T16:47:26.806943Z", + "shell.execute_reply": "2024-09-26T16:47:26.806477Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.858413Z", - "iopub.status.busy": "2024-09-26T14:51:49.858094Z", - "iopub.status.idle": "2024-09-26T14:51:49.869805Z", - "shell.execute_reply": "2024-09-26T14:51:49.869218Z" + "iopub.execute_input": "2024-09-26T16:47:26.808681Z", + "iopub.status.busy": "2024-09-26T16:47:26.808369Z", + "iopub.status.idle": "2024-09-26T16:47:26.818815Z", + "shell.execute_reply": "2024-09-26T16:47:26.818308Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:49.871476Z", - "iopub.status.busy": "2024-09-26T14:51:49.871161Z", - "iopub.status.idle": "2024-09-26T14:51:50.098032Z", - "shell.execute_reply": "2024-09-26T14:51:50.097456Z" + "iopub.execute_input": "2024-09-26T16:47:26.820494Z", + "iopub.status.busy": "2024-09-26T16:47:26.820161Z", + "iopub.status.idle": "2024-09-26T16:47:27.036629Z", + "shell.execute_reply": "2024-09-26T16:47:27.036055Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:50.099892Z", - "iopub.status.busy": "2024-09-26T14:51:50.099599Z", - "iopub.status.idle": "2024-09-26T14:51:50.107584Z", - "shell.execute_reply": "2024-09-26T14:51:50.107015Z" + "iopub.execute_input": "2024-09-26T16:47:27.038570Z", + "iopub.status.busy": "2024-09-26T16:47:27.038220Z", + "iopub.status.idle": "2024-09-26T16:47:27.046060Z", + "shell.execute_reply": "2024-09-26T16:47:27.045499Z" }, "nbsphinx": "hidden" }, @@ -3756,10 +3756,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:50.109288Z", - "iopub.status.busy": "2024-09-26T14:51:50.109111Z", - "iopub.status.idle": "2024-09-26T14:51:50.496608Z", - "shell.execute_reply": "2024-09-26T14:51:50.495787Z" + "iopub.execute_input": "2024-09-26T16:47:27.047997Z", + "iopub.status.busy": "2024-09-26T16:47:27.047729Z", + "iopub.status.idle": "2024-09-26T16:47:27.417036Z", + "shell.execute_reply": "2024-09-26T16:47:27.416327Z" } }, "outputs": [ @@ -3767,25 +3767,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-26 14:51:50-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", + "--2024-09-26 16:47:27-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...\r\n", "Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.\r\n", - "HTTP request sent, awaiting response... " + "HTTP request sent, awaiting response... 200 OK\r\n", + "Length: 986707 (964K) [application/zip]\r\n", + "Saving to: ‘CIFAR-10-subset.zip’\r\n", + "\r\n", + "\r", + "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s " ] }, { "name": "stdout", "output_type": "stream", "text": [ - "200 OK\r\n", - "Length: 986707 (964K) [application/zip]\r\n", - "Saving to: ‘CIFAR-10-subset.zip’\r\n", - "\r\n", "\r", - "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s \r", - "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.009s \r\n", + "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.03s \r\n", "\r\n", - "2024-09-26 14:51:50 (107 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", + "2024-09-26 16:47:27 (37.5 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", "\r\n" ] } @@ -3801,10 +3801,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:50.499275Z", - "iopub.status.busy": "2024-09-26T14:51:50.498755Z", - "iopub.status.idle": "2024-09-26T14:51:52.468119Z", - "shell.execute_reply": "2024-09-26T14:51:52.467505Z" + "iopub.execute_input": "2024-09-26T16:47:27.419484Z", + "iopub.status.busy": "2024-09-26T16:47:27.419066Z", + "iopub.status.idle": "2024-09-26T16:47:29.358979Z", + "shell.execute_reply": "2024-09-26T16:47:29.358404Z" } }, "outputs": [], @@ -3850,10 +3850,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:52.470295Z", - "iopub.status.busy": "2024-09-26T14:51:52.470006Z", - "iopub.status.idle": "2024-09-26T14:51:53.135612Z", - "shell.execute_reply": "2024-09-26T14:51:53.134933Z" + "iopub.execute_input": "2024-09-26T16:47:29.361270Z", + "iopub.status.busy": "2024-09-26T16:47:29.360836Z", + "iopub.status.idle": "2024-09-26T16:47:29.992524Z", + "shell.execute_reply": "2024-09-26T16:47:29.991934Z" } }, "outputs": [ @@ -3868,7 +3868,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "819cd513a50348b98c0ff3c8dd72c7bd", + "model_id": "a63bc3d27ea944c88b147aafb6e82f0b", "version_major": 2, "version_minor": 0 }, @@ -4008,10 +4008,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:53.138593Z", - "iopub.status.busy": "2024-09-26T14:51:53.138086Z", - "iopub.status.idle": "2024-09-26T14:51:53.152674Z", - "shell.execute_reply": "2024-09-26T14:51:53.152106Z" + "iopub.execute_input": "2024-09-26T16:47:29.994886Z", + "iopub.status.busy": "2024-09-26T16:47:29.994545Z", + "iopub.status.idle": "2024-09-26T16:47:30.007642Z", + "shell.execute_reply": "2024-09-26T16:47:30.007131Z" } }, "outputs": [ @@ -4257,10 +4257,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:53.155019Z", - "iopub.status.busy": "2024-09-26T14:51:53.154607Z", - "iopub.status.idle": "2024-09-26T14:51:53.305855Z", - "shell.execute_reply": "2024-09-26T14:51:53.305327Z" + "iopub.execute_input": "2024-09-26T16:47:30.009605Z", + "iopub.status.busy": "2024-09-26T16:47:30.009286Z", + "iopub.status.idle": "2024-09-26T16:47:30.156937Z", + "shell.execute_reply": "2024-09-26T16:47:30.156503Z" } }, "outputs": [ @@ -4325,10 +4325,10 @@ "execution_count": 38, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:53.308217Z", - "iopub.status.busy": "2024-09-26T14:51:53.307686Z", - "iopub.status.idle": "2024-09-26T14:51:53.823497Z", - "shell.execute_reply": "2024-09-26T14:51:53.822950Z" + "iopub.execute_input": "2024-09-26T16:47:30.158677Z", + "iopub.status.busy": "2024-09-26T16:47:30.158395Z", + "iopub.status.idle": "2024-09-26T16:47:30.654568Z", + "shell.execute_reply": "2024-09-26T16:47:30.653982Z" }, "nbsphinx": "hidden" }, @@ -4344,7 +4344,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac8a16cb60b04919938bc00b2f1342f7", + "model_id": "e7bd0490ff824dacacf614c61ccd5710", "version_major": 2, "version_minor": 0 }, @@ -4598,10 +4598,10 @@ "execution_count": 39, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:53.825382Z", - "iopub.status.busy": "2024-09-26T14:51:53.825164Z", - "iopub.status.idle": "2024-09-26T14:51:53.978845Z", - "shell.execute_reply": "2024-09-26T14:51:53.978305Z" + "iopub.execute_input": "2024-09-26T16:47:30.656660Z", + "iopub.status.busy": "2024-09-26T16:47:30.656182Z", + "iopub.status.idle": "2024-09-26T16:47:30.810730Z", + "shell.execute_reply": "2024-09-26T16:47:30.810054Z" }, "nbsphinx": "hidden" }, @@ -4653,7 +4653,30 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0cd32d52503a444d88252596c7202d70": { + "070068fc0b8a43bfa7abcf6ef25f3499": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_95db50532273485690ad795e902244aa", + "placeholder": "​", + "style": "IPY_MODEL_544dde097eb048b6ab0b9b4fe1f18d87", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 728.63it/s]" + } + }, + "32711dbdb3a2478390a85a6d959d0790": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4706,7 +4729,89 @@ "width": null } }, - "1e281a0c15b84c80941bc82a97097993": { + "37db36182460420491c4c85f86cfbf7f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_81b6b7e53ed74a599bbe694a4c8ebe13", + "placeholder": "​", + "style": "IPY_MODEL_9e1fc069d2f5460a953347e6697fec03", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "463a277a85a84149a358f0da4373af05": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "5122a12efb9046908f7d71a4dbc25479": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_32711dbdb3a2478390a85a6d959d0790", + "placeholder": "​", + "style": "IPY_MODEL_463a277a85a84149a358f0da4373af05", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "544dde097eb048b6ab0b9b4fe1f18d87": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "594b20897d9849efa4b8c38aaef6e976": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4759,7 +4864,7 @@ "width": null } }, - "2680a7b149fd4520bb536ef2dfbaa7c2": { + "6e563a24defc41f69c74511f93e3f124": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4812,7 +4917,7 @@ "width": null } }, - "2ea4ee3035ec4460b45119db4b86c88e": { + "81b6b7e53ed74a599bbe694a4c8ebe13": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4865,25 +4970,23 @@ "width": null } }, - "305f6e33eec84265b93317eacfe9b6b0": { + "8468e533025147e882087efd6a01fcf1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "31a345d79d134ece901060efdb94b165": { + "86ce6751f1af40208054f5f9ef8bc580": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4936,7 +5039,25 @@ "width": null } }, - "31b855e014d84579b8434de0e51b2846": { + "8fa4dec0afd24a3fafa704c37b9a96fd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "95db50532273485690ad795e902244aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4989,71 +5110,65 @@ "width": null } }, - "4a9cf0b92b274885a650766f05b77292": { + "9e1fc069d2f5460a953347e6697fec03": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_31b855e014d84579b8434de0e51b2846", - "placeholder": "​", - "style": "IPY_MODEL_77f44dc1b8c84ab4aef3e983303eb4a2", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 696.05it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "56f377970b0e41d0a3ab38bfadd0c51b": { + "a63bc3d27ea944c88b147aafb6e82f0b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2680a7b149fd4520bb536ef2dfbaa7c2", - "placeholder": "​", - "style": "IPY_MODEL_a9661f0b7a8d447c8c47dfe0b78f61ef", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_5122a12efb9046908f7d71a4dbc25479", + "IPY_MODEL_bfc0adceb176416a99a476073e670c8d", + "IPY_MODEL_c88a31f7518b412c8d3016b69370f673" + ], + "layout": "IPY_MODEL_cac7b4500c5543e7bcc09b5e5f741c43", "tabbable": null, - "tooltip": null, - "value": "100%" + "tooltip": null } }, - "59cfff3ec22848b6944ba0bf323bd24b": { + "bf0e05073eb84ed89e62523ce475978f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "5f01f9c656284899b3a91282330101c8": { + "bfc0adceb176416a99a476073e670c8d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5069,17 +5184,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_829ff0aad29b4270b40cd9499bc93cc7", + "layout": "IPY_MODEL_f808580136494741ad209b1398c34cb6", "max": 200.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_77633068b2ef4937bf213a3297280a11", + "style": "IPY_MODEL_bf0e05073eb84ed89e62523ce475978f", "tabbable": null, "tooltip": null, "value": 200.0 } }, - "769fa00f56a8417b92d2a48b7c419f62": { + "c88a31f7518b412c8d3016b69370f673": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5094,73 +5209,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_31a345d79d134ece901060efdb94b165", + "layout": "IPY_MODEL_86ce6751f1af40208054f5f9ef8bc580", "placeholder": "​", - "style": "IPY_MODEL_305f6e33eec84265b93317eacfe9b6b0", + "style": "IPY_MODEL_8fa4dec0afd24a3fafa704c37b9a96fd", "tabbable": null, "tooltip": null, - "value": " 200/200 [00:00<00:00, 735.07it/s]" - } - }, - "77633068b2ef4937bf213a3297280a11": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "77f44dc1b8c84ab4aef3e983303eb4a2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "819cd513a50348b98c0ff3c8dd72c7bd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_92ac731fb51746b9a27792595b65bd81", - "IPY_MODEL_e345abf69b7b4519a731b4da99441fe1", - "IPY_MODEL_769fa00f56a8417b92d2a48b7c419f62" - ], - "layout": "IPY_MODEL_2ea4ee3035ec4460b45119db4b86c88e", - "tabbable": null, - "tooltip": null + "value": " 200/200 [00:00<00:00, 796.34it/s]" } }, - "829ff0aad29b4270b40cd9499bc93cc7": { + "cac7b4500c5543e7bcc09b5e5f741c43": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5213,48 +5270,33 @@ "width": null } }, - "92ac731fb51746b9a27792595b65bd81": { + "d745bcc779404a6cb60ce4bfb57949f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0cd32d52503a444d88252596c7202d70", - "placeholder": "​", - "style": "IPY_MODEL_59cfff3ec22848b6944ba0bf323bd24b", + "layout": "IPY_MODEL_6e563a24defc41f69c74511f93e3f124", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8468e533025147e882087efd6a01fcf1", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "a9661f0b7a8d447c8c47dfe0b78f61ef": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": 200.0 } }, - "ac8a16cb60b04919938bc00b2f1342f7": { + "e7bd0490ff824dacacf614c61ccd5710": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5269,16 +5311,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_56f377970b0e41d0a3ab38bfadd0c51b", - "IPY_MODEL_5f01f9c656284899b3a91282330101c8", - "IPY_MODEL_4a9cf0b92b274885a650766f05b77292" + "IPY_MODEL_37db36182460420491c4c85f86cfbf7f", + "IPY_MODEL_d745bcc779404a6cb60ce4bfb57949f1", + "IPY_MODEL_070068fc0b8a43bfa7abcf6ef25f3499" ], - "layout": "IPY_MODEL_b805213db2034e85bcf8aa102ab8cd3c", + "layout": "IPY_MODEL_594b20897d9849efa4b8c38aaef6e976", "tabbable": null, "tooltip": null } }, - "b805213db2034e85bcf8aa102ab8cd3c": { + "f808580136494741ad209b1398c34cb6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5330,48 +5372,6 @@ "visibility": null, "width": null } - }, - "c28c38c2f92e4620b4be9878c38511ce": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "e345abf69b7b4519a731b4da99441fe1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1e281a0c15b84c80941bc82a97097993", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c28c38c2f92e4620b4be9878c38511ce", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } } }, "version_major": 2, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index c8c374ab4..028fe5b51 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:51:59.182546Z", - "iopub.status.busy": "2024-09-26T14:51:59.182366Z", - "iopub.status.idle": "2024-09-26T14:52:00.393643Z", - "shell.execute_reply": "2024-09-26T14:52:00.393076Z" + "iopub.execute_input": "2024-09-26T16:47:35.046121Z", + "iopub.status.busy": "2024-09-26T16:47:35.045646Z", + "iopub.status.idle": "2024-09-26T16:47:36.230177Z", + "shell.execute_reply": "2024-09-26T16:47:36.229531Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:00.395685Z", - "iopub.status.busy": "2024-09-26T14:52:00.395388Z", - "iopub.status.idle": "2024-09-26T14:52:00.398322Z", - "shell.execute_reply": "2024-09-26T14:52:00.397857Z" + "iopub.execute_input": "2024-09-26T16:47:36.232199Z", + "iopub.status.busy": "2024-09-26T16:47:36.231912Z", + "iopub.status.idle": "2024-09-26T16:47:36.234643Z", + "shell.execute_reply": "2024-09-26T16:47:36.234155Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:00.400144Z", - "iopub.status.busy": "2024-09-26T14:52:00.399840Z", - "iopub.status.idle": "2024-09-26T14:52:00.412193Z", - "shell.execute_reply": "2024-09-26T14:52:00.411697Z" + "iopub.execute_input": "2024-09-26T16:47:36.236403Z", + "iopub.status.busy": "2024-09-26T16:47:36.236223Z", + "iopub.status.idle": "2024-09-26T16:47:36.247957Z", + "shell.execute_reply": "2024-09-26T16:47:36.247513Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:00.414113Z", - "iopub.status.busy": "2024-09-26T14:52:00.413741Z", - "iopub.status.idle": "2024-09-26T14:52:05.730687Z", - "shell.execute_reply": "2024-09-26T14:52:05.730191Z" + "iopub.execute_input": "2024-09-26T16:47:36.249689Z", + "iopub.status.busy": "2024-09-26T16:47:36.249352Z", + "iopub.status.idle": "2024-09-26T16:47:40.785559Z", + "shell.execute_reply": "2024-09-26T16:47:40.785055Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index 13b6a03e5..34d0fa687 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -835,13 +835,13 @@

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

-
+
-
+
@@ -1706,7 +1706,7 @@

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

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

diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index 6f20431e7..f6ca8c145 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:08.034662Z", - "iopub.status.busy": "2024-09-26T14:52:08.034481Z", - "iopub.status.idle": "2024-09-26T14:52:09.304690Z", - "shell.execute_reply": "2024-09-26T14:52:09.304102Z" + "iopub.execute_input": "2024-09-26T16:47:42.997626Z", + "iopub.status.busy": "2024-09-26T16:47:42.997442Z", + "iopub.status.idle": "2024-09-26T16:47:44.237527Z", + "shell.execute_reply": "2024-09-26T16:47:44.236967Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:09.306879Z", - "iopub.status.busy": "2024-09-26T14:52:09.306585Z", - "iopub.status.idle": "2024-09-26T14:52:09.310196Z", - "shell.execute_reply": "2024-09-26T14:52:09.309631Z" + "iopub.execute_input": "2024-09-26T16:47:44.240051Z", + "iopub.status.busy": "2024-09-26T16:47:44.239587Z", + "iopub.status.idle": "2024-09-26T16:47:44.242836Z", + "shell.execute_reply": "2024-09-26T16:47:44.242370Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:09.311928Z", - "iopub.status.busy": "2024-09-26T14:52:09.311543Z", - "iopub.status.idle": "2024-09-26T14:52:12.757719Z", - "shell.execute_reply": "2024-09-26T14:52:12.756901Z" + "iopub.execute_input": "2024-09-26T16:47:44.244616Z", + "iopub.status.busy": "2024-09-26T16:47:44.244264Z", + "iopub.status.idle": "2024-09-26T16:47:47.619265Z", + "shell.execute_reply": "2024-09-26T16:47:47.618620Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.760355Z", - "iopub.status.busy": "2024-09-26T14:52:12.759696Z", - "iopub.status.idle": "2024-09-26T14:52:12.813184Z", - "shell.execute_reply": "2024-09-26T14:52:12.812421Z" + "iopub.execute_input": "2024-09-26T16:47:47.621966Z", + "iopub.status.busy": "2024-09-26T16:47:47.621155Z", + "iopub.status.idle": "2024-09-26T16:47:47.665245Z", + "shell.execute_reply": "2024-09-26T16:47:47.664606Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.815571Z", - "iopub.status.busy": "2024-09-26T14:52:12.815173Z", - "iopub.status.idle": "2024-09-26T14:52:12.861989Z", - "shell.execute_reply": "2024-09-26T14:52:12.861319Z" + "iopub.execute_input": "2024-09-26T16:47:47.667499Z", + "iopub.status.busy": "2024-09-26T16:47:47.667106Z", + "iopub.status.idle": "2024-09-26T16:47:47.708224Z", + "shell.execute_reply": "2024-09-26T16:47:47.707579Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.864397Z", - "iopub.status.busy": "2024-09-26T14:52:12.863906Z", - "iopub.status.idle": "2024-09-26T14:52:12.867232Z", - "shell.execute_reply": "2024-09-26T14:52:12.866761Z" + "iopub.execute_input": "2024-09-26T16:47:47.710601Z", + "iopub.status.busy": "2024-09-26T16:47:47.710188Z", + "iopub.status.idle": "2024-09-26T16:47:47.713698Z", + "shell.execute_reply": "2024-09-26T16:47:47.713139Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.868891Z", - "iopub.status.busy": "2024-09-26T14:52:12.868591Z", - "iopub.status.idle": "2024-09-26T14:52:12.871312Z", - "shell.execute_reply": "2024-09-26T14:52:12.870766Z" + "iopub.execute_input": "2024-09-26T16:47:47.715517Z", + "iopub.status.busy": "2024-09-26T16:47:47.715211Z", + "iopub.status.idle": "2024-09-26T16:47:47.717886Z", + "shell.execute_reply": "2024-09-26T16:47:47.717344Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.873230Z", - "iopub.status.busy": "2024-09-26T14:52:12.872884Z", - "iopub.status.idle": "2024-09-26T14:52:12.897801Z", - "shell.execute_reply": "2024-09-26T14:52:12.897165Z" + "iopub.execute_input": "2024-09-26T16:47:47.719539Z", + "iopub.status.busy": "2024-09-26T16:47:47.719373Z", + "iopub.status.idle": "2024-09-26T16:47:47.744161Z", + "shell.execute_reply": "2024-09-26T16:47:47.743608Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "554f0bffd2414657b0244763906a1e3d", + "model_id": "d619885062294fd49a18ec44c741a8f8", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d70c6118368a40e3b8c24ac57cc4db26", + "model_id": "77a64867c3e34ba7a1621aced3b7de9c", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.900530Z", - "iopub.status.busy": "2024-09-26T14:52:12.900181Z", - "iopub.status.idle": "2024-09-26T14:52:12.907197Z", - "shell.execute_reply": "2024-09-26T14:52:12.906763Z" + "iopub.execute_input": "2024-09-26T16:47:47.747161Z", + "iopub.status.busy": "2024-09-26T16:47:47.746829Z", + "iopub.status.idle": "2024-09-26T16:47:47.753312Z", + "shell.execute_reply": "2024-09-26T16:47:47.752894Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.908993Z", - "iopub.status.busy": "2024-09-26T14:52:12.908664Z", - "iopub.status.idle": "2024-09-26T14:52:12.911903Z", - "shell.execute_reply": "2024-09-26T14:52:12.911461Z" + "iopub.execute_input": "2024-09-26T16:47:47.755037Z", + "iopub.status.busy": "2024-09-26T16:47:47.754709Z", + "iopub.status.idle": "2024-09-26T16:47:47.758058Z", + "shell.execute_reply": "2024-09-26T16:47:47.757626Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.913714Z", - "iopub.status.busy": "2024-09-26T14:52:12.913385Z", - "iopub.status.idle": "2024-09-26T14:52:12.919520Z", - "shell.execute_reply": "2024-09-26T14:52:12.919085Z" + "iopub.execute_input": "2024-09-26T16:47:47.759672Z", + "iopub.status.busy": "2024-09-26T16:47:47.759381Z", + "iopub.status.idle": "2024-09-26T16:47:47.765633Z", + "shell.execute_reply": "2024-09-26T16:47:47.765099Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.921164Z", - "iopub.status.busy": "2024-09-26T14:52:12.920839Z", - "iopub.status.idle": "2024-09-26T14:52:12.968393Z", - "shell.execute_reply": "2024-09-26T14:52:12.967757Z" + "iopub.execute_input": "2024-09-26T16:47:47.767223Z", + "iopub.status.busy": "2024-09-26T16:47:47.766929Z", + "iopub.status.idle": "2024-09-26T16:47:47.810265Z", + "shell.execute_reply": "2024-09-26T16:47:47.809530Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:12.970571Z", - "iopub.status.busy": "2024-09-26T14:52:12.970308Z", - "iopub.status.idle": "2024-09-26T14:52:13.022776Z", - "shell.execute_reply": "2024-09-26T14:52:13.022011Z" + "iopub.execute_input": "2024-09-26T16:47:47.812866Z", + "iopub.status.busy": "2024-09-26T16:47:47.812464Z", + "iopub.status.idle": "2024-09-26T16:47:47.852851Z", + "shell.execute_reply": "2024-09-26T16:47:47.852088Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:13.025203Z", - "iopub.status.busy": "2024-09-26T14:52:13.024937Z", - "iopub.status.idle": "2024-09-26T14:52:13.170260Z", - "shell.execute_reply": "2024-09-26T14:52:13.169652Z" + "iopub.execute_input": "2024-09-26T16:47:47.855239Z", + "iopub.status.busy": "2024-09-26T16:47:47.855002Z", + "iopub.status.idle": "2024-09-26T16:47:47.983516Z", + "shell.execute_reply": "2024-09-26T16:47:47.982838Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:13.172750Z", - "iopub.status.busy": "2024-09-26T14:52:13.171949Z", - "iopub.status.idle": "2024-09-26T14:52:16.250921Z", - "shell.execute_reply": "2024-09-26T14:52:16.250318Z" + "iopub.execute_input": "2024-09-26T16:47:47.986081Z", + "iopub.status.busy": "2024-09-26T16:47:47.985286Z", + "iopub.status.idle": "2024-09-26T16:47:51.036136Z", + "shell.execute_reply": "2024-09-26T16:47:51.035477Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:16.253054Z", - "iopub.status.busy": "2024-09-26T14:52:16.252685Z", - "iopub.status.idle": "2024-09-26T14:52:16.313315Z", - "shell.execute_reply": "2024-09-26T14:52:16.312808Z" + "iopub.execute_input": "2024-09-26T16:47:51.038019Z", + "iopub.status.busy": "2024-09-26T16:47:51.037824Z", + "iopub.status.idle": "2024-09-26T16:47:51.097120Z", + "shell.execute_reply": "2024-09-26T16:47:51.096525Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:16.315165Z", - "iopub.status.busy": "2024-09-26T14:52:16.314831Z", - "iopub.status.idle": "2024-09-26T14:52:16.358568Z", - "shell.execute_reply": "2024-09-26T14:52:16.358096Z" + "iopub.execute_input": "2024-09-26T16:47:51.098978Z", + "iopub.status.busy": "2024-09-26T16:47:51.098793Z", + "iopub.status.idle": "2024-09-26T16:47:51.139818Z", + "shell.execute_reply": "2024-09-26T16:47:51.139259Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "52d078eb", + "id": "009eedf2", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "79b5500c", + "id": "61ca24ac", "metadata": {}, "source": [ "The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n", @@ -1338,7 +1338,7 @@ }, { "cell_type": "markdown", - "id": "f114fab1", + "id": "a456caf9", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "a6fcaf91", + "id": "8fb04f62", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:16.360590Z", - "iopub.status.busy": "2024-09-26T14:52:16.360173Z", - "iopub.status.idle": "2024-09-26T14:52:16.368057Z", - "shell.execute_reply": "2024-09-26T14:52:16.367484Z" + "iopub.execute_input": "2024-09-26T16:47:51.141760Z", + "iopub.status.busy": "2024-09-26T16:47:51.141422Z", + "iopub.status.idle": "2024-09-26T16:47:51.148936Z", + "shell.execute_reply": "2024-09-26T16:47:51.148467Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "fe87ea59", + "id": "c874c70e", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1472,13 +1472,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "6c7bf69f", + "id": "de0af888", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:16.369947Z", - "iopub.status.busy": "2024-09-26T14:52:16.369620Z", - "iopub.status.idle": "2024-09-26T14:52:16.389325Z", - "shell.execute_reply": "2024-09-26T14:52:16.388736Z" + "iopub.execute_input": "2024-09-26T16:47:51.150713Z", + "iopub.status.busy": "2024-09-26T16:47:51.150373Z", + "iopub.status.idle": "2024-09-26T16:47:51.168692Z", + "shell.execute_reply": "2024-09-26T16:47:51.168254Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "c73832aa", + "id": "abfd1ffb", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:16.391059Z", - "iopub.status.busy": "2024-09-26T14:52:16.390763Z", - "iopub.status.idle": "2024-09-26T14:52:16.394252Z", - "shell.execute_reply": "2024-09-26T14:52:16.393690Z" + "iopub.execute_input": "2024-09-26T16:47:51.170308Z", + "iopub.status.busy": "2024-09-26T16:47:51.169975Z", + "iopub.status.idle": "2024-09-26T16:47:51.173268Z", + "shell.execute_reply": "2024-09-26T16:47:51.172728Z" } }, "outputs": [ @@ -1622,25 +1622,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01c2b1694c624296b114bf1d67d63cff": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0b91ac3abe0a43e4b471a93ba3834871": { + "32e30b9f6ae44fe39cf7fc64a6d7ff35": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1693,25 +1675,7 @@ "width": null } }, - "1c5e90fb88e44d4280704d7ea69107fc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "1f603edcaca0493985e74b52602fd4e9": { + "367b8a8969924c6ebbc4a4e565e8f487": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1764,70 +1728,7 @@ "width": null } }, - "24e8217d30ae40ec8e547d17a79c5035": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4d41deb2547b43daa3622e6c0f359568": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_853967e0e61449b5b519cbd25c8830fc", - "placeholder": "​", - "style": "IPY_MODEL_70ee3acf89114ab190c8c886bcad952a", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: " - } - }, - "554f0bffd2414657b0244763906a1e3d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c8c65a735e994d1792a82e7140824616", - "IPY_MODEL_77dcb3bbbcad4620bbed6ca47c4c44db", - "IPY_MODEL_e241857383f5412490f7baca022471b6" - ], - "layout": "IPY_MODEL_af8c576b995749f49b6a3ff1a3ef7338", - "tabbable": null, - "tooltip": null - } - }, - "5569a8600bc64dfebf5774ddc1b6543c": { + "376ada55c7eb419fb04664279ec549c5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1880,48 +1781,7 @@ "width": null } }, - "70745b2f7b374dbb8bf3ac849b0ce45e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1f603edcaca0493985e74b52602fd4e9", - "placeholder": "​", - "style": "IPY_MODEL_01c2b1694c624296b114bf1d67d63cff", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1566266.10it/s]" - } - }, - "70ee3acf89114ab190c8c886bcad952a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "74ffd9bb94c34169aa80dd9d157cd10d": { + "399a4a8d381a40fbb3b3bd0966a71321": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1974,33 +1834,69 @@ "width": null } }, - "77dcb3bbbcad4620bbed6ca47c4c44db": { + "5e97117dd2b54f4bb848df455f52d734": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "5fbb63076efc4ca8a0390e49a60bcaef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c8f68f3e3778452dbb68e37345ea22a9", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c7a5057a16474398af969c555e555a5d", + "layout": "IPY_MODEL_399a4a8d381a40fbb3b3bd0966a71321", + "placeholder": "​", + "style": "IPY_MODEL_d5f8a8198b4742c8b970a37984360995", "tabbable": null, "tooltip": null, - "value": 50.0 + "value": " 10000/? [00:00<00:00, 1520336.38it/s]" + } + }, + "6148ee7e78ab462fbc2008172eaada4a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_367b8a8969924c6ebbc4a4e565e8f487", + "placeholder": "​", + "style": "IPY_MODEL_805e6cd3d19a431a8d3e7397034bb36f", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for checking labels: " } }, - "853967e0e61449b5b519cbd25c8830fc": { + "6eb939ae34e74aff8e1b4a61756cdd71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2053,7 +1949,7 @@ "width": null } }, - "af8c576b995749f49b6a3ff1a3ef7338": { + "746658ac11484b8798b05320b475118b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2106,23 +2002,95 @@ "width": null } }, - "c7a5057a16474398af969c555e555a5d": { + "7469e1771e8640a6b8e3d5abf60949df": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6eb939ae34e74aff8e1b4a61756cdd71", + "placeholder": "​", + "style": "IPY_MODEL_c1a195be8f6146beb8668dbc9343f0b9", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1085819.61it/s]" + } + }, + "77a64867c3e34ba7a1621aced3b7de9c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6148ee7e78ab462fbc2008172eaada4a", + "IPY_MODEL_a97461e5777d4317b73a65a404b18456", + "IPY_MODEL_5fbb63076efc4ca8a0390e49a60bcaef" + ], + "layout": "IPY_MODEL_c846b5991d5f4f62b33e17a83c611c2c", + "tabbable": null, + "tooltip": null + } + }, + "805e6cd3d19a431a8d3e7397034bb36f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "849c9ee3d4cf4d07acf4702cb9597a8b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_376ada55c7eb419fb04664279ec549c5", + "placeholder": "​", + "style": "IPY_MODEL_ddd711a084694a399e33772e83ec167c", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: " } }, - "c7fe0e5fdfd74b84af9f7585515c62f8": { + "a97461e5777d4317b73a65a404b18456": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2138,40 +2106,35 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_fead4528991a4aafb24e155e68de7bc9", + "layout": "IPY_MODEL_eca2c0ef792b4c58ae2be260c3551ba4", "max": 50.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_24e8217d30ae40ec8e547d17a79c5035", + "style": "IPY_MODEL_ce07695bcb5c44a48f65db8550cf5fd1", "tabbable": null, "tooltip": null, "value": 50.0 } }, - "c8c65a735e994d1792a82e7140824616": { + "c1a195be8f6146beb8668dbc9343f0b9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0b91ac3abe0a43e4b471a93ba3834871", - "placeholder": "​", - "style": "IPY_MODEL_1c5e90fb88e44d4280704d7ea69107fc", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: " + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c8f68f3e3778452dbb68e37345ea22a9": { + "c846b5991d5f4f62b33e17a83c611c2c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2224,54 +2187,91 @@ "width": null } }, - "d70c6118368a40e3b8c24ac57cc4db26": { + "c8e843b08dfb48f79b4aa5ff0094cd67": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4d41deb2547b43daa3622e6c0f359568", - "IPY_MODEL_c7fe0e5fdfd74b84af9f7585515c62f8", - "IPY_MODEL_70745b2f7b374dbb8bf3ac849b0ce45e" - ], - "layout": "IPY_MODEL_74ffd9bb94c34169aa80dd9d157cd10d", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_746658ac11484b8798b05320b475118b", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5e97117dd2b54f4bb848df455f52d734", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 50.0 } }, - "e241857383f5412490f7baca022471b6": { + "ce07695bcb5c44a48f65db8550cf5fd1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "d5f8a8198b4742c8b970a37984360995": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d619885062294fd49a18ec44c741a8f8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5569a8600bc64dfebf5774ddc1b6543c", - "placeholder": "​", - "style": "IPY_MODEL_f6897d47a5094424a94e9a8a0a058c31", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_849c9ee3d4cf4d07acf4702cb9597a8b", + "IPY_MODEL_c8e843b08dfb48f79b4aa5ff0094cd67", + "IPY_MODEL_7469e1771e8640a6b8e3d5abf60949df" + ], + "layout": "IPY_MODEL_32e30b9f6ae44fe39cf7fc64a6d7ff35", "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1012407.73it/s]" + "tooltip": null } }, - "f6897d47a5094424a94e9a8a0a058c31": { + "ddd711a084694a399e33772e83ec167c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2289,7 +2289,7 @@ "text_color": null } }, - "fead4528991a4aafb24e155e68de7bc9": { + "eca2c0ef792b4c58ae2be260c3551ba4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/tutorials/improving_ml_performance.ipynb b/master/tutorials/improving_ml_performance.ipynb index d59d7a26c..f656fc151 100644 --- a/master/tutorials/improving_ml_performance.ipynb +++ b/master/tutorials/improving_ml_performance.ipynb @@ -60,10 +60,10 @@ "id": "2d638465", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:19.810405Z", - "iopub.status.busy": "2024-09-26T14:52:19.810223Z", - "iopub.status.idle": "2024-09-26T14:52:21.040404Z", - "shell.execute_reply": "2024-09-26T14:52:21.039829Z" + "iopub.execute_input": "2024-09-26T16:47:54.535195Z", + "iopub.status.busy": "2024-09-26T16:47:54.535006Z", + "iopub.status.idle": "2024-09-26T16:47:55.733521Z", + "shell.execute_reply": "2024-09-26T16:47:55.732967Z" }, "nbsphinx": "hidden" }, @@ -73,7 +73,7 @@ "dependencies = [\"cleanlab\", \"xgboost\", \"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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -99,10 +99,10 @@ "id": "b0bbf715-47c6-44ea-b15e-89800e62ee04", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.042734Z", - "iopub.status.busy": "2024-09-26T14:52:21.042166Z", - "iopub.status.idle": "2024-09-26T14:52:21.046124Z", - "shell.execute_reply": "2024-09-26T14:52:21.045639Z" + "iopub.execute_input": "2024-09-26T16:47:55.735716Z", + "iopub.status.busy": "2024-09-26T16:47:55.735319Z", + "iopub.status.idle": "2024-09-26T16:47:55.738921Z", + "shell.execute_reply": "2024-09-26T16:47:55.738470Z" } }, "outputs": [], @@ -140,10 +140,10 @@ "id": "c58f8015-d051-411c-9e03-5659cf3ad956", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.047800Z", - "iopub.status.busy": "2024-09-26T14:52:21.047493Z", - "iopub.status.idle": "2024-09-26T14:52:21.500478Z", - "shell.execute_reply": "2024-09-26T14:52:21.499906Z" + "iopub.execute_input": "2024-09-26T16:47:55.740620Z", + "iopub.status.busy": "2024-09-26T16:47:55.740276Z", + "iopub.status.idle": "2024-09-26T16:47:56.033739Z", + "shell.execute_reply": "2024-09-26T16:47:56.033249Z" } }, "outputs": [ @@ -273,10 +273,10 @@ "id": "1b5f50e6-d125-4e61-b63e-4004f0c9099a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.502342Z", - "iopub.status.busy": "2024-09-26T14:52:21.502065Z", - "iopub.status.idle": "2024-09-26T14:52:21.509359Z", - "shell.execute_reply": "2024-09-26T14:52:21.508870Z" + "iopub.execute_input": "2024-09-26T16:47:56.035764Z", + "iopub.status.busy": "2024-09-26T16:47:56.035394Z", + "iopub.status.idle": "2024-09-26T16:47:56.042905Z", + "shell.execute_reply": "2024-09-26T16:47:56.042423Z" } }, "outputs": [], @@ -312,10 +312,10 @@ "id": "a36c21e9-1c32-4df9-bd87-fffeb8c2175f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.511294Z", - "iopub.status.busy": "2024-09-26T14:52:21.510958Z", - "iopub.status.idle": "2024-09-26T14:52:21.518230Z", - "shell.execute_reply": "2024-09-26T14:52:21.517794Z" + "iopub.execute_input": "2024-09-26T16:47:56.044596Z", + "iopub.status.busy": "2024-09-26T16:47:56.044248Z", + "iopub.status.idle": "2024-09-26T16:47:56.051192Z", + "shell.execute_reply": "2024-09-26T16:47:56.050612Z" } }, "outputs": [ @@ -418,10 +418,10 @@ "id": "5f856a3a-8aae-4836-b146-9ab68d8d1c7a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.520016Z", - "iopub.status.busy": "2024-09-26T14:52:21.519670Z", - "iopub.status.idle": "2024-09-26T14:52:21.524522Z", - "shell.execute_reply": "2024-09-26T14:52:21.524038Z" + "iopub.execute_input": "2024-09-26T16:47:56.053068Z", + "iopub.status.busy": "2024-09-26T16:47:56.052727Z", + "iopub.status.idle": "2024-09-26T16:47:56.057302Z", + "shell.execute_reply": "2024-09-26T16:47:56.056846Z" } }, "outputs": [], @@ -449,10 +449,10 @@ "id": "46275634-da56-4e58-9061-8108be2b585d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.526279Z", - "iopub.status.busy": "2024-09-26T14:52:21.525942Z", - "iopub.status.idle": "2024-09-26T14:52:21.531374Z", - "shell.execute_reply": "2024-09-26T14:52:21.530921Z" + "iopub.execute_input": "2024-09-26T16:47:56.058992Z", + "iopub.status.busy": "2024-09-26T16:47:56.058659Z", + "iopub.status.idle": "2024-09-26T16:47:56.064036Z", + "shell.execute_reply": "2024-09-26T16:47:56.063598Z" } }, "outputs": [], @@ -488,10 +488,10 @@ "id": "769c4c5e-a7ff-4e02-bee5-2b2e676aec14", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.533093Z", - "iopub.status.busy": "2024-09-26T14:52:21.532754Z", - "iopub.status.idle": "2024-09-26T14:52:21.536654Z", - "shell.execute_reply": "2024-09-26T14:52:21.536203Z" + "iopub.execute_input": "2024-09-26T16:47:56.065679Z", + "iopub.status.busy": "2024-09-26T16:47:56.065338Z", + "iopub.status.idle": "2024-09-26T16:47:56.069491Z", + "shell.execute_reply": "2024-09-26T16:47:56.068945Z" } }, "outputs": [], @@ -506,10 +506,10 @@ "id": "7ac47c3d-9e87-45b7-9064-bfa45578872e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.538466Z", - "iopub.status.busy": "2024-09-26T14:52:21.538138Z", - "iopub.status.idle": "2024-09-26T14:52:21.605533Z", - "shell.execute_reply": "2024-09-26T14:52:21.604911Z" + "iopub.execute_input": "2024-09-26T16:47:56.071256Z", + "iopub.status.busy": "2024-09-26T16:47:56.070925Z", + "iopub.status.idle": "2024-09-26T16:47:56.137855Z", + "shell.execute_reply": "2024-09-26T16:47:56.137165Z" } }, "outputs": [ @@ -609,10 +609,10 @@ "id": "6cef169e-d15b-4d18-9cb7-8ea589557e6b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.608178Z", - "iopub.status.busy": "2024-09-26T14:52:21.607735Z", - "iopub.status.idle": "2024-09-26T14:52:21.620493Z", - "shell.execute_reply": "2024-09-26T14:52:21.619924Z" + "iopub.execute_input": "2024-09-26T16:47:56.140117Z", + "iopub.status.busy": "2024-09-26T16:47:56.139904Z", + "iopub.status.idle": "2024-09-26T16:47:56.151148Z", + "shell.execute_reply": "2024-09-26T16:47:56.150548Z" } }, "outputs": [ @@ -724,10 +724,10 @@ "id": "b68e0418-86cf-431f-9107-2dd0a310ca42", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.623400Z", - "iopub.status.busy": "2024-09-26T14:52:21.622546Z", - "iopub.status.idle": "2024-09-26T14:52:21.644716Z", - "shell.execute_reply": "2024-09-26T14:52:21.644193Z" + "iopub.execute_input": "2024-09-26T16:47:56.153073Z", + "iopub.status.busy": "2024-09-26T16:47:56.152869Z", + "iopub.status.idle": "2024-09-26T16:47:56.173390Z", + "shell.execute_reply": "2024-09-26T16:47:56.172785Z" } }, "outputs": [ @@ -931,10 +931,10 @@ "id": "0e9bd131-429f-48af-b4fc-ed8b907950b9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.647639Z", - "iopub.status.busy": "2024-09-26T14:52:21.646753Z", - "iopub.status.idle": "2024-09-26T14:52:21.652233Z", - "shell.execute_reply": "2024-09-26T14:52:21.651741Z" + "iopub.execute_input": "2024-09-26T16:47:56.175433Z", + "iopub.status.busy": "2024-09-26T16:47:56.175226Z", + "iopub.status.idle": "2024-09-26T16:47:56.180488Z", + "shell.execute_reply": "2024-09-26T16:47:56.179989Z" } }, "outputs": [ @@ -968,10 +968,10 @@ "id": "e72320ec-7792-4347-b2fb-630f2519127c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.654600Z", - "iopub.status.busy": "2024-09-26T14:52:21.654175Z", - "iopub.status.idle": "2024-09-26T14:52:21.659391Z", - "shell.execute_reply": "2024-09-26T14:52:21.658868Z" + "iopub.execute_input": "2024-09-26T16:47:56.183374Z", + "iopub.status.busy": "2024-09-26T16:47:56.182610Z", + "iopub.status.idle": "2024-09-26T16:47:56.188232Z", + "shell.execute_reply": "2024-09-26T16:47:56.187733Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "id": "8520ba4a-3ad6-408a-b377-3f47c32d745a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.661608Z", - "iopub.status.busy": "2024-09-26T14:52:21.661407Z", - "iopub.status.idle": "2024-09-26T14:52:21.671252Z", - "shell.execute_reply": "2024-09-26T14:52:21.670825Z" + "iopub.execute_input": "2024-09-26T16:47:56.191098Z", + "iopub.status.busy": "2024-09-26T16:47:56.190327Z", + "iopub.status.idle": "2024-09-26T16:47:56.201327Z", + "shell.execute_reply": "2024-09-26T16:47:56.200924Z" } }, "outputs": [ @@ -1205,10 +1205,10 @@ "id": "3c002665-c48b-4f04-91f7-ad112a49efc7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.673132Z", - "iopub.status.busy": "2024-09-26T14:52:21.672789Z", - "iopub.status.idle": "2024-09-26T14:52:21.677167Z", - "shell.execute_reply": "2024-09-26T14:52:21.676751Z" + "iopub.execute_input": "2024-09-26T16:47:56.203402Z", + "iopub.status.busy": "2024-09-26T16:47:56.202954Z", + "iopub.status.idle": "2024-09-26T16:47:56.207388Z", + "shell.execute_reply": "2024-09-26T16:47:56.206865Z" } }, "outputs": [], @@ -1234,10 +1234,10 @@ "id": "36319f39-f563-4f63-913f-821373180350", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.678723Z", - "iopub.status.busy": "2024-09-26T14:52:21.678550Z", - "iopub.status.idle": "2024-09-26T14:52:21.827660Z", - "shell.execute_reply": "2024-09-26T14:52:21.827142Z" + "iopub.execute_input": "2024-09-26T16:47:56.209276Z", + "iopub.status.busy": "2024-09-26T16:47:56.208859Z", + "iopub.status.idle": "2024-09-26T16:47:56.338295Z", + "shell.execute_reply": "2024-09-26T16:47:56.337800Z" } }, "outputs": [ @@ -1711,10 +1711,10 @@ "id": "044c0eb1-299a-4851-b1bf-268d5bce56c1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.829459Z", - "iopub.status.busy": "2024-09-26T14:52:21.829100Z", - "iopub.status.idle": "2024-09-26T14:52:21.835627Z", - "shell.execute_reply": "2024-09-26T14:52:21.835049Z" + "iopub.execute_input": "2024-09-26T16:47:56.340280Z", + "iopub.status.busy": "2024-09-26T16:47:56.339886Z", + "iopub.status.idle": "2024-09-26T16:47:56.346194Z", + "shell.execute_reply": "2024-09-26T16:47:56.345619Z" } }, "outputs": [], @@ -1738,10 +1738,10 @@ "id": "c43df278-abfe-40e5-9d48-2df3efea9379", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:21.837607Z", - "iopub.status.busy": "2024-09-26T14:52:21.837231Z", - "iopub.status.idle": "2024-09-26T14:52:23.851625Z", - "shell.execute_reply": "2024-09-26T14:52:23.850969Z" + "iopub.execute_input": "2024-09-26T16:47:56.348028Z", + "iopub.status.busy": "2024-09-26T16:47:56.347737Z", + "iopub.status.idle": "2024-09-26T16:47:58.347461Z", + "shell.execute_reply": "2024-09-26T16:47:58.346802Z" } }, "outputs": [ @@ -1953,10 +1953,10 @@ "id": "77c7f776-54b3-45b5-9207-715d6d2e90c0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:23.853998Z", - "iopub.status.busy": "2024-09-26T14:52:23.853506Z", - "iopub.status.idle": "2024-09-26T14:52:23.867378Z", - "shell.execute_reply": "2024-09-26T14:52:23.866868Z" + "iopub.execute_input": "2024-09-26T16:47:58.351104Z", + "iopub.status.busy": "2024-09-26T16:47:58.350154Z", + "iopub.status.idle": "2024-09-26T16:47:58.364975Z", + "shell.execute_reply": "2024-09-26T16:47:58.364458Z" } }, "outputs": [ @@ -2073,10 +2073,10 @@ "id": "7e218d04-0729-4f42-b264-51c73601ebe6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:23.869442Z", - "iopub.status.busy": "2024-09-26T14:52:23.869086Z", - "iopub.status.idle": "2024-09-26T14:52:23.871992Z", - "shell.execute_reply": "2024-09-26T14:52:23.871490Z" + "iopub.execute_input": "2024-09-26T16:47:58.368010Z", + "iopub.status.busy": "2024-09-26T16:47:58.367231Z", + "iopub.status.idle": "2024-09-26T16:47:58.370949Z", + "shell.execute_reply": "2024-09-26T16:47:58.370432Z" } }, "outputs": [], @@ -2090,10 +2090,10 @@ "id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:23.873901Z", - "iopub.status.busy": "2024-09-26T14:52:23.873567Z", - "iopub.status.idle": "2024-09-26T14:52:23.878299Z", - "shell.execute_reply": "2024-09-26T14:52:23.877773Z" + "iopub.execute_input": "2024-09-26T16:47:58.373819Z", + "iopub.status.busy": "2024-09-26T16:47:58.373057Z", + "iopub.status.idle": "2024-09-26T16:47:58.378326Z", + "shell.execute_reply": "2024-09-26T16:47:58.377824Z" } }, "outputs": [], @@ -2117,10 +2117,10 @@ "id": "5ce2d89f-e832-448d-bfac-9941da15c895", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:23.880472Z", - "iopub.status.busy": "2024-09-26T14:52:23.880009Z", - "iopub.status.idle": "2024-09-26T14:52:23.917031Z", - "shell.execute_reply": "2024-09-26T14:52:23.916497Z" + "iopub.execute_input": "2024-09-26T16:47:58.381268Z", + "iopub.status.busy": "2024-09-26T16:47:58.380485Z", + "iopub.status.idle": "2024-09-26T16:47:58.395696Z", + "shell.execute_reply": "2024-09-26T16:47:58.395139Z" } }, "outputs": [ @@ -2160,10 +2160,10 @@ "id": "9f437756-112e-4531-84fc-6ceadd0c9ef5", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:23.919143Z", - "iopub.status.busy": "2024-09-26T14:52:23.918754Z", - "iopub.status.idle": "2024-09-26T14:52:24.441145Z", - "shell.execute_reply": "2024-09-26T14:52:24.440578Z" + "iopub.execute_input": "2024-09-26T16:47:58.397886Z", + "iopub.status.busy": "2024-09-26T16:47:58.397509Z", + "iopub.status.idle": "2024-09-26T16:47:58.914150Z", + "shell.execute_reply": "2024-09-26T16:47:58.913545Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "id": "707625f6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.443535Z", - "iopub.status.busy": "2024-09-26T14:52:24.443148Z", - "iopub.status.idle": "2024-09-26T14:52:24.581215Z", - "shell.execute_reply": "2024-09-26T14:52:24.580592Z" + "iopub.execute_input": "2024-09-26T16:47:58.917583Z", + "iopub.status.busy": "2024-09-26T16:47:58.916599Z", + "iopub.status.idle": "2024-09-26T16:47:59.051939Z", + "shell.execute_reply": "2024-09-26T16:47:59.051264Z" } }, "outputs": [ @@ -2408,10 +2408,10 @@ "id": "25afe46c-a521-483c-b168-728c76d970dc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.583982Z", - "iopub.status.busy": "2024-09-26T14:52:24.583021Z", - "iopub.status.idle": "2024-09-26T14:52:24.591560Z", - "shell.execute_reply": "2024-09-26T14:52:24.591052Z" + "iopub.execute_input": "2024-09-26T16:47:59.055010Z", + "iopub.status.busy": "2024-09-26T16:47:59.054194Z", + "iopub.status.idle": "2024-09-26T16:47:59.062555Z", + "shell.execute_reply": "2024-09-26T16:47:59.062018Z" } }, "outputs": [ @@ -2441,10 +2441,10 @@ "id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.594472Z", - "iopub.status.busy": "2024-09-26T14:52:24.593722Z", - "iopub.status.idle": "2024-09-26T14:52:24.601463Z", - "shell.execute_reply": "2024-09-26T14:52:24.600918Z" + "iopub.execute_input": "2024-09-26T16:47:59.065453Z", + "iopub.status.busy": "2024-09-26T16:47:59.064693Z", + "iopub.status.idle": "2024-09-26T16:47:59.072235Z", + "shell.execute_reply": "2024-09-26T16:47:59.071728Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.604404Z", - "iopub.status.busy": "2024-09-26T14:52:24.603652Z", - "iopub.status.idle": "2024-09-26T14:52:24.610627Z", - "shell.execute_reply": "2024-09-26T14:52:24.610123Z" + "iopub.execute_input": "2024-09-26T16:47:59.075116Z", + "iopub.status.busy": "2024-09-26T16:47:59.074334Z", + "iopub.status.idle": "2024-09-26T16:47:59.081249Z", + "shell.execute_reply": "2024-09-26T16:47:59.080749Z" } }, "outputs": [ @@ -2513,10 +2513,10 @@ "id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.613514Z", - "iopub.status.busy": "2024-09-26T14:52:24.612748Z", - "iopub.status.idle": "2024-09-26T14:52:24.618379Z", - "shell.execute_reply": "2024-09-26T14:52:24.617862Z" + "iopub.execute_input": "2024-09-26T16:47:59.084123Z", + "iopub.status.busy": "2024-09-26T16:47:59.083369Z", + "iopub.status.idle": "2024-09-26T16:47:59.089039Z", + "shell.execute_reply": "2024-09-26T16:47:59.088466Z" } }, "outputs": [ @@ -2542,10 +2542,10 @@ "id": "08080458-0cd7-447d-80e6-384cb8d31eaf", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.621206Z", - "iopub.status.busy": "2024-09-26T14:52:24.620459Z", - "iopub.status.idle": "2024-09-26T14:52:24.625372Z", - "shell.execute_reply": "2024-09-26T14:52:24.624794Z" + "iopub.execute_input": "2024-09-26T16:47:59.092213Z", + "iopub.status.busy": "2024-09-26T16:47:59.091430Z", + "iopub.status.idle": "2024-09-26T16:47:59.096568Z", + "shell.execute_reply": "2024-09-26T16:47:59.096115Z" } }, "outputs": [], @@ -2569,10 +2569,10 @@ "id": "009bb215-4d26-47da-a230-d0ccf4122629", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.627070Z", - "iopub.status.busy": "2024-09-26T14:52:24.626899Z", - "iopub.status.idle": "2024-09-26T14:52:24.703448Z", - "shell.execute_reply": "2024-09-26T14:52:24.702825Z" + "iopub.execute_input": "2024-09-26T16:47:59.098276Z", + "iopub.status.busy": "2024-09-26T16:47:59.097990Z", + "iopub.status.idle": "2024-09-26T16:47:59.172717Z", + "shell.execute_reply": "2024-09-26T16:47:59.172212Z" } }, "outputs": [ @@ -3052,10 +3052,10 @@ "id": "dcaeda51-9b24-4c04-889d-7e63563594fc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.705665Z", - "iopub.status.busy": "2024-09-26T14:52:24.705281Z", - "iopub.status.idle": "2024-09-26T14:52:24.718371Z", - "shell.execute_reply": "2024-09-26T14:52:24.717910Z" + "iopub.execute_input": "2024-09-26T16:47:59.174408Z", + "iopub.status.busy": "2024-09-26T16:47:59.174231Z", + "iopub.status.idle": "2024-09-26T16:47:59.187866Z", + "shell.execute_reply": "2024-09-26T16:47:59.187306Z" } }, "outputs": [ @@ -3111,10 +3111,10 @@ "id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.719953Z", - "iopub.status.busy": "2024-09-26T14:52:24.719774Z", - "iopub.status.idle": "2024-09-26T14:52:24.722525Z", - "shell.execute_reply": "2024-09-26T14:52:24.721993Z" + "iopub.execute_input": "2024-09-26T16:47:59.190093Z", + "iopub.status.busy": "2024-09-26T16:47:59.189785Z", + "iopub.status.idle": "2024-09-26T16:47:59.192749Z", + "shell.execute_reply": "2024-09-26T16:47:59.192221Z" } }, "outputs": [], @@ -3150,10 +3150,10 @@ "id": "941ab2a6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.724217Z", - "iopub.status.busy": "2024-09-26T14:52:24.723890Z", - "iopub.status.idle": "2024-09-26T14:52:24.733856Z", - "shell.execute_reply": "2024-09-26T14:52:24.733386Z" + "iopub.execute_input": "2024-09-26T16:47:59.194421Z", + "iopub.status.busy": "2024-09-26T16:47:59.194249Z", + "iopub.status.idle": "2024-09-26T16:47:59.204777Z", + "shell.execute_reply": "2024-09-26T16:47:59.204208Z" } }, "outputs": [], @@ -3261,10 +3261,10 @@ "id": "50666fb9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.735568Z", - "iopub.status.busy": "2024-09-26T14:52:24.735390Z", - "iopub.status.idle": "2024-09-26T14:52:24.741960Z", - "shell.execute_reply": "2024-09-26T14:52:24.741508Z" + "iopub.execute_input": "2024-09-26T16:47:59.206629Z", + "iopub.status.busy": "2024-09-26T16:47:59.206434Z", + "iopub.status.idle": "2024-09-26T16:47:59.212877Z", + "shell.execute_reply": "2024-09-26T16:47:59.212424Z" }, "nbsphinx": "hidden" }, @@ -3346,10 +3346,10 @@ "id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.743631Z", - "iopub.status.busy": "2024-09-26T14:52:24.743288Z", - "iopub.status.idle": "2024-09-26T14:52:24.746500Z", - "shell.execute_reply": "2024-09-26T14:52:24.746046Z" + "iopub.execute_input": "2024-09-26T16:47:59.214539Z", + "iopub.status.busy": "2024-09-26T16:47:59.214194Z", + "iopub.status.idle": "2024-09-26T16:47:59.217309Z", + "shell.execute_reply": "2024-09-26T16:47:59.216867Z" } }, "outputs": [], @@ -3373,10 +3373,10 @@ "id": "ce1c0ada-88b1-4654-b43f-3c0b59002979", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:24.748147Z", - "iopub.status.busy": "2024-09-26T14:52:24.747796Z", - "iopub.status.idle": "2024-09-26T14:52:28.830714Z", - "shell.execute_reply": "2024-09-26T14:52:28.830201Z" + "iopub.execute_input": "2024-09-26T16:47:59.218943Z", + "iopub.status.busy": "2024-09-26T16:47:59.218613Z", + "iopub.status.idle": "2024-09-26T16:48:03.194978Z", + "shell.execute_reply": "2024-09-26T16:48:03.194447Z" } }, "outputs": [ @@ -3419,10 +3419,10 @@ "id": "3f572acf-31c3-4874-9100-451796e35b06", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:28.832745Z", - "iopub.status.busy": "2024-09-26T14:52:28.832361Z", - "iopub.status.idle": "2024-09-26T14:52:28.835718Z", - "shell.execute_reply": "2024-09-26T14:52:28.835165Z" + "iopub.execute_input": "2024-09-26T16:48:03.197052Z", + "iopub.status.busy": "2024-09-26T16:48:03.196643Z", + "iopub.status.idle": "2024-09-26T16:48:03.200280Z", + "shell.execute_reply": "2024-09-26T16:48:03.199664Z" } }, "outputs": [ @@ -3460,10 +3460,10 @@ "id": "6a025a88", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:28.837752Z", - "iopub.status.busy": "2024-09-26T14:52:28.837357Z", - "iopub.status.idle": "2024-09-26T14:52:28.840312Z", - "shell.execute_reply": "2024-09-26T14:52:28.839737Z" + "iopub.execute_input": "2024-09-26T16:48:03.202401Z", + "iopub.status.busy": "2024-09-26T16:48:03.201997Z", + "iopub.status.idle": "2024-09-26T16:48:03.204987Z", + "shell.execute_reply": "2024-09-26T16:48:03.204434Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index f81022c48..bc30e9d38 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": "2024-09-26T14:52:32.169125Z", - "iopub.status.busy": "2024-09-26T14:52:32.168956Z", - "iopub.status.idle": "2024-09-26T14:52:33.431499Z", - "shell.execute_reply": "2024-09-26T14:52:33.430884Z" + "iopub.execute_input": "2024-09-26T16:48:06.553023Z", + "iopub.status.busy": "2024-09-26T16:48:06.552853Z", + "iopub.status.idle": "2024-09-26T16:48:07.795130Z", + "shell.execute_reply": "2024-09-26T16:48:07.794475Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:52:33.434100Z", - "iopub.status.busy": "2024-09-26T14:52:33.433789Z", - "iopub.status.idle": "2024-09-26T14:52:33.621182Z", - "shell.execute_reply": "2024-09-26T14:52:33.620609Z" + "iopub.execute_input": "2024-09-26T16:48:07.797242Z", + "iopub.status.busy": "2024-09-26T16:48:07.796966Z", + "iopub.status.idle": "2024-09-26T16:48:07.974981Z", + "shell.execute_reply": "2024-09-26T16:48:07.974472Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:33.623560Z", - "iopub.status.busy": "2024-09-26T14:52:33.623109Z", - "iopub.status.idle": "2024-09-26T14:52:33.635370Z", - "shell.execute_reply": "2024-09-26T14:52:33.634790Z" + "iopub.execute_input": "2024-09-26T16:48:07.977164Z", + "iopub.status.busy": "2024-09-26T16:48:07.976814Z", + "iopub.status.idle": "2024-09-26T16:48:07.988584Z", + "shell.execute_reply": "2024-09-26T16:48:07.988135Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:33.637192Z", - "iopub.status.busy": "2024-09-26T14:52:33.636918Z", - "iopub.status.idle": "2024-09-26T14:52:33.875845Z", - "shell.execute_reply": "2024-09-26T14:52:33.875224Z" + "iopub.execute_input": "2024-09-26T16:48:07.990211Z", + "iopub.status.busy": "2024-09-26T16:48:07.990037Z", + "iopub.status.idle": "2024-09-26T16:48:08.228312Z", + "shell.execute_reply": "2024-09-26T16:48:08.227796Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:33.877984Z", - "iopub.status.busy": "2024-09-26T14:52:33.877640Z", - "iopub.status.idle": "2024-09-26T14:52:33.905047Z", - "shell.execute_reply": "2024-09-26T14:52:33.904562Z" + "iopub.execute_input": "2024-09-26T16:48:08.230136Z", + "iopub.status.busy": "2024-09-26T16:48:08.229942Z", + "iopub.status.idle": "2024-09-26T16:48:08.256644Z", + "shell.execute_reply": "2024-09-26T16:48:08.256184Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:33.906945Z", - "iopub.status.busy": "2024-09-26T14:52:33.906618Z", - "iopub.status.idle": "2024-09-26T14:52:36.066124Z", - "shell.execute_reply": "2024-09-26T14:52:36.065509Z" + "iopub.execute_input": "2024-09-26T16:48:08.258377Z", + "iopub.status.busy": "2024-09-26T16:48:08.258198Z", + "iopub.status.idle": "2024-09-26T16:48:10.362194Z", + "shell.execute_reply": "2024-09-26T16:48:10.361482Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:36.068327Z", - "iopub.status.busy": "2024-09-26T14:52:36.067791Z", - "iopub.status.idle": "2024-09-26T14:52:36.085955Z", - "shell.execute_reply": "2024-09-26T14:52:36.085444Z" + "iopub.execute_input": "2024-09-26T16:48:10.364420Z", + "iopub.status.busy": "2024-09-26T16:48:10.364067Z", + "iopub.status.idle": "2024-09-26T16:48:10.382576Z", + "shell.execute_reply": "2024-09-26T16:48:10.382074Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:36.087636Z", - "iopub.status.busy": "2024-09-26T14:52:36.087436Z", - "iopub.status.idle": "2024-09-26T14:52:37.714159Z", - "shell.execute_reply": "2024-09-26T14:52:37.713482Z" + "iopub.execute_input": "2024-09-26T16:48:10.384193Z", + "iopub.status.busy": "2024-09-26T16:48:10.384006Z", + "iopub.status.idle": "2024-09-26T16:48:11.969211Z", + "shell.execute_reply": "2024-09-26T16:48:11.968507Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:37.716539Z", - "iopub.status.busy": "2024-09-26T14:52:37.715812Z", - "iopub.status.idle": "2024-09-26T14:52:37.730102Z", - "shell.execute_reply": "2024-09-26T14:52:37.729543Z" + "iopub.execute_input": "2024-09-26T16:48:11.971754Z", + "iopub.status.busy": "2024-09-26T16:48:11.970934Z", + "iopub.status.idle": "2024-09-26T16:48:11.985165Z", + "shell.execute_reply": "2024-09-26T16:48:11.984596Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:37.731957Z", - "iopub.status.busy": "2024-09-26T14:52:37.731617Z", - "iopub.status.idle": "2024-09-26T14:52:37.821262Z", - "shell.execute_reply": "2024-09-26T14:52:37.820618Z" + "iopub.execute_input": "2024-09-26T16:48:11.986910Z", + "iopub.status.busy": "2024-09-26T16:48:11.986638Z", + "iopub.status.idle": "2024-09-26T16:48:12.073205Z", + "shell.execute_reply": "2024-09-26T16:48:12.072548Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:37.823300Z", - "iopub.status.busy": "2024-09-26T14:52:37.822839Z", - "iopub.status.idle": "2024-09-26T14:52:38.038920Z", - "shell.execute_reply": "2024-09-26T14:52:38.038375Z" + "iopub.execute_input": "2024-09-26T16:48:12.075457Z", + "iopub.status.busy": "2024-09-26T16:48:12.074967Z", + "iopub.status.idle": "2024-09-26T16:48:12.291023Z", + "shell.execute_reply": "2024-09-26T16:48:12.290463Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.040759Z", - "iopub.status.busy": "2024-09-26T14:52:38.040570Z", - "iopub.status.idle": "2024-09-26T14:52:38.058165Z", - "shell.execute_reply": "2024-09-26T14:52:38.057614Z" + "iopub.execute_input": "2024-09-26T16:48:12.292925Z", + "iopub.status.busy": "2024-09-26T16:48:12.292566Z", + "iopub.status.idle": "2024-09-26T16:48:12.309548Z", + "shell.execute_reply": "2024-09-26T16:48:12.309129Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.060074Z", - "iopub.status.busy": "2024-09-26T14:52:38.059687Z", - "iopub.status.idle": "2024-09-26T14:52:38.069888Z", - "shell.execute_reply": "2024-09-26T14:52:38.069309Z" + "iopub.execute_input": "2024-09-26T16:48:12.311261Z", + "iopub.status.busy": "2024-09-26T16:48:12.310931Z", + "iopub.status.idle": "2024-09-26T16:48:12.320409Z", + "shell.execute_reply": "2024-09-26T16:48:12.319856Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.071813Z", - "iopub.status.busy": "2024-09-26T14:52:38.071379Z", - "iopub.status.idle": "2024-09-26T14:52:38.170054Z", - "shell.execute_reply": "2024-09-26T14:52:38.169477Z" + "iopub.execute_input": "2024-09-26T16:48:12.322060Z", + "iopub.status.busy": "2024-09-26T16:48:12.321883Z", + "iopub.status.idle": "2024-09-26T16:48:12.417155Z", + "shell.execute_reply": "2024-09-26T16:48:12.416502Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.171925Z", - "iopub.status.busy": "2024-09-26T14:52:38.171696Z", - "iopub.status.idle": "2024-09-26T14:52:38.324224Z", - "shell.execute_reply": "2024-09-26T14:52:38.323549Z" + "iopub.execute_input": "2024-09-26T16:48:12.419437Z", + "iopub.status.busy": "2024-09-26T16:48:12.419075Z", + "iopub.status.idle": "2024-09-26T16:48:12.569836Z", + "shell.execute_reply": "2024-09-26T16:48:12.569209Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.326329Z", - "iopub.status.busy": "2024-09-26T14:52:38.325951Z", - "iopub.status.idle": "2024-09-26T14:52:38.329903Z", - "shell.execute_reply": "2024-09-26T14:52:38.329357Z" + "iopub.execute_input": "2024-09-26T16:48:12.572062Z", + "iopub.status.busy": "2024-09-26T16:48:12.571683Z", + "iopub.status.idle": "2024-09-26T16:48:12.575922Z", + "shell.execute_reply": "2024-09-26T16:48:12.575355Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.331907Z", - "iopub.status.busy": "2024-09-26T14:52:38.331482Z", - "iopub.status.idle": "2024-09-26T14:52:38.335196Z", - "shell.execute_reply": "2024-09-26T14:52:38.334746Z" + "iopub.execute_input": "2024-09-26T16:48:12.577700Z", + "iopub.status.busy": "2024-09-26T16:48:12.577393Z", + "iopub.status.idle": "2024-09-26T16:48:12.581358Z", + "shell.execute_reply": "2024-09-26T16:48:12.580897Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.336922Z", - "iopub.status.busy": "2024-09-26T14:52:38.336603Z", - "iopub.status.idle": "2024-09-26T14:52:38.376114Z", - "shell.execute_reply": "2024-09-26T14:52:38.375641Z" + "iopub.execute_input": "2024-09-26T16:48:12.583069Z", + "iopub.status.busy": "2024-09-26T16:48:12.582741Z", + "iopub.status.idle": "2024-09-26T16:48:12.620360Z", + "shell.execute_reply": "2024-09-26T16:48:12.619792Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.378083Z", - "iopub.status.busy": "2024-09-26T14:52:38.377733Z", - "iopub.status.idle": "2024-09-26T14:52:38.419996Z", - "shell.execute_reply": "2024-09-26T14:52:38.419527Z" + "iopub.execute_input": "2024-09-26T16:48:12.622179Z", + "iopub.status.busy": "2024-09-26T16:48:12.621840Z", + "iopub.status.idle": "2024-09-26T16:48:12.663148Z", + "shell.execute_reply": "2024-09-26T16:48:12.662655Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.421872Z", - "iopub.status.busy": "2024-09-26T14:52:38.421510Z", - "iopub.status.idle": "2024-09-26T14:52:38.531907Z", - "shell.execute_reply": "2024-09-26T14:52:38.531268Z" + "iopub.execute_input": "2024-09-26T16:48:12.664830Z", + "iopub.status.busy": "2024-09-26T16:48:12.664495Z", + "iopub.status.idle": "2024-09-26T16:48:12.770136Z", + "shell.execute_reply": "2024-09-26T16:48:12.769404Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.534145Z", - "iopub.status.busy": "2024-09-26T14:52:38.533766Z", - "iopub.status.idle": "2024-09-26T14:52:38.651268Z", - "shell.execute_reply": "2024-09-26T14:52:38.650679Z" + "iopub.execute_input": "2024-09-26T16:48:12.772418Z", + "iopub.status.busy": "2024-09-26T16:48:12.772185Z", + "iopub.status.idle": "2024-09-26T16:48:12.871572Z", + "shell.execute_reply": "2024-09-26T16:48:12.870903Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.653171Z", - "iopub.status.busy": "2024-09-26T14:52:38.652916Z", - "iopub.status.idle": "2024-09-26T14:52:38.868009Z", - "shell.execute_reply": "2024-09-26T14:52:38.867481Z" + "iopub.execute_input": "2024-09-26T16:48:12.873520Z", + "iopub.status.busy": "2024-09-26T16:48:12.873281Z", + "iopub.status.idle": "2024-09-26T16:48:13.086985Z", + "shell.execute_reply": "2024-09-26T16:48:13.086374Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:38.870022Z", - "iopub.status.busy": "2024-09-26T14:52:38.869668Z", - "iopub.status.idle": "2024-09-26T14:52:39.116995Z", - "shell.execute_reply": "2024-09-26T14:52:39.116409Z" + "iopub.execute_input": "2024-09-26T16:48:13.088923Z", + "iopub.status.busy": "2024-09-26T16:48:13.088594Z", + "iopub.status.idle": "2024-09-26T16:48:13.299972Z", + "shell.execute_reply": "2024-09-26T16:48:13.299384Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:39.119063Z", - "iopub.status.busy": "2024-09-26T14:52:39.118651Z", - "iopub.status.idle": "2024-09-26T14:52:39.124659Z", - "shell.execute_reply": "2024-09-26T14:52:39.124212Z" + "iopub.execute_input": "2024-09-26T16:48:13.302064Z", + "iopub.status.busy": "2024-09-26T16:48:13.301647Z", + "iopub.status.idle": "2024-09-26T16:48:13.307803Z", + "shell.execute_reply": "2024-09-26T16:48:13.307350Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:39.126372Z", - "iopub.status.busy": "2024-09-26T14:52:39.126025Z", - "iopub.status.idle": "2024-09-26T14:52:39.360620Z", - "shell.execute_reply": "2024-09-26T14:52:39.360015Z" + "iopub.execute_input": "2024-09-26T16:48:13.309546Z", + "iopub.status.busy": "2024-09-26T16:48:13.309226Z", + "iopub.status.idle": "2024-09-26T16:48:13.528527Z", + "shell.execute_reply": "2024-09-26T16:48:13.527944Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:39.362552Z", - "iopub.status.busy": "2024-09-26T14:52:39.362361Z", - "iopub.status.idle": "2024-09-26T14:52:40.445531Z", - "shell.execute_reply": "2024-09-26T14:52:40.444958Z" + "iopub.execute_input": "2024-09-26T16:48:13.530301Z", + "iopub.status.busy": "2024-09-26T16:48:13.530081Z", + "iopub.status.idle": "2024-09-26T16:48:14.612951Z", + "shell.execute_reply": "2024-09-26T16:48:14.612272Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index f2aa83ef9..4728163df 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:44.089068Z", - "iopub.status.busy": "2024-09-26T14:52:44.088906Z", - "iopub.status.idle": "2024-09-26T14:52:45.299550Z", - "shell.execute_reply": "2024-09-26T14:52:45.298928Z" + "iopub.execute_input": "2024-09-26T16:48:18.142269Z", + "iopub.status.busy": "2024-09-26T16:48:18.142102Z", + "iopub.status.idle": "2024-09-26T16:48:19.317022Z", + "shell.execute_reply": "2024-09-26T16:48:19.316455Z" }, "nbsphinx": "hidden" }, @@ -101,7 +101,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.301912Z", - "iopub.status.busy": "2024-09-26T14:52:45.301449Z", - "iopub.status.idle": "2024-09-26T14:52:45.304645Z", - "shell.execute_reply": "2024-09-26T14:52:45.304094Z" + "iopub.execute_input": "2024-09-26T16:48:19.319070Z", + "iopub.status.busy": "2024-09-26T16:48:19.318798Z", + "iopub.status.idle": "2024-09-26T16:48:19.321986Z", + "shell.execute_reply": "2024-09-26T16:48:19.321534Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.306413Z", - "iopub.status.busy": "2024-09-26T14:52:45.306142Z", - "iopub.status.idle": "2024-09-26T14:52:45.314151Z", - "shell.execute_reply": "2024-09-26T14:52:45.313702Z" + "iopub.execute_input": "2024-09-26T16:48:19.323721Z", + "iopub.status.busy": "2024-09-26T16:48:19.323418Z", + "iopub.status.idle": "2024-09-26T16:48:19.331345Z", + "shell.execute_reply": "2024-09-26T16:48:19.330765Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.315923Z", - "iopub.status.busy": "2024-09-26T14:52:45.315583Z", - "iopub.status.idle": "2024-09-26T14:52:45.364795Z", - "shell.execute_reply": "2024-09-26T14:52:45.364189Z" + "iopub.execute_input": "2024-09-26T16:48:19.333169Z", + "iopub.status.busy": "2024-09-26T16:48:19.332818Z", + "iopub.status.idle": "2024-09-26T16:48:19.380624Z", + "shell.execute_reply": "2024-09-26T16:48:19.379993Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.371300Z", - "iopub.status.busy": "2024-09-26T14:52:45.370858Z", - "iopub.status.idle": "2024-09-26T14:52:45.389579Z", - "shell.execute_reply": "2024-09-26T14:52:45.389064Z" + "iopub.execute_input": "2024-09-26T16:48:19.382816Z", + "iopub.status.busy": "2024-09-26T16:48:19.382354Z", + "iopub.status.idle": "2024-09-26T16:48:19.399250Z", + "shell.execute_reply": "2024-09-26T16:48:19.398688Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.391559Z", - "iopub.status.busy": "2024-09-26T14:52:45.391112Z", - "iopub.status.idle": "2024-09-26T14:52:45.395156Z", - "shell.execute_reply": "2024-09-26T14:52:45.394627Z" + "iopub.execute_input": "2024-09-26T16:48:19.401002Z", + "iopub.status.busy": "2024-09-26T16:48:19.400603Z", + "iopub.status.idle": "2024-09-26T16:48:19.404303Z", + "shell.execute_reply": "2024-09-26T16:48:19.403868Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.397035Z", - "iopub.status.busy": "2024-09-26T14:52:45.396725Z", - "iopub.status.idle": "2024-09-26T14:52:45.414290Z", - "shell.execute_reply": "2024-09-26T14:52:45.413687Z" + "iopub.execute_input": "2024-09-26T16:48:19.405876Z", + "iopub.status.busy": "2024-09-26T16:48:19.405709Z", + "iopub.status.idle": "2024-09-26T16:48:19.420365Z", + "shell.execute_reply": "2024-09-26T16:48:19.419910Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.416157Z", - "iopub.status.busy": "2024-09-26T14:52:45.415806Z", - "iopub.status.idle": "2024-09-26T14:52:45.442358Z", - "shell.execute_reply": "2024-09-26T14:52:45.441883Z" + "iopub.execute_input": "2024-09-26T16:48:19.421950Z", + "iopub.status.busy": "2024-09-26T16:48:19.421677Z", + "iopub.status.idle": "2024-09-26T16:48:19.448553Z", + "shell.execute_reply": "2024-09-26T16:48:19.448111Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:45.444293Z", - "iopub.status.busy": "2024-09-26T14:52:45.443936Z", - "iopub.status.idle": "2024-09-26T14:52:47.450691Z", - "shell.execute_reply": "2024-09-26T14:52:47.450163Z" + "iopub.execute_input": "2024-09-26T16:48:19.450361Z", + "iopub.status.busy": "2024-09-26T16:48:19.450034Z", + "iopub.status.idle": "2024-09-26T16:48:21.390331Z", + "shell.execute_reply": "2024-09-26T16:48:21.389680Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.452884Z", - "iopub.status.busy": "2024-09-26T14:52:47.452391Z", - "iopub.status.idle": "2024-09-26T14:52:47.459433Z", - "shell.execute_reply": "2024-09-26T14:52:47.458958Z" + "iopub.execute_input": "2024-09-26T16:48:21.392671Z", + "iopub.status.busy": "2024-09-26T16:48:21.392249Z", + "iopub.status.idle": "2024-09-26T16:48:21.398923Z", + "shell.execute_reply": "2024-09-26T16:48:21.398456Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.461250Z", - "iopub.status.busy": "2024-09-26T14:52:47.460913Z", - "iopub.status.idle": "2024-09-26T14:52:47.473767Z", - "shell.execute_reply": "2024-09-26T14:52:47.473271Z" + "iopub.execute_input": "2024-09-26T16:48:21.400606Z", + "iopub.status.busy": "2024-09-26T16:48:21.400272Z", + "iopub.status.idle": "2024-09-26T16:48:21.413278Z", + "shell.execute_reply": "2024-09-26T16:48:21.412720Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.475532Z", - "iopub.status.busy": "2024-09-26T14:52:47.475187Z", - "iopub.status.idle": "2024-09-26T14:52:47.481746Z", - "shell.execute_reply": "2024-09-26T14:52:47.481272Z" + "iopub.execute_input": "2024-09-26T16:48:21.415273Z", + "iopub.status.busy": "2024-09-26T16:48:21.414860Z", + "iopub.status.idle": "2024-09-26T16:48:21.421293Z", + "shell.execute_reply": "2024-09-26T16:48:21.420859Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.483691Z", - "iopub.status.busy": "2024-09-26T14:52:47.483212Z", - "iopub.status.idle": "2024-09-26T14:52:47.486088Z", - "shell.execute_reply": "2024-09-26T14:52:47.485626Z" + "iopub.execute_input": "2024-09-26T16:48:21.423003Z", + "iopub.status.busy": "2024-09-26T16:48:21.422668Z", + "iopub.status.idle": "2024-09-26T16:48:21.425203Z", + "shell.execute_reply": "2024-09-26T16:48:21.424767Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.487800Z", - "iopub.status.busy": "2024-09-26T14:52:47.487397Z", - "iopub.status.idle": "2024-09-26T14:52:47.491109Z", - "shell.execute_reply": "2024-09-26T14:52:47.490533Z" + "iopub.execute_input": "2024-09-26T16:48:21.426848Z", + "iopub.status.busy": "2024-09-26T16:48:21.426577Z", + "iopub.status.idle": "2024-09-26T16:48:21.430180Z", + "shell.execute_reply": "2024-09-26T16:48:21.429630Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.493003Z", - "iopub.status.busy": "2024-09-26T14:52:47.492607Z", - "iopub.status.idle": "2024-09-26T14:52:47.495261Z", - "shell.execute_reply": "2024-09-26T14:52:47.494806Z" + "iopub.execute_input": "2024-09-26T16:48:21.431960Z", + "iopub.status.busy": "2024-09-26T16:48:21.431634Z", + "iopub.status.idle": "2024-09-26T16:48:21.434083Z", + "shell.execute_reply": "2024-09-26T16:48:21.433650Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.497043Z", - "iopub.status.busy": "2024-09-26T14:52:47.496706Z", - "iopub.status.idle": "2024-09-26T14:52:47.500642Z", - "shell.execute_reply": "2024-09-26T14:52:47.500187Z" + "iopub.execute_input": "2024-09-26T16:48:21.435745Z", + "iopub.status.busy": "2024-09-26T16:48:21.435436Z", + "iopub.status.idle": "2024-09-26T16:48:21.439689Z", + "shell.execute_reply": "2024-09-26T16:48:21.439240Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.502313Z", - "iopub.status.busy": "2024-09-26T14:52:47.502139Z", - "iopub.status.idle": "2024-09-26T14:52:47.531332Z", - "shell.execute_reply": "2024-09-26T14:52:47.530848Z" + "iopub.execute_input": "2024-09-26T16:48:21.441490Z", + "iopub.status.busy": "2024-09-26T16:48:21.441094Z", + "iopub.status.idle": "2024-09-26T16:48:21.470480Z", + "shell.execute_reply": "2024-09-26T16:48:21.470043Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:47.533361Z", - "iopub.status.busy": "2024-09-26T14:52:47.532995Z", - "iopub.status.idle": "2024-09-26T14:52:47.537680Z", - "shell.execute_reply": "2024-09-26T14:52:47.537223Z" + "iopub.execute_input": "2024-09-26T16:48:21.472224Z", + "iopub.status.busy": "2024-09-26T16:48:21.471914Z", + "iopub.status.idle": "2024-09-26T16:48:21.476586Z", + "shell.execute_reply": "2024-09-26T16:48:21.476030Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 9b60292d7..3bffaa946 100644 --- a/master/tutorials/multilabel_classification.ipynb +++ b/master/tutorials/multilabel_classification.ipynb @@ -64,10 +64,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:50.516908Z", - "iopub.status.busy": "2024-09-26T14:52:50.516724Z", - "iopub.status.idle": "2024-09-26T14:52:51.779618Z", - "shell.execute_reply": "2024-09-26T14:52:51.779002Z" + "iopub.execute_input": "2024-09-26T16:48:24.262882Z", + "iopub.status.busy": "2024-09-26T16:48:24.262697Z", + "iopub.status.idle": "2024-09-26T16:48:25.499956Z", + "shell.execute_reply": "2024-09-26T16:48:25.499356Z" }, "nbsphinx": "hidden" }, @@ -79,7 +79,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -105,10 +105,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:51.781880Z", - "iopub.status.busy": "2024-09-26T14:52:51.781585Z", - "iopub.status.idle": "2024-09-26T14:52:51.979199Z", - "shell.execute_reply": "2024-09-26T14:52:51.978560Z" + "iopub.execute_input": "2024-09-26T16:48:25.502185Z", + "iopub.status.busy": "2024-09-26T16:48:25.501865Z", + "iopub.status.idle": "2024-09-26T16:48:25.699662Z", + "shell.execute_reply": "2024-09-26T16:48:25.699142Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:51.981718Z", - "iopub.status.busy": "2024-09-26T14:52:51.981227Z", - "iopub.status.idle": "2024-09-26T14:52:51.994745Z", - "shell.execute_reply": "2024-09-26T14:52:51.994150Z" + "iopub.execute_input": "2024-09-26T16:48:25.702141Z", + "iopub.status.busy": "2024-09-26T16:48:25.701628Z", + "iopub.status.idle": "2024-09-26T16:48:25.714875Z", + "shell.execute_reply": "2024-09-26T16:48:25.714375Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:51.996498Z", - "iopub.status.busy": "2024-09-26T14:52:51.996168Z", - "iopub.status.idle": "2024-09-26T14:52:54.626693Z", - "shell.execute_reply": "2024-09-26T14:52:54.626198Z" + "iopub.execute_input": "2024-09-26T16:48:25.716767Z", + "iopub.status.busy": "2024-09-26T16:48:25.716472Z", + "iopub.status.idle": "2024-09-26T16:48:28.390235Z", + "shell.execute_reply": "2024-09-26T16:48:28.389764Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:54.628558Z", - "iopub.status.busy": "2024-09-26T14:52:54.628209Z", - "iopub.status.idle": "2024-09-26T14:52:55.959728Z", - "shell.execute_reply": "2024-09-26T14:52:55.959162Z" + "iopub.execute_input": "2024-09-26T16:48:28.392181Z", + "iopub.status.busy": "2024-09-26T16:48:28.391835Z", + "iopub.status.idle": "2024-09-26T16:48:29.724580Z", + "shell.execute_reply": "2024-09-26T16:48:29.723927Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:55.962013Z", - "iopub.status.busy": "2024-09-26T14:52:55.961551Z", - "iopub.status.idle": "2024-09-26T14:52:55.965395Z", - "shell.execute_reply": "2024-09-26T14:52:55.964876Z" + "iopub.execute_input": "2024-09-26T16:48:29.726682Z", + "iopub.status.busy": "2024-09-26T16:48:29.726488Z", + "iopub.status.idle": "2024-09-26T16:48:29.730597Z", + "shell.execute_reply": "2024-09-26T16:48:29.730108Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:55.967241Z", - "iopub.status.busy": "2024-09-26T14:52:55.966882Z", - "iopub.status.idle": "2024-09-26T14:52:58.123639Z", - "shell.execute_reply": "2024-09-26T14:52:58.123040Z" + "iopub.execute_input": "2024-09-26T16:48:29.732159Z", + "iopub.status.busy": "2024-09-26T16:48:29.731989Z", + "iopub.status.idle": "2024-09-26T16:48:31.766277Z", + "shell.execute_reply": "2024-09-26T16:48:31.765597Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:58.126062Z", - "iopub.status.busy": "2024-09-26T14:52:58.125463Z", - "iopub.status.idle": "2024-09-26T14:52:58.134883Z", - "shell.execute_reply": "2024-09-26T14:52:58.134421Z" + "iopub.execute_input": "2024-09-26T16:48:31.768822Z", + "iopub.status.busy": "2024-09-26T16:48:31.768118Z", + "iopub.status.idle": "2024-09-26T16:48:31.776489Z", + "shell.execute_reply": "2024-09-26T16:48:31.776003Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:52:58.136727Z", - "iopub.status.busy": "2024-09-26T14:52:58.136398Z", - "iopub.status.idle": "2024-09-26T14:53:00.725562Z", - "shell.execute_reply": "2024-09-26T14:53:00.724908Z" + "iopub.execute_input": "2024-09-26T16:48:31.778053Z", + "iopub.status.busy": "2024-09-26T16:48:31.777879Z", + "iopub.status.idle": "2024-09-26T16:48:34.356589Z", + "shell.execute_reply": "2024-09-26T16:48:34.356051Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:00.727650Z", - "iopub.status.busy": "2024-09-26T14:53:00.727262Z", - "iopub.status.idle": "2024-09-26T14:53:00.731306Z", - "shell.execute_reply": "2024-09-26T14:53:00.730747Z" + "iopub.execute_input": "2024-09-26T16:48:34.358338Z", + "iopub.status.busy": "2024-09-26T16:48:34.358153Z", + "iopub.status.idle": "2024-09-26T16:48:34.361978Z", + "shell.execute_reply": "2024-09-26T16:48:34.361527Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:00.733136Z", - "iopub.status.busy": "2024-09-26T14:53:00.732824Z", - "iopub.status.idle": "2024-09-26T14:53:00.736387Z", - "shell.execute_reply": "2024-09-26T14:53:00.735914Z" + "iopub.execute_input": "2024-09-26T16:48:34.363627Z", + "iopub.status.busy": "2024-09-26T16:48:34.363450Z", + "iopub.status.idle": "2024-09-26T16:48:34.366997Z", + "shell.execute_reply": "2024-09-26T16:48:34.366411Z" } }, "outputs": [], @@ -769,10 +769,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:00.738211Z", - "iopub.status.busy": "2024-09-26T14:53:00.737791Z", - "iopub.status.idle": "2024-09-26T14:53:00.740949Z", - "shell.execute_reply": "2024-09-26T14:53:00.740494Z" + "iopub.execute_input": "2024-09-26T16:48:34.368816Z", + "iopub.status.busy": "2024-09-26T16:48:34.368483Z", + "iopub.status.idle": "2024-09-26T16:48:34.371746Z", + "shell.execute_reply": "2024-09-26T16:48:34.371169Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index 1a465fa59..e035498fb 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:03.303111Z", - "iopub.status.busy": "2024-09-26T14:53:03.302931Z", - "iopub.status.idle": "2024-09-26T14:53:04.571865Z", - "shell.execute_reply": "2024-09-26T14:53:04.571288Z" + "iopub.execute_input": "2024-09-26T16:48:37.038602Z", + "iopub.status.busy": "2024-09-26T16:48:37.038135Z", + "iopub.status.idle": "2024-09-26T16:48:38.252813Z", + "shell.execute_reply": "2024-09-26T16:48:38.252255Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:53:04.574087Z", - "iopub.status.busy": "2024-09-26T14:53:04.573598Z", - "iopub.status.idle": "2024-09-26T14:53:06.166960Z", - "shell.execute_reply": "2024-09-26T14:53:06.166164Z" + "iopub.execute_input": "2024-09-26T16:48:38.254992Z", + "iopub.status.busy": "2024-09-26T16:48:38.254563Z", + "iopub.status.idle": "2024-09-26T16:48:39.600107Z", + "shell.execute_reply": "2024-09-26T16:48:39.599320Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.169408Z", - "iopub.status.busy": "2024-09-26T14:53:06.168985Z", - "iopub.status.idle": "2024-09-26T14:53:06.172322Z", - "shell.execute_reply": "2024-09-26T14:53:06.171868Z" + "iopub.execute_input": "2024-09-26T16:48:39.602525Z", + "iopub.status.busy": "2024-09-26T16:48:39.602141Z", + "iopub.status.idle": "2024-09-26T16:48:39.605609Z", + "shell.execute_reply": "2024-09-26T16:48:39.605025Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.174071Z", - "iopub.status.busy": "2024-09-26T14:53:06.173721Z", - "iopub.status.idle": "2024-09-26T14:53:06.180705Z", - "shell.execute_reply": "2024-09-26T14:53:06.180264Z" + "iopub.execute_input": "2024-09-26T16:48:39.607514Z", + "iopub.status.busy": "2024-09-26T16:48:39.607174Z", + "iopub.status.idle": "2024-09-26T16:48:39.613965Z", + "shell.execute_reply": "2024-09-26T16:48:39.613519Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.182537Z", - "iopub.status.busy": "2024-09-26T14:53:06.182190Z", - "iopub.status.idle": "2024-09-26T14:53:06.687592Z", - "shell.execute_reply": "2024-09-26T14:53:06.686965Z" + "iopub.execute_input": "2024-09-26T16:48:39.615650Z", + "iopub.status.busy": "2024-09-26T16:48:39.615473Z", + "iopub.status.idle": "2024-09-26T16:48:40.108171Z", + "shell.execute_reply": "2024-09-26T16:48:40.107601Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.689555Z", - "iopub.status.busy": "2024-09-26T14:53:06.689377Z", - "iopub.status.idle": "2024-09-26T14:53:06.695403Z", - "shell.execute_reply": "2024-09-26T14:53:06.694799Z" + "iopub.execute_input": "2024-09-26T16:48:40.110582Z", + "iopub.status.busy": "2024-09-26T16:48:40.110385Z", + "iopub.status.idle": "2024-09-26T16:48:40.116258Z", + "shell.execute_reply": "2024-09-26T16:48:40.115836Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.697090Z", - "iopub.status.busy": "2024-09-26T14:53:06.696909Z", - "iopub.status.idle": "2024-09-26T14:53:06.700584Z", - "shell.execute_reply": "2024-09-26T14:53:06.700149Z" + "iopub.execute_input": "2024-09-26T16:48:40.117906Z", + "iopub.status.busy": "2024-09-26T16:48:40.117571Z", + "iopub.status.idle": "2024-09-26T16:48:40.121283Z", + "shell.execute_reply": "2024-09-26T16:48:40.120834Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:06.702385Z", - "iopub.status.busy": "2024-09-26T14:53:06.702049Z", - "iopub.status.idle": "2024-09-26T14:53:07.596906Z", - "shell.execute_reply": "2024-09-26T14:53:07.596232Z" + "iopub.execute_input": "2024-09-26T16:48:40.123056Z", + "iopub.status.busy": "2024-09-26T16:48:40.122724Z", + "iopub.status.idle": "2024-09-26T16:48:40.973369Z", + "shell.execute_reply": "2024-09-26T16:48:40.972808Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:07.599111Z", - "iopub.status.busy": "2024-09-26T14:53:07.598647Z", - "iopub.status.idle": "2024-09-26T14:53:07.803313Z", - "shell.execute_reply": "2024-09-26T14:53:07.802716Z" + "iopub.execute_input": "2024-09-26T16:48:40.975414Z", + "iopub.status.busy": "2024-09-26T16:48:40.975022Z", + "iopub.status.idle": "2024-09-26T16:48:41.181534Z", + "shell.execute_reply": "2024-09-26T16:48:41.181035Z" } }, "outputs": [ @@ -627,14 +627,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pruning 0 predictions out of 138 using threshold==0.0. These predictions are no longer considered as potential candidates for identifying label issues as their similarity with the given labels is no longer considered." - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "Pruning 0 predictions out of 138 using threshold==0.0. These predictions are no longer considered as potential candidates for identifying label issues as their similarity with the given labels is no longer considered.\n" ] }, { @@ -667,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:07.805415Z", - "iopub.status.busy": "2024-09-26T14:53:07.804927Z", - "iopub.status.idle": "2024-09-26T14:53:07.809280Z", - "shell.execute_reply": "2024-09-26T14:53:07.808847Z" + "iopub.execute_input": "2024-09-26T16:48:41.183524Z", + "iopub.status.busy": "2024-09-26T16:48:41.183174Z", + "iopub.status.idle": "2024-09-26T16:48:41.187742Z", + "shell.execute_reply": "2024-09-26T16:48:41.187280Z" } }, "outputs": [ @@ -707,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:07.810942Z", - "iopub.status.busy": "2024-09-26T14:53:07.810764Z", - "iopub.status.idle": "2024-09-26T14:53:08.277163Z", - "shell.execute_reply": "2024-09-26T14:53:08.276574Z" + "iopub.execute_input": "2024-09-26T16:48:41.189424Z", + "iopub.status.busy": "2024-09-26T16:48:41.189089Z", + "iopub.status.idle": "2024-09-26T16:48:41.644167Z", + "shell.execute_reply": "2024-09-26T16:48:41.643601Z" } }, "outputs": [ @@ -769,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:08.279934Z", - "iopub.status.busy": "2024-09-26T14:53:08.279727Z", - "iopub.status.idle": "2024-09-26T14:53:08.615867Z", - "shell.execute_reply": "2024-09-26T14:53:08.615304Z" + "iopub.execute_input": "2024-09-26T16:48:41.647146Z", + "iopub.status.busy": "2024-09-26T16:48:41.646765Z", + "iopub.status.idle": "2024-09-26T16:48:41.979419Z", + "shell.execute_reply": "2024-09-26T16:48:41.978885Z" } }, "outputs": [ @@ -819,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:08.617985Z", - "iopub.status.busy": "2024-09-26T14:53:08.617788Z", - "iopub.status.idle": "2024-09-26T14:53:08.987995Z", - "shell.execute_reply": "2024-09-26T14:53:08.987382Z" + "iopub.execute_input": "2024-09-26T16:48:41.981728Z", + "iopub.status.busy": "2024-09-26T16:48:41.981454Z", + "iopub.status.idle": "2024-09-26T16:48:42.347417Z", + "shell.execute_reply": "2024-09-26T16:48:42.346860Z" } }, "outputs": [ @@ -869,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:08.990870Z", - "iopub.status.busy": "2024-09-26T14:53:08.990636Z", - "iopub.status.idle": "2024-09-26T14:53:09.438626Z", - "shell.execute_reply": "2024-09-26T14:53:09.438065Z" + "iopub.execute_input": "2024-09-26T16:48:42.350252Z", + "iopub.status.busy": "2024-09-26T16:48:42.349861Z", + "iopub.status.idle": "2024-09-26T16:48:42.790573Z", + "shell.execute_reply": "2024-09-26T16:48:42.789939Z" } }, "outputs": [ @@ -932,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:09.442663Z", - "iopub.status.busy": "2024-09-26T14:53:09.442289Z", - "iopub.status.idle": "2024-09-26T14:53:09.875533Z", - "shell.execute_reply": "2024-09-26T14:53:09.874886Z" + "iopub.execute_input": "2024-09-26T16:48:42.794577Z", + "iopub.status.busy": "2024-09-26T16:48:42.794377Z", + "iopub.status.idle": "2024-09-26T16:48:43.245732Z", + "shell.execute_reply": "2024-09-26T16:48:43.245124Z" } }, "outputs": [ @@ -978,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:09.878235Z", - "iopub.status.busy": "2024-09-26T14:53:09.877876Z", - "iopub.status.idle": "2024-09-26T14:53:10.074349Z", - "shell.execute_reply": "2024-09-26T14:53:10.073721Z" + "iopub.execute_input": "2024-09-26T16:48:43.248409Z", + "iopub.status.busy": "2024-09-26T16:48:43.247937Z", + "iopub.status.idle": "2024-09-26T16:48:43.462862Z", + "shell.execute_reply": "2024-09-26T16:48:43.462278Z" } }, "outputs": [ @@ -1024,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:10.076454Z", - "iopub.status.busy": "2024-09-26T14:53:10.076093Z", - "iopub.status.idle": "2024-09-26T14:53:10.258000Z", - "shell.execute_reply": "2024-09-26T14:53:10.257430Z" + "iopub.execute_input": "2024-09-26T16:48:43.464759Z", + "iopub.status.busy": "2024-09-26T16:48:43.464356Z", + "iopub.status.idle": "2024-09-26T16:48:43.664360Z", + "shell.execute_reply": "2024-09-26T16:48:43.663754Z" } }, "outputs": [ @@ -1074,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:10.260221Z", - "iopub.status.busy": "2024-09-26T14:53:10.259868Z", - "iopub.status.idle": "2024-09-26T14:53:10.262670Z", - "shell.execute_reply": "2024-09-26T14:53:10.262238Z" + "iopub.execute_input": "2024-09-26T16:48:43.666114Z", + "iopub.status.busy": "2024-09-26T16:48:43.665828Z", + "iopub.status.idle": "2024-09-26T16:48:43.668931Z", + "shell.execute_reply": "2024-09-26T16:48:43.668363Z" } }, "outputs": [], @@ -1097,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:10.264357Z", - "iopub.status.busy": "2024-09-26T14:53:10.264032Z", - "iopub.status.idle": "2024-09-26T14:53:11.303194Z", - "shell.execute_reply": "2024-09-26T14:53:11.302561Z" + "iopub.execute_input": "2024-09-26T16:48:43.670754Z", + "iopub.status.busy": "2024-09-26T16:48:43.670355Z", + "iopub.status.idle": "2024-09-26T16:48:44.607344Z", + "shell.execute_reply": "2024-09-26T16:48:44.606795Z" } }, "outputs": [ @@ -1179,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:11.305028Z", - "iopub.status.busy": "2024-09-26T14:53:11.304725Z", - "iopub.status.idle": "2024-09-26T14:53:11.509799Z", - "shell.execute_reply": "2024-09-26T14:53:11.509285Z" + "iopub.execute_input": "2024-09-26T16:48:44.609323Z", + "iopub.status.busy": "2024-09-26T16:48:44.609143Z", + "iopub.status.idle": "2024-09-26T16:48:44.794922Z", + "shell.execute_reply": "2024-09-26T16:48:44.794464Z" } }, "outputs": [ @@ -1221,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:11.511395Z", - "iopub.status.busy": "2024-09-26T14:53:11.511212Z", - "iopub.status.idle": "2024-09-26T14:53:11.718820Z", - "shell.execute_reply": "2024-09-26T14:53:11.718199Z" + "iopub.execute_input": "2024-09-26T16:48:44.796633Z", + "iopub.status.busy": "2024-09-26T16:48:44.796300Z", + "iopub.status.idle": "2024-09-26T16:48:44.925533Z", + "shell.execute_reply": "2024-09-26T16:48:44.925049Z" } }, "outputs": [], @@ -1273,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:11.720947Z", - "iopub.status.busy": "2024-09-26T14:53:11.720765Z", - "iopub.status.idle": "2024-09-26T14:53:12.421538Z", - "shell.execute_reply": "2024-09-26T14:53:12.420820Z" + "iopub.execute_input": "2024-09-26T16:48:44.927354Z", + "iopub.status.busy": "2024-09-26T16:48:44.926951Z", + "iopub.status.idle": "2024-09-26T16:48:45.681126Z", + "shell.execute_reply": "2024-09-26T16:48:45.680563Z" } }, "outputs": [ @@ -1358,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:12.423286Z", - "iopub.status.busy": "2024-09-26T14:53:12.423091Z", - "iopub.status.idle": "2024-09-26T14:53:12.427074Z", - "shell.execute_reply": "2024-09-26T14:53:12.426599Z" + "iopub.execute_input": "2024-09-26T16:48:45.682911Z", + "iopub.status.busy": "2024-09-26T16:48:45.682624Z", + "iopub.status.idle": "2024-09-26T16:48:45.686567Z", + "shell.execute_reply": "2024-09-26T16:48:45.686134Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index b18d3d052..7117f4f32 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -784,7 +784,7 @@

2. Pre-process the Cifar10 dataset
-100%|██████████| 170498071/170498071 [00:03<00:00, 49690890.85it/s]
+100%|██████████| 170498071/170498071 [00:01<00:00, 106723750.68it/s]
 

-
+
@@ -1134,7 +1134,7 @@

Spending too much time on data quality?Cleanlab Studio – an automated platform to find and fix issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. Try it for free!

The modern AI pipeline automated with Cleanlab Studio

diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index 82b2532b4..b3dee231f 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:14.827019Z", - "iopub.status.busy": "2024-09-26T14:53:14.826845Z", - "iopub.status.idle": "2024-09-26T14:53:17.796587Z", - "shell.execute_reply": "2024-09-26T14:53:17.795936Z" + "iopub.execute_input": "2024-09-26T16:48:47.942096Z", + "iopub.status.busy": "2024-09-26T16:48:47.941917Z", + "iopub.status.idle": "2024-09-26T16:48:50.871486Z", + "shell.execute_reply": "2024-09-26T16:48:50.870921Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:53:17.798905Z", - "iopub.status.busy": "2024-09-26T14:53:17.798584Z", - "iopub.status.idle": "2024-09-26T14:53:18.137749Z", - "shell.execute_reply": "2024-09-26T14:53:18.137173Z" + "iopub.execute_input": "2024-09-26T16:48:50.873698Z", + "iopub.status.busy": "2024-09-26T16:48:50.873191Z", + "iopub.status.idle": "2024-09-26T16:48:51.206267Z", + "shell.execute_reply": "2024-09-26T16:48:51.205578Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:18.139715Z", - "iopub.status.busy": "2024-09-26T14:53:18.139407Z", - "iopub.status.idle": "2024-09-26T14:53:18.143870Z", - "shell.execute_reply": "2024-09-26T14:53:18.143450Z" + "iopub.execute_input": "2024-09-26T16:48:51.208571Z", + "iopub.status.busy": "2024-09-26T16:48:51.208081Z", + "iopub.status.idle": "2024-09-26T16:48:51.212464Z", + "shell.execute_reply": "2024-09-26T16:48:51.211895Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:18.145657Z", - "iopub.status.busy": "2024-09-26T14:53:18.145384Z", - "iopub.status.idle": "2024-09-26T14:53:24.392739Z", - "shell.execute_reply": "2024-09-26T14:53:24.392209Z" + "iopub.execute_input": "2024-09-26T16:48:51.214478Z", + "iopub.status.busy": "2024-09-26T16:48:51.214009Z", + "iopub.status.idle": "2024-09-26T16:48:55.704461Z", + "shell.execute_reply": "2024-09-26T16:48:55.703852Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 1212416/170498071 [00:00<00:14, 12024376.95it/s]" + " 1%| | 884736/170498071 [00:00<00:21, 8029939.27it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▎ | 6160384/170498071 [00:00<00:04, 33857865.84it/s]" + " 6%|▋ | 10846208/170498071 [00:00<00:02, 59627773.92it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 10518528/170498071 [00:00<00:04, 38150209.28it/s]" + " 13%|█▎ | 22052864/170498071 [00:00<00:01, 82940324.14it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 15400960/170498071 [00:00<00:03, 42330857.28it/s]" + " 20%|█▉ | 33554432/170498071 [00:00<00:01, 95390224.57it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 20250624/170498071 [00:00<00:03, 44424970.42it/s]" + " 27%|██▋ | 45252608/170498071 [00:00<00:01, 103012463.70it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 24739840/170498071 [00:00<00:03, 44347437.97it/s]" + " 33%|███▎ | 56885248/170498071 [00:00<00:01, 107499259.85it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 29294592/170498071 [00:00<00:03, 44719226.63it/s]" + " 40%|████ | 68452352/170498071 [00:00<00:00, 110123131.51it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 34144256/170498071 [00:00<00:02, 45791541.93it/s]" + " 47%|████▋ | 79986688/170498071 [00:00<00:00, 111678262.93it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 38731776/170498071 [00:00<00:02, 45062771.26it/s]" + " 54%|█████▎ | 91488256/170498071 [00:00<00:00, 112623672.78it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 43253760/170498071 [00:01<00:02, 45089662.43it/s]" + " 60%|██████ | 103022592/170498071 [00:01<00:00, 113408983.06it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 47874048/170498071 [00:01<00:02, 45213443.22it/s]" + " 67%|██████▋ | 114556928/170498071 [00:01<00:00, 113923691.11it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 52494336/170498071 [00:01<00:02, 45379651.12it/s]" + " 74%|███████▍ | 126124032/170498071 [00:01<00:00, 114394535.96it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 57049088/170498071 [00:01<00:02, 44930180.76it/s]" + " 81%|████████ | 137756672/170498071 [00:01<00:00, 114936767.22it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 61571072/170498071 [00:01<00:02, 43892355.61it/s]" + " 88%|████████▊ | 149258240/170498071 [00:01<00:00, 114805892.76it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▊ | 65994752/170498071 [00:01<00:02, 43763301.63it/s]" + " 94%|█████████▍| 160792576/170498071 [00:01<00:00, 114416952.65it/s]" ] }, { @@ -372,151 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 70385664/170498071 [00:01<00:02, 43438744.69it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 44%|████▍ | 75104256/170498071 [00:01<00:02, 44425115.39it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 47%|████▋ | 79855616/170498071 [00:01<00:02, 45166993.85it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 49%|████▉ | 84377600/170498071 [00:01<00:01, 43789983.48it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 52%|█████▏ | 88768512/170498071 [00:02<00:01, 43106787.26it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 55%|█████▍ | 93093888/170498071 [00:02<00:01, 42763173.18it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 57%|█████▋ | 97386496/170498071 [00:02<00:01, 42678693.69it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 60%|█████▉ | 101679104/170498071 [00:02<00:01, 42558052.16it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 62%|██████▏ | 106102784/170498071 [00:02<00:01, 43049601.15it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 65%|██████▍ | 110592000/170498071 [00:02<00:01, 43553293.68it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 67%|██████▋ | 114950144/170498071 [00:02<00:01, 43398814.53it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 70%|██████▉ | 119308288/170498071 [00:02<00:01, 43218682.93it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 73%|███████▎ | 125075456/170498071 [00:02<00:00, 47414945.57it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 78%|███████▊ | 133234688/170498071 [00:02<00:00, 57528916.08it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 83%|████████▎ | 141262848/170498071 [00:03<00:00, 64272093.96it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 87%|████████▋ | 149127168/170498071 [00:03<00:00, 68499385.74it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 92%|█████████▏| 157024256/170498071 [00:03<00:00, 71592148.49it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 97%|█████████▋| 165117952/170498071 [00:03<00:00, 74385700.68it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 170498071/170498071 [00:03<00:00, 49690890.85it/s]" + "100%|██████████| 170498071/170498071 [00:01<00:00, 106723750.68it/s]" ] }, { @@ -634,10 +490,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:24.394624Z", - "iopub.status.busy": "2024-09-26T14:53:24.394340Z", - "iopub.status.idle": "2024-09-26T14:53:24.399279Z", - "shell.execute_reply": "2024-09-26T14:53:24.398789Z" + "iopub.execute_input": "2024-09-26T16:48:55.706640Z", + "iopub.status.busy": "2024-09-26T16:48:55.706205Z", + "iopub.status.idle": "2024-09-26T16:48:55.711022Z", + "shell.execute_reply": "2024-09-26T16:48:55.710463Z" }, "nbsphinx": "hidden" }, @@ -688,10 +544,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:24.400938Z", - "iopub.status.busy": "2024-09-26T14:53:24.400609Z", - "iopub.status.idle": "2024-09-26T14:53:24.953810Z", - "shell.execute_reply": "2024-09-26T14:53:24.953168Z" + "iopub.execute_input": "2024-09-26T16:48:55.712782Z", + "iopub.status.busy": "2024-09-26T16:48:55.712433Z", + "iopub.status.idle": "2024-09-26T16:48:56.260960Z", + "shell.execute_reply": "2024-09-26T16:48:56.260402Z" } }, "outputs": [ @@ -724,10 +580,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:24.955849Z", - "iopub.status.busy": "2024-09-26T14:53:24.955452Z", - "iopub.status.idle": "2024-09-26T14:53:25.472907Z", - "shell.execute_reply": "2024-09-26T14:53:25.472351Z" + "iopub.execute_input": "2024-09-26T16:48:56.262948Z", + "iopub.status.busy": "2024-09-26T16:48:56.262577Z", + "iopub.status.idle": "2024-09-26T16:48:56.780598Z", + "shell.execute_reply": "2024-09-26T16:48:56.779990Z" } }, "outputs": [ @@ -765,10 +621,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:25.474962Z", - "iopub.status.busy": "2024-09-26T14:53:25.474606Z", - "iopub.status.idle": "2024-09-26T14:53:25.478282Z", - "shell.execute_reply": "2024-09-26T14:53:25.477855Z" + "iopub.execute_input": "2024-09-26T16:48:56.782340Z", + "iopub.status.busy": "2024-09-26T16:48:56.782142Z", + "iopub.status.idle": "2024-09-26T16:48:56.785768Z", + "shell.execute_reply": "2024-09-26T16:48:56.785279Z" } }, "outputs": [], @@ -791,17 +647,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:25.479985Z", - "iopub.status.busy": "2024-09-26T14:53:25.479646Z", - "iopub.status.idle": "2024-09-26T14:53:38.119311Z", - "shell.execute_reply": "2024-09-26T14:53:38.118760Z" + "iopub.execute_input": "2024-09-26T16:48:56.787224Z", + "iopub.status.busy": "2024-09-26T16:48:56.787047Z", + "iopub.status.idle": "2024-09-26T16:49:09.349226Z", + "shell.execute_reply": "2024-09-26T16:49:09.348684Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "502208beacbc4eb2877f50728ccb04c0", + "model_id": "1568567d61794ef3be7306ec89cc19ed", "version_major": 2, "version_minor": 0 }, @@ -860,10 +716,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:38.121453Z", - "iopub.status.busy": "2024-09-26T14:53:38.121019Z", - "iopub.status.idle": "2024-09-26T14:53:40.226608Z", - "shell.execute_reply": "2024-09-26T14:53:40.226078Z" + "iopub.execute_input": "2024-09-26T16:49:09.351108Z", + "iopub.status.busy": "2024-09-26T16:49:09.350912Z", + "iopub.status.idle": "2024-09-26T16:49:11.514574Z", + "shell.execute_reply": "2024-09-26T16:49:11.513950Z" } }, "outputs": [ @@ -907,10 +763,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:40.228769Z", - "iopub.status.busy": "2024-09-26T14:53:40.228334Z", - "iopub.status.idle": "2024-09-26T14:53:40.460757Z", - "shell.execute_reply": "2024-09-26T14:53:40.459979Z" + "iopub.execute_input": "2024-09-26T16:49:11.516779Z", + "iopub.status.busy": "2024-09-26T16:49:11.516299Z", + "iopub.status.idle": "2024-09-26T16:49:11.775844Z", + "shell.execute_reply": "2024-09-26T16:49:11.775225Z" } }, "outputs": [ @@ -946,10 +802,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:40.462963Z", - "iopub.status.busy": "2024-09-26T14:53:40.462510Z", - "iopub.status.idle": "2024-09-26T14:53:41.139530Z", - "shell.execute_reply": "2024-09-26T14:53:41.138920Z" + "iopub.execute_input": "2024-09-26T16:49:11.778182Z", + "iopub.status.busy": "2024-09-26T16:49:11.777875Z", + "iopub.status.idle": "2024-09-26T16:49:12.458180Z", + "shell.execute_reply": "2024-09-26T16:49:12.457592Z" } }, "outputs": [ @@ -999,10 +855,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:41.141576Z", - "iopub.status.busy": "2024-09-26T14:53:41.141387Z", - "iopub.status.idle": "2024-09-26T14:53:41.442674Z", - "shell.execute_reply": "2024-09-26T14:53:41.442054Z" + "iopub.execute_input": "2024-09-26T16:49:12.460934Z", + "iopub.status.busy": "2024-09-26T16:49:12.460410Z", + "iopub.status.idle": "2024-09-26T16:49:12.801673Z", + "shell.execute_reply": "2024-09-26T16:49:12.801157Z" } }, "outputs": [ @@ -1050,10 +906,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:41.444606Z", - "iopub.status.busy": "2024-09-26T14:53:41.444407Z", - "iopub.status.idle": "2024-09-26T14:53:41.692450Z", - "shell.execute_reply": "2024-09-26T14:53:41.691834Z" + "iopub.execute_input": "2024-09-26T16:49:12.803602Z", + "iopub.status.busy": "2024-09-26T16:49:12.803295Z", + "iopub.status.idle": "2024-09-26T16:49:13.047992Z", + "shell.execute_reply": "2024-09-26T16:49:13.047318Z" } }, "outputs": [ @@ -1109,10 +965,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:41.694792Z", - "iopub.status.busy": "2024-09-26T14:53:41.694309Z", - "iopub.status.idle": "2024-09-26T14:53:41.786453Z", - "shell.execute_reply": "2024-09-26T14:53:41.785871Z" + "iopub.execute_input": "2024-09-26T16:49:13.050430Z", + "iopub.status.busy": "2024-09-26T16:49:13.049967Z", + "iopub.status.idle": "2024-09-26T16:49:13.144064Z", + "shell.execute_reply": "2024-09-26T16:49:13.143541Z" } }, "outputs": [], @@ -1133,10 +989,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:41.788692Z", - "iopub.status.busy": "2024-09-26T14:53:41.788289Z", - "iopub.status.idle": "2024-09-26T14:53:52.391383Z", - "shell.execute_reply": "2024-09-26T14:53:52.390803Z" + "iopub.execute_input": "2024-09-26T16:49:13.146001Z", + "iopub.status.busy": "2024-09-26T16:49:13.145817Z", + "iopub.status.idle": "2024-09-26T16:49:24.322742Z", + "shell.execute_reply": "2024-09-26T16:49:24.322123Z" } }, "outputs": [ @@ -1173,10 +1029,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:52.393513Z", - "iopub.status.busy": "2024-09-26T14:53:52.393049Z", - "iopub.status.idle": "2024-09-26T14:53:54.671283Z", - "shell.execute_reply": "2024-09-26T14:53:54.670780Z" + "iopub.execute_input": "2024-09-26T16:49:24.324669Z", + "iopub.status.busy": "2024-09-26T16:49:24.324456Z", + "iopub.status.idle": "2024-09-26T16:49:26.555604Z", + "shell.execute_reply": "2024-09-26T16:49:26.555068Z" } }, "outputs": [ @@ -1207,10 +1063,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:54.673751Z", - "iopub.status.busy": "2024-09-26T14:53:54.673100Z", - "iopub.status.idle": "2024-09-26T14:53:54.874229Z", - "shell.execute_reply": "2024-09-26T14:53:54.873718Z" + "iopub.execute_input": "2024-09-26T16:49:26.557921Z", + "iopub.status.busy": "2024-09-26T16:49:26.557336Z", + "iopub.status.idle": "2024-09-26T16:49:26.765308Z", + "shell.execute_reply": "2024-09-26T16:49:26.764809Z" } }, "outputs": [], @@ -1224,10 +1080,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:54.876098Z", - "iopub.status.busy": "2024-09-26T14:53:54.875918Z", - "iopub.status.idle": "2024-09-26T14:53:54.879013Z", - "shell.execute_reply": "2024-09-26T14:53:54.878602Z" + "iopub.execute_input": "2024-09-26T16:49:26.767450Z", + "iopub.status.busy": "2024-09-26T16:49:26.766988Z", + "iopub.status.idle": "2024-09-26T16:49:26.770215Z", + "shell.execute_reply": "2024-09-26T16:49:26.769781Z" } }, "outputs": [], @@ -1265,10 +1121,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:54.880796Z", - "iopub.status.busy": "2024-09-26T14:53:54.880464Z", - "iopub.status.idle": "2024-09-26T14:53:54.888465Z", - "shell.execute_reply": "2024-09-26T14:53:54.888011Z" + "iopub.execute_input": "2024-09-26T16:49:26.771953Z", + "iopub.status.busy": "2024-09-26T16:49:26.771643Z", + "iopub.status.idle": "2024-09-26T16:49:26.780528Z", + "shell.execute_reply": "2024-09-26T16:49:26.779968Z" }, "nbsphinx": "hidden" }, @@ -1313,7 +1169,30 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "19f8d7cfcb2441f39ec909950206b100": { + "0a0ccca290d849a9a789e4b592e9595d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7a2ddabd7aa24965b01b202aab4c0f6c", + "placeholder": "​", + "style": "IPY_MODEL_a9ed174ff34f43a78a93918b71313fcc", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } + }, + "0c22791d5b4549aab88fbdb1117551c9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1366,30 +1245,47 @@ "width": null } }, - "3dbde950338e4819980320793264b8f6": { + "1568567d61794ef3be7306ec89cc19ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_444a8341757540238acd548381d3cf78", - "placeholder": "​", - "style": "IPY_MODEL_c63ffa48637c4cf790d73142dcbf1bca", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0a0ccca290d849a9a789e4b592e9595d", + "IPY_MODEL_f565c63018c44bf3a256937bc1041a5e", + "IPY_MODEL_9bc2b17042da4bac975d46c5271d017a" + ], + "layout": "IPY_MODEL_da8c44c809db4ca9b0751f81c514dde6", "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "tooltip": null } }, - "444a8341757540238acd548381d3cf78": { + "2f3b10ce827a4e0da6fcbda13879c1dd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "31ff6041b83f4b43856cf5ea87682fc6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1442,91 +1338,7 @@ "width": null } }, - "4b5509bd08094575af9bfd6e1b39af74": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "502208beacbc4eb2877f50728ccb04c0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_3dbde950338e4819980320793264b8f6", - "IPY_MODEL_55906b2e5cc7451a90a629cf8eaf9dfa", - "IPY_MODEL_e08a4a5cd5a34519a67999d955a20b6a" - ], - "layout": "IPY_MODEL_f0529d443cdc4ef783433718b133c35d", - "tabbable": null, - "tooltip": null - } - }, - "55906b2e5cc7451a90a629cf8eaf9dfa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a6577ec2ef7f4efc9edc61cb0c210c81", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6428955d0d764798b409d0eed1cd24c0", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "6428955d0d764798b409d0eed1cd24c0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "a6577ec2ef7f4efc9edc61cb0c210c81": { + "7a2ddabd7aa24965b01b202aab4c0f6c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1579,7 +1391,30 @@ "width": null } }, - "c63ffa48637c4cf790d73142dcbf1bca": { + "9bc2b17042da4bac975d46c5271d017a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0c22791d5b4549aab88fbdb1117551c9", + "placeholder": "​", + "style": "IPY_MODEL_b6a18501680c472d9c0f8df4018f9f05", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 251MB/s]" + } + }, + "a9ed174ff34f43a78a93918b71313fcc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1597,30 +1432,25 @@ "text_color": null } }, - "e08a4a5cd5a34519a67999d955a20b6a": { + "b6a18501680c472d9c0f8df4018f9f05": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_19f8d7cfcb2441f39ec909950206b100", - "placeholder": "​", - "style": "IPY_MODEL_4b5509bd08094575af9bfd6e1b39af74", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 297MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f0529d443cdc4ef783433718b133c35d": { + "da8c44c809db4ca9b0751f81c514dde6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1672,6 +1502,32 @@ "visibility": null, "width": null } + }, + "f565c63018c44bf3a256937bc1041a5e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_31ff6041b83f4b43856cf5ea87682fc6", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2f3b10ce827a4e0da6fcbda13879c1dd", + "tabbable": null, + "tooltip": null, + "value": 102469840.0 + } } }, "version_major": 2, diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index 5670e5e42..a578e0757 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:53:59.188556Z", - "iopub.status.busy": "2024-09-26T14:53:59.188370Z", - "iopub.status.idle": "2024-09-26T14:54:00.464944Z", - "shell.execute_reply": "2024-09-26T14:54:00.464378Z" + "iopub.execute_input": "2024-09-26T16:49:30.934149Z", + "iopub.status.busy": "2024-09-26T16:49:30.933967Z", + "iopub.status.idle": "2024-09-26T16:49:32.164050Z", + "shell.execute_reply": "2024-09-26T16:49:32.163421Z" }, "nbsphinx": "hidden" }, @@ -116,7 +116,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -142,10 +142,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:00.467202Z", - "iopub.status.busy": "2024-09-26T14:54:00.466665Z", - "iopub.status.idle": "2024-09-26T14:54:00.486020Z", - "shell.execute_reply": "2024-09-26T14:54:00.485402Z" + "iopub.execute_input": "2024-09-26T16:49:32.166155Z", + "iopub.status.busy": "2024-09-26T16:49:32.165898Z", + "iopub.status.idle": "2024-09-26T16:49:32.184052Z", + "shell.execute_reply": "2024-09-26T16:49:32.183625Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:00.488158Z", - "iopub.status.busy": "2024-09-26T14:54:00.487625Z", - "iopub.status.idle": "2024-09-26T14:54:00.490770Z", - "shell.execute_reply": "2024-09-26T14:54:00.490324Z" + "iopub.execute_input": "2024-09-26T16:49:32.185766Z", + "iopub.status.busy": "2024-09-26T16:49:32.185371Z", + "iopub.status.idle": "2024-09-26T16:49:32.188249Z", + "shell.execute_reply": "2024-09-26T16:49:32.187760Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:00.492476Z", - "iopub.status.busy": "2024-09-26T14:54:00.492170Z", - "iopub.status.idle": "2024-09-26T14:54:00.593026Z", - "shell.execute_reply": "2024-09-26T14:54:00.592503Z" + "iopub.execute_input": "2024-09-26T16:49:32.190088Z", + "iopub.status.busy": "2024-09-26T16:49:32.189648Z", + "iopub.status.idle": "2024-09-26T16:49:32.293466Z", + "shell.execute_reply": "2024-09-26T16:49:32.292891Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:00.595033Z", - "iopub.status.busy": "2024-09-26T14:54:00.594676Z", - "iopub.status.idle": "2024-09-26T14:54:00.781165Z", - "shell.execute_reply": "2024-09-26T14:54:00.780607Z" + "iopub.execute_input": "2024-09-26T16:49:32.295419Z", + "iopub.status.busy": "2024-09-26T16:49:32.295087Z", + "iopub.status.idle": "2024-09-26T16:49:32.475211Z", + "shell.execute_reply": "2024-09-26T16:49:32.474596Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:00.783347Z", - "iopub.status.busy": "2024-09-26T14:54:00.782969Z", - "iopub.status.idle": "2024-09-26T14:54:01.032458Z", - "shell.execute_reply": "2024-09-26T14:54:01.031929Z" + "iopub.execute_input": "2024-09-26T16:49:32.477258Z", + "iopub.status.busy": "2024-09-26T16:49:32.477063Z", + "iopub.status.idle": "2024-09-26T16:49:32.697411Z", + "shell.execute_reply": "2024-09-26T16:49:32.696786Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:01.034452Z", - "iopub.status.busy": "2024-09-26T14:54:01.034056Z", - "iopub.status.idle": "2024-09-26T14:54:01.038763Z", - "shell.execute_reply": "2024-09-26T14:54:01.038275Z" + "iopub.execute_input": "2024-09-26T16:49:32.699513Z", + "iopub.status.busy": "2024-09-26T16:49:32.699117Z", + "iopub.status.idle": "2024-09-26T16:49:32.703469Z", + "shell.execute_reply": "2024-09-26T16:49:32.703008Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:01.040507Z", - "iopub.status.busy": "2024-09-26T14:54:01.040163Z", - "iopub.status.idle": "2024-09-26T14:54:01.046197Z", - "shell.execute_reply": "2024-09-26T14:54:01.045737Z" + "iopub.execute_input": "2024-09-26T16:49:32.705261Z", + "iopub.status.busy": "2024-09-26T16:49:32.704952Z", + "iopub.status.idle": "2024-09-26T16:49:32.711173Z", + "shell.execute_reply": "2024-09-26T16:49:32.710630Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:01.048092Z", - "iopub.status.busy": "2024-09-26T14:54:01.047754Z", - "iopub.status.idle": "2024-09-26T14:54:01.050568Z", - "shell.execute_reply": "2024-09-26T14:54:01.050000Z" + "iopub.execute_input": "2024-09-26T16:49:32.712999Z", + "iopub.status.busy": "2024-09-26T16:49:32.712672Z", + "iopub.status.idle": "2024-09-26T16:49:32.715518Z", + "shell.execute_reply": "2024-09-26T16:49:32.715075Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:01.052488Z", - "iopub.status.busy": "2024-09-26T14:54:01.052092Z", - "iopub.status.idle": "2024-09-26T14:54:10.157589Z", - "shell.execute_reply": "2024-09-26T14:54:10.157001Z" + "iopub.execute_input": "2024-09-26T16:49:32.717162Z", + "iopub.status.busy": "2024-09-26T16:49:32.716830Z", + "iopub.status.idle": "2024-09-26T16:49:41.734764Z", + "shell.execute_reply": "2024-09-26T16:49:41.734156Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.160258Z", - "iopub.status.busy": "2024-09-26T14:54:10.159589Z", - "iopub.status.idle": "2024-09-26T14:54:10.167515Z", - "shell.execute_reply": "2024-09-26T14:54:10.167054Z" + "iopub.execute_input": "2024-09-26T16:49:41.737298Z", + "iopub.status.busy": "2024-09-26T16:49:41.736633Z", + "iopub.status.idle": "2024-09-26T16:49:41.744294Z", + "shell.execute_reply": "2024-09-26T16:49:41.743827Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.169285Z", - "iopub.status.busy": "2024-09-26T14:54:10.168935Z", - "iopub.status.idle": "2024-09-26T14:54:10.172611Z", - "shell.execute_reply": "2024-09-26T14:54:10.172168Z" + "iopub.execute_input": "2024-09-26T16:49:41.746155Z", + "iopub.status.busy": "2024-09-26T16:49:41.745811Z", + "iopub.status.idle": "2024-09-26T16:49:41.749437Z", + "shell.execute_reply": "2024-09-26T16:49:41.748991Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.174288Z", - "iopub.status.busy": "2024-09-26T14:54:10.173947Z", - "iopub.status.idle": "2024-09-26T14:54:10.177369Z", - "shell.execute_reply": "2024-09-26T14:54:10.176897Z" + "iopub.execute_input": "2024-09-26T16:49:41.751039Z", + "iopub.status.busy": "2024-09-26T16:49:41.750857Z", + "iopub.status.idle": "2024-09-26T16:49:41.753838Z", + "shell.execute_reply": "2024-09-26T16:49:41.753402Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.179183Z", - "iopub.status.busy": "2024-09-26T14:54:10.178849Z", - "iopub.status.idle": "2024-09-26T14:54:10.182081Z", - "shell.execute_reply": "2024-09-26T14:54:10.181652Z" + "iopub.execute_input": "2024-09-26T16:49:41.755387Z", + "iopub.status.busy": "2024-09-26T16:49:41.755213Z", + "iopub.status.idle": "2024-09-26T16:49:41.758144Z", + "shell.execute_reply": "2024-09-26T16:49:41.757690Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.183707Z", - "iopub.status.busy": "2024-09-26T14:54:10.183367Z", - "iopub.status.idle": "2024-09-26T14:54:10.191340Z", - "shell.execute_reply": "2024-09-26T14:54:10.190898Z" + "iopub.execute_input": "2024-09-26T16:49:41.759733Z", + "iopub.status.busy": "2024-09-26T16:49:41.759558Z", + "iopub.status.idle": "2024-09-26T16:49:41.767740Z", + "shell.execute_reply": "2024-09-26T16:49:41.767203Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.193003Z", - "iopub.status.busy": "2024-09-26T14:54:10.192665Z", - "iopub.status.idle": "2024-09-26T14:54:10.195213Z", - "shell.execute_reply": "2024-09-26T14:54:10.194766Z" + "iopub.execute_input": "2024-09-26T16:49:41.769406Z", + "iopub.status.busy": "2024-09-26T16:49:41.769226Z", + "iopub.status.idle": "2024-09-26T16:49:41.771789Z", + "shell.execute_reply": "2024-09-26T16:49:41.771346Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.196853Z", - "iopub.status.busy": "2024-09-26T14:54:10.196518Z", - "iopub.status.idle": "2024-09-26T14:54:10.322626Z", - "shell.execute_reply": "2024-09-26T14:54:10.322078Z" + "iopub.execute_input": "2024-09-26T16:49:41.773381Z", + "iopub.status.busy": "2024-09-26T16:49:41.773208Z", + "iopub.status.idle": "2024-09-26T16:49:41.900172Z", + "shell.execute_reply": "2024-09-26T16:49:41.899567Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.324771Z", - "iopub.status.busy": "2024-09-26T14:54:10.324359Z", - "iopub.status.idle": "2024-09-26T14:54:10.435194Z", - "shell.execute_reply": "2024-09-26T14:54:10.434642Z" + "iopub.execute_input": "2024-09-26T16:49:41.901970Z", + "iopub.status.busy": "2024-09-26T16:49:41.901791Z", + "iopub.status.idle": "2024-09-26T16:49:42.011526Z", + "shell.execute_reply": "2024-09-26T16:49:42.010936Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.437396Z", - "iopub.status.busy": "2024-09-26T14:54:10.436936Z", - "iopub.status.idle": "2024-09-26T14:54:10.943293Z", - "shell.execute_reply": "2024-09-26T14:54:10.942658Z" + "iopub.execute_input": "2024-09-26T16:49:42.013507Z", + "iopub.status.busy": "2024-09-26T16:49:42.013151Z", + "iopub.status.idle": "2024-09-26T16:49:42.521399Z", + "shell.execute_reply": "2024-09-26T16:49:42.520858Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:10.945562Z", - "iopub.status.busy": "2024-09-26T14:54:10.945188Z", - "iopub.status.idle": "2024-09-26T14:54:11.045547Z", - "shell.execute_reply": "2024-09-26T14:54:11.044913Z" + "iopub.execute_input": "2024-09-26T16:49:42.523595Z", + "iopub.status.busy": "2024-09-26T16:49:42.523202Z", + "iopub.status.idle": "2024-09-26T16:49:42.628290Z", + "shell.execute_reply": "2024-09-26T16:49:42.627757Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:11.047649Z", - "iopub.status.busy": "2024-09-26T14:54:11.047228Z", - "iopub.status.idle": "2024-09-26T14:54:11.055699Z", - "shell.execute_reply": "2024-09-26T14:54:11.055230Z" + "iopub.execute_input": "2024-09-26T16:49:42.630336Z", + "iopub.status.busy": "2024-09-26T16:49:42.629907Z", + "iopub.status.idle": "2024-09-26T16:49:42.638614Z", + "shell.execute_reply": "2024-09-26T16:49:42.638150Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:11.057456Z", - "iopub.status.busy": "2024-09-26T14:54:11.057092Z", - "iopub.status.idle": "2024-09-26T14:54:11.059706Z", - "shell.execute_reply": "2024-09-26T14:54:11.059257Z" + "iopub.execute_input": "2024-09-26T16:49:42.640252Z", + "iopub.status.busy": "2024-09-26T16:49:42.640077Z", + "iopub.status.idle": "2024-09-26T16:49:42.642912Z", + "shell.execute_reply": "2024-09-26T16:49:42.642418Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:11.061497Z", - "iopub.status.busy": "2024-09-26T14:54:11.061113Z", - "iopub.status.idle": "2024-09-26T14:54:16.702766Z", - "shell.execute_reply": "2024-09-26T14:54:16.702139Z" + "iopub.execute_input": "2024-09-26T16:49:42.644520Z", + "iopub.status.busy": "2024-09-26T16:49:42.644347Z", + "iopub.status.idle": "2024-09-26T16:49:48.426835Z", + "shell.execute_reply": "2024-09-26T16:49:48.426209Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:16.704653Z", - "iopub.status.busy": "2024-09-26T14:54:16.704460Z", - "iopub.status.idle": "2024-09-26T14:54:16.712980Z", - "shell.execute_reply": "2024-09-26T14:54:16.712530Z" + "iopub.execute_input": "2024-09-26T16:49:48.428878Z", + "iopub.status.busy": "2024-09-26T16:49:48.428500Z", + "iopub.status.idle": "2024-09-26T16:49:48.437064Z", + "shell.execute_reply": "2024-09-26T16:49:48.436506Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:16.714897Z", - "iopub.status.busy": "2024-09-26T14:54:16.714556Z", - "iopub.status.idle": "2024-09-26T14:54:16.786785Z", - "shell.execute_reply": "2024-09-26T14:54:16.786234Z" + "iopub.execute_input": "2024-09-26T16:49:48.438914Z", + "iopub.status.busy": "2024-09-26T16:49:48.438608Z", + "iopub.status.idle": "2024-09-26T16:49:48.502713Z", + "shell.execute_reply": "2024-09-26T16:49:48.502072Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html index ff97a230b..9d67055e9 100644 --- a/master/tutorials/segmentation.html +++ b/master/tutorials/segmentation.html @@ -804,13 +804,13 @@

3. Use cleanlab to find label issues

-
+
-
+

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().

@@ -1200,7 +1200,7 @@

Get label quality scores -{"state": {"d5f572bcf9e34ff5b3f799cfc3b2c03c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fc418d04bfd44dc999d29a7cfbaf1bf5": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "03edd2e8077d415a86a42428227957c1": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d5f572bcf9e34ff5b3f799cfc3b2c03c", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_fc418d04bfd44dc999d29a7cfbaf1bf5", "tabbable": null, "tooltip": null, "value": 30.0}}, "a6ce96bd4a1f4a83b1164ac6cbe3d02f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "38f90d531db8409db73b7389ee4986c2": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "5d4af35c70b14d9b95542e9fbacf5ee2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_a6ce96bd4a1f4a83b1164ac6cbe3d02f", "placeholder": "\u200b", "style": "IPY_MODEL_38f90d531db8409db73b7389ee4986c2", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "aad869b6d41d459097999efed9f5aabb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7211d82a11904799ba5182ef4f7e1762": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "d408f59a6c4642dbacacf8536dd5bb86": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_aad869b6d41d459097999efed9f5aabb", "placeholder": "\u200b", "style": "IPY_MODEL_7211d82a11904799ba5182ef4f7e1762", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007761.55it/s]"}}, "b25023ee46574f0987d4401430bdbe95": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0d3c194b71ae41699ecaf593bb466ee6": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_5d4af35c70b14d9b95542e9fbacf5ee2", "IPY_MODEL_03edd2e8077d415a86a42428227957c1", "IPY_MODEL_d408f59a6c4642dbacacf8536dd5bb86"], "layout": "IPY_MODEL_b25023ee46574f0987d4401430bdbe95", "tabbable": null, "tooltip": null}}, "496448b9dfc748d7b07ed9a700cc1ab7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c19fb70e50ef4b3aa215c397be2fa0ed": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "477c45c2e60a4cc7bc955c274f038c75": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_496448b9dfc748d7b07ed9a700cc1ab7", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c19fb70e50ef4b3aa215c397be2fa0ed", "tabbable": null, "tooltip": null, "value": 30.0}}, "cbe2b73a42764a2aacaeaee0b9c612b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d26696e0cc9f4eef935b52e6f5301e41": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "21f930a5e16b44e6896cab16aadf76b0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_cbe2b73a42764a2aacaeaee0b9c612b7", "placeholder": "\u200b", "style": "IPY_MODEL_d26696e0cc9f4eef935b52e6f5301e41", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "ffca702bd3444f1690f1f5f85493ca09": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "008823ad1c554e5fa5b1815e6e7eee3a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "15a01925ca5e45e5bb086a7b185ac53c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ffca702bd3444f1690f1f5f85493ca09", "placeholder": "\u200b", "style": "IPY_MODEL_008823ad1c554e5fa5b1815e6e7eee3a", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:25<00:00,\u2007\u20071.22it/s]"}}, "6fc3fa5fef38489287ed8d9f7c6e1c3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f246aefc67174f658fc6990471fd838b": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_21f930a5e16b44e6896cab16aadf76b0", "IPY_MODEL_477c45c2e60a4cc7bc955c274f038c75", "IPY_MODEL_15a01925ca5e45e5bb086a7b185ac53c"], "layout": "IPY_MODEL_6fc3fa5fef38489287ed8d9f7c6e1c3e", "tabbable": null, "tooltip": null}}, "3539b50ce0e843448d49322ce25b2b2e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5ab8498d427444c6b4d07bf8d5bc6157": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "c126cab8ab9c4826849b4c390465afaf": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3539b50ce0e843448d49322ce25b2b2e", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5ab8498d427444c6b4d07bf8d5bc6157", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "6c7533cc89b74b90bdcf06dda9d4297f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "aedea8fe506b40c1933ac0b06c3dc5c7": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2e397451daaa420aac06b58d115ddb89": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6c7533cc89b74b90bdcf06dda9d4297f", "placeholder": "\u200b", "style": "IPY_MODEL_aedea8fe506b40c1933ac0b06c3dc5c7", "tabbable": null, "tooltip": null, "value": "100%"}}, "11d7b29b91e94ee382b5c3abbb5da356": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2cada3550c7444318e24a19d4c5bac92": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "1b8ddda746534779bbbb5fd4b8f8df0b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_11d7b29b91e94ee382b5c3abbb5da356", "placeholder": "\u200b", "style": "IPY_MODEL_2cada3550c7444318e24a19d4c5bac92", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:32<00:00,\u2007153968.10it/s]"}}, "60825090590c4420817f531a12ba0cb9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b6f8c999233c44e6b60c123e18607ca1": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_2e397451daaa420aac06b58d115ddb89", "IPY_MODEL_c126cab8ab9c4826849b4c390465afaf", "IPY_MODEL_1b8ddda746534779bbbb5fd4b8f8df0b"], "layout": "IPY_MODEL_60825090590c4420817f531a12ba0cb9", "tabbable": null, "tooltip": null}}, "414724ec89444e8ebc1105e3c21216d3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c0780c5558e44bdf9cd38943fbc6879f": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "db3abba05009401583103fd3bfc35643": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_414724ec89444e8ebc1105e3c21216d3", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c0780c5558e44bdf9cd38943fbc6879f", "tabbable": null, "tooltip": null, "value": 30.0}}, "038939be2791404a8d8b3535498c5720": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e7222a4d37404d41a68f2bf782915ef2": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "e15e2d1b74894e47b98ed243861d83d8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_038939be2791404a8d8b3535498c5720", "placeholder": "\u200b", "style": "IPY_MODEL_e7222a4d37404d41a68f2bf782915ef2", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "aeabc766c99c4e2b8edffb93d948620a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cbf3bc2871f144bda8f96df51315cc6a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "4db5293fd3e94b6eb261d17cfdd19337": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_aeabc766c99c4e2b8edffb93d948620a", "placeholder": "\u200b", "style": "IPY_MODEL_cbf3bc2871f144bda8f96df51315cc6a", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200720.31it/s]"}}, "c41365fd01984997be6e7450cfa7d4d5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5444a2dc1c4c403ab396248114105df7": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_e15e2d1b74894e47b98ed243861d83d8", "IPY_MODEL_db3abba05009401583103fd3bfc35643", "IPY_MODEL_4db5293fd3e94b6eb261d17cfdd19337"], "layout": "IPY_MODEL_c41365fd01984997be6e7450cfa7d4d5", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"2067dcea68e84063ad1f6a5054c3e789": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fda6bf3d4a3349d082a2341f068a5e89": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "af36ee45554e45998cbe073cbd448f30": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_2067dcea68e84063ad1f6a5054c3e789", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_fda6bf3d4a3349d082a2341f068a5e89", "tabbable": null, "tooltip": null, "value": 30.0}}, "b6f143853a084fb0b6ccc250fe93236e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fe0fc4bc25ce44089ff347ab28732dab": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "190e7a9469bc4e24a369b43d468f6485": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b6f143853a084fb0b6ccc250fe93236e", "placeholder": "\u200b", "style": "IPY_MODEL_fe0fc4bc25ce44089ff347ab28732dab", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "53b638ae41f3478f99d13a3ebb42169f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "42ac1b98a0284f3892253b28e716c7ad": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "fefa0f5d00b74ed48732a140b6e290ba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_53b638ae41f3478f99d13a3ebb42169f", "placeholder": "\u200b", "style": "IPY_MODEL_42ac1b98a0284f3892253b28e716c7ad", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007822.88it/s]"}}, "013d5146b8714b33a9b49c5875a584bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "502b9ff8baad4af5b5dddfdbca9a8ab9": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_190e7a9469bc4e24a369b43d468f6485", "IPY_MODEL_af36ee45554e45998cbe073cbd448f30", "IPY_MODEL_fefa0f5d00b74ed48732a140b6e290ba"], "layout": "IPY_MODEL_013d5146b8714b33a9b49c5875a584bf", "tabbable": null, "tooltip": null}}, "a701b3a09a964c25b447425ed13b3e2c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "861c793818bd43bca46ff4897a3a44fa": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "7a004d25557e444496cc520252ba637f": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_a701b3a09a964c25b447425ed13b3e2c", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_861c793818bd43bca46ff4897a3a44fa", "tabbable": null, "tooltip": null, "value": 30.0}}, "6149d5ce9db84ce78a209d8113fbcdeb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6de0625b60cd4c02b120f1de805e03c9": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b1ecb6440eeb484cab4c6d3a1846d880": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6149d5ce9db84ce78a209d8113fbcdeb", "placeholder": "\u200b", "style": "IPY_MODEL_6de0625b60cd4c02b120f1de805e03c9", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "0121c98358854d4f8fe3347d2c02f405": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "245b1a0dff754ba583337f4d70981efe": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f411da9220e6477ebc385b8c9da96582": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0121c98358854d4f8fe3347d2c02f405", "placeholder": "\u200b", "style": "IPY_MODEL_245b1a0dff754ba583337f4d70981efe", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:25<00:00,\u2007\u20071.17it/s]"}}, "b7f3da04e1de47889cbec5d9b381d330": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bb7371c71aa847f2b4d79a007cc0db13": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b1ecb6440eeb484cab4c6d3a1846d880", "IPY_MODEL_7a004d25557e444496cc520252ba637f", "IPY_MODEL_f411da9220e6477ebc385b8c9da96582"], "layout": "IPY_MODEL_b7f3da04e1de47889cbec5d9b381d330", "tabbable": null, "tooltip": null}}, "60f7f25133834746944894473307e0e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1970eb3b8ea140959defd0687c894d97": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "e3dce47f83364b91ab69a6c0d0ada3a5": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_60f7f25133834746944894473307e0e7", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_1970eb3b8ea140959defd0687c894d97", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "b1b74b4c0287481381f3cd409688ae72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e9fd87514e6b4549b129af0d6e8a05f0": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "1c4b81bd340f4a0295988a552f7a58bb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b1b74b4c0287481381f3cd409688ae72", "placeholder": "\u200b", "style": "IPY_MODEL_e9fd87514e6b4549b129af0d6e8a05f0", "tabbable": null, "tooltip": null, "value": "100%"}}, "47581e85de3940b587f67825e19622c5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "97feb3768a2e4dbda7a36b18153e5c0f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "20ab2ca6c5f64a7ea18f346ddf743a8b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_47581e85de3940b587f67825e19622c5", "placeholder": "\u200b", "style": "IPY_MODEL_97feb3768a2e4dbda7a36b18153e5c0f", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:31<00:00,\u2007158200.14it/s]"}}, "32926b1dbb4b452d965d85b9ab3280bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a82830a9b3954f49ba5ec182f4498604": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_1c4b81bd340f4a0295988a552f7a58bb", "IPY_MODEL_e3dce47f83364b91ab69a6c0d0ada3a5", "IPY_MODEL_20ab2ca6c5f64a7ea18f346ddf743a8b"], "layout": "IPY_MODEL_32926b1dbb4b452d965d85b9ab3280bb", "tabbable": null, "tooltip": null}}, "0c78a349af244bf3bc6ee925f47cf139": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a95f2478462247808e7fbfeaf7f4269c": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "44eb89ac642f451e9d987ca3647bdbe2": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0c78a349af244bf3bc6ee925f47cf139", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a95f2478462247808e7fbfeaf7f4269c", "tabbable": null, "tooltip": null, "value": 30.0}}, "e417d1d7ab4b479ba1a24db21005ee18": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "25ab34454075497b970ae157d65bc0f2": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "abb25c23e4fe4deb9022759c984b7e1a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e417d1d7ab4b479ba1a24db21005ee18", "placeholder": "\u200b", "style": "IPY_MODEL_25ab34454075497b970ae157d65bc0f2", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "0b9d95e573da454fa4d21aa6a8707152": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9939290b74ec4ebe886ed5834f42faf6": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "a24f9eab3fb94dc2990c0f506f2f987a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0b9d95e573da454fa4d21aa6a8707152", "placeholder": "\u200b", "style": "IPY_MODEL_9939290b74ec4ebe886ed5834f42faf6", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200719.48it/s]"}}, "69d0fc942d0d4e6a97445f38e4d57ea2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bdb101c173114412ac307c8cf1b57cbf": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_abb25c23e4fe4deb9022759c984b7e1a", "IPY_MODEL_44eb89ac642f451e9d987ca3647bdbe2", "IPY_MODEL_a24f9eab3fb94dc2990c0f506f2f987a"], "layout": "IPY_MODEL_69d0fc942d0d4e6a97445f38e4d57ea2", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index 6779478cb..b69ce6189 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:20.095591Z", - "iopub.status.busy": "2024-09-26T14:54:20.095416Z", - "iopub.status.idle": "2024-09-26T14:54:23.030061Z", - "shell.execute_reply": "2024-09-26T14:54:23.029312Z" + "iopub.execute_input": "2024-09-26T16:49:51.750531Z", + "iopub.status.busy": "2024-09-26T16:49:51.750346Z", + "iopub.status.idle": "2024-09-26T16:49:53.816346Z", + "shell.execute_reply": "2024-09-26T16:49:53.815636Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:54:23.032402Z", - "iopub.status.busy": "2024-09-26T14:54:23.032024Z", - "iopub.status.idle": "2024-09-26T14:55:28.921952Z", - "shell.execute_reply": "2024-09-26T14:55:28.921155Z" + "iopub.execute_input": "2024-09-26T16:49:53.818373Z", + "iopub.status.busy": "2024-09-26T16:49:53.818182Z", + "iopub.status.idle": "2024-09-26T16:50:58.968730Z", + "shell.execute_reply": "2024-09-26T16:50:58.968046Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:55:28.924172Z", - "iopub.status.busy": "2024-09-26T14:55:28.923971Z", - "iopub.status.idle": "2024-09-26T14:55:30.137143Z", - "shell.execute_reply": "2024-09-26T14:55:30.136538Z" + "iopub.execute_input": "2024-09-26T16:50:58.970954Z", + "iopub.status.busy": "2024-09-26T16:50:58.970572Z", + "iopub.status.idle": "2024-09-26T16:51:00.191978Z", + "shell.execute_reply": "2024-09-26T16:51:00.191433Z" }, "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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\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": "2024-09-26T14:55:30.139396Z", - "iopub.status.busy": "2024-09-26T14:55:30.139106Z", - "iopub.status.idle": "2024-09-26T14:55:30.142481Z", - "shell.execute_reply": "2024-09-26T14:55:30.141914Z" + "iopub.execute_input": "2024-09-26T16:51:00.194104Z", + "iopub.status.busy": "2024-09-26T16:51:00.193692Z", + "iopub.status.idle": "2024-09-26T16:51:00.197076Z", + "shell.execute_reply": "2024-09-26T16:51:00.196619Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:55:30.144228Z", - "iopub.status.busy": "2024-09-26T14:55:30.144050Z", - "iopub.status.idle": "2024-09-26T14:55:30.147926Z", - "shell.execute_reply": "2024-09-26T14:55:30.147419Z" + "iopub.execute_input": "2024-09-26T16:51:00.198776Z", + "iopub.status.busy": "2024-09-26T16:51:00.198500Z", + "iopub.status.idle": "2024-09-26T16:51:00.202390Z", + "shell.execute_reply": "2024-09-26T16:51:00.201918Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:55:30.149873Z", - "iopub.status.busy": "2024-09-26T14:55:30.149499Z", - "iopub.status.idle": "2024-09-26T14:55:30.153440Z", - "shell.execute_reply": "2024-09-26T14:55:30.152904Z" + "iopub.execute_input": "2024-09-26T16:51:00.204130Z", + "iopub.status.busy": "2024-09-26T16:51:00.203796Z", + "iopub.status.idle": "2024-09-26T16:51:00.207281Z", + "shell.execute_reply": "2024-09-26T16:51:00.206836Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:55:30.155269Z", - "iopub.status.busy": "2024-09-26T14:55:30.154919Z", - "iopub.status.idle": "2024-09-26T14:55:30.158026Z", - "shell.execute_reply": "2024-09-26T14:55:30.157441Z" + "iopub.execute_input": "2024-09-26T16:51:00.208813Z", + "iopub.status.busy": "2024-09-26T16:51:00.208544Z", + "iopub.status.idle": "2024-09-26T16:51:00.211286Z", + "shell.execute_reply": "2024-09-26T16:51:00.210833Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:55:30.159991Z", - "iopub.status.busy": "2024-09-26T14:55:30.159527Z", - "iopub.status.idle": "2024-09-26T14:56:07.853263Z", - "shell.execute_reply": "2024-09-26T14:56:07.852683Z" + "iopub.execute_input": "2024-09-26T16:51:00.212837Z", + "iopub.status.busy": "2024-09-26T16:51:00.212655Z", + "iopub.status.idle": "2024-09-26T16:51:38.202000Z", + "shell.execute_reply": "2024-09-26T16:51:38.201357Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d3c194b71ae41699ecaf593bb466ee6", + "model_id": "502b9ff8baad4af5b5dddfdbca9a8ab9", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f246aefc67174f658fc6990471fd838b", + "model_id": "bb7371c71aa847f2b4d79a007cc0db13", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:56:07.855727Z", - "iopub.status.busy": "2024-09-26T14:56:07.855280Z", - "iopub.status.idle": "2024-09-26T14:56:08.539218Z", - "shell.execute_reply": "2024-09-26T14:56:08.538732Z" + "iopub.execute_input": "2024-09-26T16:51:38.204539Z", + "iopub.status.busy": "2024-09-26T16:51:38.204042Z", + "iopub.status.idle": "2024-09-26T16:51:38.876296Z", + "shell.execute_reply": "2024-09-26T16:51:38.875807Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:56:08.541245Z", - "iopub.status.busy": "2024-09-26T14:56:08.540794Z", - "iopub.status.idle": "2024-09-26T14:56:11.382690Z", - "shell.execute_reply": "2024-09-26T14:56:11.382214Z" + "iopub.execute_input": "2024-09-26T16:51:38.878353Z", + "iopub.status.busy": "2024-09-26T16:51:38.877791Z", + "iopub.status.idle": "2024-09-26T16:51:41.686939Z", + "shell.execute_reply": "2024-09-26T16:51:41.686386Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:56:11.384692Z", - "iopub.status.busy": "2024-09-26T14:56:11.384341Z", - "iopub.status.idle": "2024-09-26T14:56:43.908330Z", - "shell.execute_reply": "2024-09-26T14:56:43.907746Z" + "iopub.execute_input": "2024-09-26T16:51:41.688815Z", + "iopub.status.busy": "2024-09-26T16:51:41.688478Z", + "iopub.status.idle": "2024-09-26T16:52:13.850567Z", + "shell.execute_reply": "2024-09-26T16:52:13.849973Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b6f8c999233c44e6b60c123e18607ca1", + "model_id": "a82830a9b3954f49ba5ec182f4498604", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:56:43.910328Z", - "iopub.status.busy": "2024-09-26T14:56:43.909998Z", - "iopub.status.idle": "2024-09-26T14:56:59.179852Z", - "shell.execute_reply": "2024-09-26T14:56:59.179195Z" + "iopub.execute_input": "2024-09-26T16:52:13.852523Z", + "iopub.status.busy": "2024-09-26T16:52:13.852050Z", + "iopub.status.idle": "2024-09-26T16:52:29.573789Z", + "shell.execute_reply": "2024-09-26T16:52:29.573253Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:56:59.182094Z", - "iopub.status.busy": "2024-09-26T14:56:59.181790Z", - "iopub.status.idle": "2024-09-26T14:57:03.058054Z", - "shell.execute_reply": "2024-09-26T14:57:03.057557Z" + "iopub.execute_input": "2024-09-26T16:52:29.575856Z", + "iopub.status.busy": "2024-09-26T16:52:29.575654Z", + "iopub.status.idle": "2024-09-26T16:52:33.399699Z", + "shell.execute_reply": "2024-09-26T16:52:33.399171Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:03.059722Z", - "iopub.status.busy": "2024-09-26T14:57:03.059541Z", - "iopub.status.idle": "2024-09-26T14:57:04.551144Z", - "shell.execute_reply": "2024-09-26T14:57:04.550565Z" + "iopub.execute_input": "2024-09-26T16:52:33.401431Z", + "iopub.status.busy": "2024-09-26T16:52:33.401250Z", + "iopub.status.idle": "2024-09-26T16:52:34.923698Z", + "shell.execute_reply": "2024-09-26T16:52:34.923154Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5444a2dc1c4c403ab396248114105df7", + "model_id": "bdb101c173114412ac307c8cf1b57cbf", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:04.553436Z", - "iopub.status.busy": "2024-09-26T14:57:04.552948Z", - "iopub.status.idle": "2024-09-26T14:57:04.584746Z", - "shell.execute_reply": "2024-09-26T14:57:04.584077Z" + "iopub.execute_input": "2024-09-26T16:52:34.925981Z", + "iopub.status.busy": "2024-09-26T16:52:34.925610Z", + "iopub.status.idle": "2024-09-26T16:52:34.959645Z", + "shell.execute_reply": "2024-09-26T16:52:34.958978Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:04.587085Z", - "iopub.status.busy": "2024-09-26T14:57:04.586688Z", - "iopub.status.idle": "2024-09-26T14:57:10.717190Z", - "shell.execute_reply": "2024-09-26T14:57:10.716706Z" + "iopub.execute_input": "2024-09-26T16:52:34.961505Z", + "iopub.status.busy": "2024-09-26T16:52:34.961304Z", + "iopub.status.idle": "2024-09-26T16:52:41.078411Z", + "shell.execute_reply": "2024-09-26T16:52:41.077883Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:10.719179Z", - "iopub.status.busy": "2024-09-26T14:57:10.718830Z", - "iopub.status.idle": "2024-09-26T14:57:10.774462Z", - "shell.execute_reply": "2024-09-26T14:57:10.773892Z" + "iopub.execute_input": "2024-09-26T16:52:41.080563Z", + "iopub.status.busy": "2024-09-26T16:52:41.080164Z", + "iopub.status.idle": "2024-09-26T16:52:41.137242Z", + "shell.execute_reply": "2024-09-26T16:52:41.136739Z" }, "nbsphinx": "hidden" }, @@ -1038,25 +1038,60 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "008823ad1c554e5fa5b1815e6e7eee3a": { - "model_module": "@jupyter-widgets/controls", + "0121c98358854d4f8fe3347d2c02f405": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "038939be2791404a8d8b3535498c5720": { + "013d5146b8714b33a9b49c5875a584bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1109,57 +1144,7 @@ "width": null } }, - "03edd2e8077d415a86a42428227957c1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d5f572bcf9e34ff5b3f799cfc3b2c03c", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_fc418d04bfd44dc999d29a7cfbaf1bf5", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "0d3c194b71ae41699ecaf593bb466ee6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_5d4af35c70b14d9b95542e9fbacf5ee2", - "IPY_MODEL_03edd2e8077d415a86a42428227957c1", - "IPY_MODEL_d408f59a6c4642dbacacf8536dd5bb86" - ], - "layout": "IPY_MODEL_b25023ee46574f0987d4401430bdbe95", - "tabbable": null, - "tooltip": null - } - }, - "11d7b29b91e94ee382b5c3abbb5da356": { + "0b9d95e573da454fa4d21aa6a8707152": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1212,53 +1197,60 @@ "width": null } }, - "15a01925ca5e45e5bb086a7b185ac53c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ffca702bd3444f1690f1f5f85493ca09", - "placeholder": "​", - "style": "IPY_MODEL_008823ad1c554e5fa5b1815e6e7eee3a", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:25<00:00,  1.22it/s]" - } - }, - "1b8ddda746534779bbbb5fd4b8f8df0b": { - "model_module": "@jupyter-widgets/controls", + "0c78a349af244bf3bc6ee925f47cf139": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_11d7b29b91e94ee382b5c3abbb5da356", - "placeholder": "​", - "style": "IPY_MODEL_2cada3550c7444318e24a19d4c5bac92", - "tabbable": null, - "tooltip": null, - "value": " 4997683/4997683 [00:32<00:00, 153968.10it/s]" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "21f930a5e16b44e6896cab16aadf76b0": { + "190e7a9469bc4e24a369b43d468f6485": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1273,33 +1265,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_cbe2b73a42764a2aacaeaee0b9c612b7", + "layout": "IPY_MODEL_b6f143853a084fb0b6ccc250fe93236e", "placeholder": "​", - "style": "IPY_MODEL_d26696e0cc9f4eef935b52e6f5301e41", + "style": "IPY_MODEL_fe0fc4bc25ce44089ff347ab28732dab", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: 100%" + "value": "number of examples processed for estimating thresholds: 100%" } }, - "2cada3550c7444318e24a19d4c5bac92": { + "1970eb3b8ea140959defd0687c894d97": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "2e397451daaa420aac06b58d115ddb89": { + "1c4b81bd340f4a0295988a552f7a58bb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1314,15 +1304,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6c7533cc89b74b90bdcf06dda9d4297f", + "layout": "IPY_MODEL_b1b74b4c0287481381f3cd409688ae72", "placeholder": "​", - "style": "IPY_MODEL_aedea8fe506b40c1933ac0b06c3dc5c7", + "style": "IPY_MODEL_e9fd87514e6b4549b129af0d6e8a05f0", "tabbable": null, "tooltip": null, "value": "100%" } }, - "3539b50ce0e843448d49322ce25b2b2e": { + "2067dcea68e84063ad1f6a5054c3e789": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1375,7 +1365,48 @@ "width": null } }, - "38f90d531db8409db73b7389ee4986c2": { + "20ab2ca6c5f64a7ea18f346ddf743a8b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_47581e85de3940b587f67825e19622c5", + "placeholder": "​", + "style": "IPY_MODEL_97feb3768a2e4dbda7a36b18153e5c0f", + "tabbable": null, + "tooltip": null, + "value": " 4997683/4997683 [00:31<00:00, 158200.14it/s]" + } + }, + "245b1a0dff754ba583337f4d70981efe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "25ab34454075497b970ae157d65bc0f2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1393,7 +1424,7 @@ "text_color": null } }, - "414724ec89444e8ebc1105e3c21216d3": { + "32926b1dbb4b452d965d85b9ab3280bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1446,7 +1477,25 @@ "width": null } }, - "477c45c2e60a4cc7bc955c274f038c75": { + "42ac1b98a0284f3892253b28e716c7ad": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "44eb89ac642f451e9d987ca3647bdbe2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1462,17 +1511,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_496448b9dfc748d7b07ed9a700cc1ab7", + "layout": "IPY_MODEL_0c78a349af244bf3bc6ee925f47cf139", "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_c19fb70e50ef4b3aa215c397be2fa0ed", + "style": "IPY_MODEL_a95f2478462247808e7fbfeaf7f4269c", "tabbable": null, "tooltip": null, "value": 30.0 } }, - "496448b9dfc748d7b07ed9a700cc1ab7": { + "47581e85de3940b587f67825e19622c5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1525,30 +1574,7 @@ "width": null } }, - "4db5293fd3e94b6eb261d17cfdd19337": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_aeabc766c99c4e2b8edffb93d948620a", - "placeholder": "​", - "style": "IPY_MODEL_cbf3bc2871f144bda8f96df51315cc6a", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:01<00:00, 20.31it/s]" - } - }, - "5444a2dc1c4c403ab396248114105df7": { + "502b9ff8baad4af5b5dddfdbca9a8ab9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1563,55 +1589,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e15e2d1b74894e47b98ed243861d83d8", - "IPY_MODEL_db3abba05009401583103fd3bfc35643", - "IPY_MODEL_4db5293fd3e94b6eb261d17cfdd19337" + "IPY_MODEL_190e7a9469bc4e24a369b43d468f6485", + "IPY_MODEL_af36ee45554e45998cbe073cbd448f30", + "IPY_MODEL_fefa0f5d00b74ed48732a140b6e290ba" ], - "layout": "IPY_MODEL_c41365fd01984997be6e7450cfa7d4d5", + "layout": "IPY_MODEL_013d5146b8714b33a9b49c5875a584bf", "tabbable": null, "tooltip": null } }, - "5ab8498d427444c6b4d07bf8d5bc6157": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "5d4af35c70b14d9b95542e9fbacf5ee2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a6ce96bd4a1f4a83b1164ac6cbe3d02f", - "placeholder": "​", - "style": "IPY_MODEL_38f90d531db8409db73b7389ee4986c2", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: 100%" - } - }, - "60825090590c4420817f531a12ba0cb9": { + "53b638ae41f3478f99d13a3ebb42169f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1664,7 +1651,7 @@ "width": null } }, - "6c7533cc89b74b90bdcf06dda9d4297f": { + "60f7f25133834746944894473307e0e7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1717,7 +1704,7 @@ "width": null } }, - "6fc3fa5fef38489287ed8d9f7c6e1c3e": { + "6149d5ce9db84ce78a209d8113fbcdeb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1770,25 +1757,7 @@ "width": null } }, - "7211d82a11904799ba5182ef4f7e1762": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a6ce96bd4a1f4a83b1164ac6cbe3d02f": { + "69d0fc942d0d4e6a97445f38e4d57ea2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1841,113 +1810,67 @@ "width": null } }, - "aad869b6d41d459097999efed9f5aabb": { - "model_module": "@jupyter-widgets/base", + "6de0625b60cd4c02b120f1de805e03c9": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "aeabc766c99c4e2b8edffb93d948620a": { - "model_module": "@jupyter-widgets/base", + "7a004d25557e444496cc520252ba637f": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a701b3a09a964c25b447425ed13b3e2c", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_861c793818bd43bca46ff4897a3a44fa", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "861c793818bd43bca46ff4897a3a44fa": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "aedea8fe506b40c1933ac0b06c3dc5c7": { + "97feb3768a2e4dbda7a36b18153e5c0f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1965,7 +1888,48 @@ "text_color": null } }, - "b25023ee46574f0987d4401430bdbe95": { + "9939290b74ec4ebe886ed5834f42faf6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a24f9eab3fb94dc2990c0f506f2f987a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0b9d95e573da454fa4d21aa6a8707152", + "placeholder": "​", + "style": "IPY_MODEL_9939290b74ec4ebe886ed5834f42faf6", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:01<00:00, 19.48it/s]" + } + }, + "a701b3a09a964c25b447425ed13b3e2c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2018,7 +1982,7 @@ "width": null } }, - "b6f8c999233c44e6b60c123e18607ca1": { + "a82830a9b3954f49ba5ec182f4498604": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2033,16 +1997,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2e397451daaa420aac06b58d115ddb89", - "IPY_MODEL_c126cab8ab9c4826849b4c390465afaf", - "IPY_MODEL_1b8ddda746534779bbbb5fd4b8f8df0b" + "IPY_MODEL_1c4b81bd340f4a0295988a552f7a58bb", + "IPY_MODEL_e3dce47f83364b91ab69a6c0d0ada3a5", + "IPY_MODEL_20ab2ca6c5f64a7ea18f346ddf743a8b" ], - "layout": "IPY_MODEL_60825090590c4420817f531a12ba0cb9", + "layout": "IPY_MODEL_32926b1dbb4b452d965d85b9ab3280bb", "tabbable": null, "tooltip": null } }, - "c0780c5558e44bdf9cd38943fbc6879f": { + "a95f2478462247808e7fbfeaf7f4269c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2058,7 +2022,30 @@ "description_width": "" } }, - "c126cab8ab9c4826849b4c390465afaf": { + "abb25c23e4fe4deb9022759c984b7e1a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e417d1d7ab4b479ba1a24db21005ee18", + "placeholder": "​", + "style": "IPY_MODEL_25ab34454075497b970ae157d65bc0f2", + "tabbable": null, + "tooltip": null, + "value": "images processed using softmin: 100%" + } + }, + "af36ee45554e45998cbe073cbd448f30": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2074,33 +2061,93 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_3539b50ce0e843448d49322ce25b2b2e", - "max": 4997683.0, + "layout": "IPY_MODEL_2067dcea68e84063ad1f6a5054c3e789", + "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_5ab8498d427444c6b4d07bf8d5bc6157", + "style": "IPY_MODEL_fda6bf3d4a3349d082a2341f068a5e89", "tabbable": null, "tooltip": null, - "value": 4997683.0 + "value": 30.0 + } + }, + "b1b74b4c0287481381f3cd409688ae72": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "c19fb70e50ef4b3aa215c397be2fa0ed": { + "b1ecb6440eeb484cab4c6d3a1846d880": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6149d5ce9db84ce78a209d8113fbcdeb", + "placeholder": "​", + "style": "IPY_MODEL_6de0625b60cd4c02b120f1de805e03c9", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for checking labels: 100%" } }, - "c41365fd01984997be6e7450cfa7d4d5": { + "b6f143853a084fb0b6ccc250fe93236e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2153,7 +2200,7 @@ "width": null } }, - "cbe2b73a42764a2aacaeaee0b9c612b7": { + "b7f3da04e1de47889cbec5d9b381d330": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2206,66 +2253,81 @@ "width": null } }, - "cbf3bc2871f144bda8f96df51315cc6a": { + "bb7371c71aa847f2b4d79a007cc0db13": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b1ecb6440eeb484cab4c6d3a1846d880", + "IPY_MODEL_7a004d25557e444496cc520252ba637f", + "IPY_MODEL_f411da9220e6477ebc385b8c9da96582" + ], + "layout": "IPY_MODEL_b7f3da04e1de47889cbec5d9b381d330", + "tabbable": null, + "tooltip": null } }, - "d26696e0cc9f4eef935b52e6f5301e41": { + "bdb101c173114412ac307c8cf1b57cbf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_abb25c23e4fe4deb9022759c984b7e1a", + "IPY_MODEL_44eb89ac642f451e9d987ca3647bdbe2", + "IPY_MODEL_a24f9eab3fb94dc2990c0f506f2f987a" + ], + "layout": "IPY_MODEL_69d0fc942d0d4e6a97445f38e4d57ea2", + "tabbable": null, + "tooltip": null } }, - "d408f59a6c4642dbacacf8536dd5bb86": { + "e3dce47f83364b91ab69a6c0d0ada3a5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_aad869b6d41d459097999efed9f5aabb", - "placeholder": "​", - "style": "IPY_MODEL_7211d82a11904799ba5182ef4f7e1762", + "layout": "IPY_MODEL_60f7f25133834746944894473307e0e7", + "max": 4997683.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1970eb3b8ea140959defd0687c894d97", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:00<00:00, 761.55it/s]" + "value": 4997683.0 } }, - "d5f572bcf9e34ff5b3f799cfc3b2c03c": { + "e417d1d7ab4b479ba1a24db21005ee18": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2318,33 +2380,25 @@ "width": null } }, - "db3abba05009401583103fd3bfc35643": { + "e9fd87514e6b4549b129af0d6e8a05f0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_414724ec89444e8ebc1105e3c21216d3", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c0780c5558e44bdf9cd38943fbc6879f", - "tabbable": null, - "tooltip": null, - "value": 30.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "e15e2d1b74894e47b98ed243861d83d8": { + "f411da9220e6477ebc385b8c9da96582": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2359,123 +2413,69 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_038939be2791404a8d8b3535498c5720", + "layout": "IPY_MODEL_0121c98358854d4f8fe3347d2c02f405", "placeholder": "​", - "style": "IPY_MODEL_e7222a4d37404d41a68f2bf782915ef2", + "style": "IPY_MODEL_245b1a0dff754ba583337f4d70981efe", "tabbable": null, "tooltip": null, - "value": "images processed using softmin: 100%" + "value": " 30/30 [00:25<00:00,  1.17it/s]" } }, - "e7222a4d37404d41a68f2bf782915ef2": { + "fda6bf3d4a3349d082a2341f068a5e89": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f246aefc67174f658fc6990471fd838b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_21f930a5e16b44e6896cab16aadf76b0", - "IPY_MODEL_477c45c2e60a4cc7bc955c274f038c75", - "IPY_MODEL_15a01925ca5e45e5bb086a7b185ac53c" - ], - "layout": "IPY_MODEL_6fc3fa5fef38489287ed8d9f7c6e1c3e", - "tabbable": null, - "tooltip": null + "bar_color": null, + "description_width": "" } }, - "fc418d04bfd44dc999d29a7cfbaf1bf5": { + "fe0fc4bc25ce44089ff347ab28732dab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ffca702bd3444f1690f1f5f85493ca09": { - "model_module": "@jupyter-widgets/base", + "fefa0f5d00b74ed48732a140b6e290ba": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_53b638ae41f3478f99d13a3ebb42169f", + "placeholder": "​", + "style": "IPY_MODEL_42ac1b98a0284f3892253b28e716c7ad", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 822.88it/s]" } } }, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index 413046fc5..559a1a521 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -714,16 +714,16 @@

1. Install required dependencies and download data

diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index 9d0e0764f..9eee41a96 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:13.331707Z", - "iopub.status.busy": "2024-09-26T14:57:13.331541Z", - "iopub.status.idle": "2024-09-26T14:57:15.936866Z", - "shell.execute_reply": "2024-09-26T14:57:15.936192Z" + "iopub.execute_input": "2024-09-26T16:52:43.644067Z", + "iopub.status.busy": "2024-09-26T16:52:43.643568Z", + "iopub.status.idle": "2024-09-26T16:52:44.942651Z", + "shell.execute_reply": "2024-09-26T16:52:44.941999Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-26 14:57:13-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-09-26 16:52:43-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.243, 2400:52e0:1a00::940:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.243|:443... connected.\r\n", + "185.93.1.251, 2400:52e0:1a00::1207:2\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.251|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -116,9 +123,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.41MB/s in 0.2s \r\n", "\r\n", - "2024-09-26 14:57:13 (7.67 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-09-26 16:52:44 (5.41 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -127,13 +134,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Archive: conll2003.zip\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Archive: conll2003.zip\r\n", " inflating: data/metadata \r\n", " inflating: data/test.txt \r\n", " inflating: data/train.txt \r\n", @@ -144,16 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-26 14:57:14-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.27.119, 52.217.207.97, 52.217.171.81, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.27.119|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n", + "--2024-09-26 16:52:44-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.81.60, 3.5.29.169, 16.182.105.153, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.81.60|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,57 +168,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 2%[ ] 482.32K 2.17MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 7%[> ] 1.23M 2.84MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 14%[=> ] 2.42M 3.72MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 26%[====> ] 4.26M 4.90MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 43%[=======> ] 7.12M 6.54MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 71%[=============> ] 11.56M 8.85MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 100%[===================>] 16.26M 11.2MB/s in 1.5s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 82.5MB/s in 0.2s \r\n", "\r\n", - "2024-09-26 14:57:15 (11.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-09-26 16:52:44 (82.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -241,10 +187,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:15.939149Z", - "iopub.status.busy": "2024-09-26T14:57:15.938782Z", - "iopub.status.idle": "2024-09-26T14:57:17.187528Z", - "shell.execute_reply": "2024-09-26T14:57:17.186884Z" + "iopub.execute_input": "2024-09-26T16:52:44.944544Z", + "iopub.status.busy": "2024-09-26T16:52:44.944353Z", + "iopub.status.idle": "2024-09-26T16:52:46.315902Z", + "shell.execute_reply": "2024-09-26T16:52:46.315340Z" }, "nbsphinx": "hidden" }, @@ -255,7 +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@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@fcb653811c1ddd4afd960ba2710e443cf6edb384\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -281,10 +227,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:17.190094Z", - "iopub.status.busy": "2024-09-26T14:57:17.189576Z", - "iopub.status.idle": "2024-09-26T14:57:17.193093Z", - "shell.execute_reply": "2024-09-26T14:57:17.192623Z" + "iopub.execute_input": "2024-09-26T16:52:46.318115Z", + "iopub.status.busy": "2024-09-26T16:52:46.317709Z", + "iopub.status.idle": "2024-09-26T16:52:46.321236Z", + "shell.execute_reply": "2024-09-26T16:52:46.320645Z" } }, "outputs": [], @@ -334,10 +280,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:17.194944Z", - "iopub.status.busy": "2024-09-26T14:57:17.194599Z", - "iopub.status.idle": "2024-09-26T14:57:17.197554Z", - "shell.execute_reply": "2024-09-26T14:57:17.197086Z" + "iopub.execute_input": "2024-09-26T16:52:46.323141Z", + "iopub.status.busy": "2024-09-26T16:52:46.322796Z", + "iopub.status.idle": "2024-09-26T16:52:46.325840Z", + "shell.execute_reply": "2024-09-26T16:52:46.325375Z" }, "nbsphinx": "hidden" }, @@ -355,10 +301,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:17.199051Z", - "iopub.status.busy": "2024-09-26T14:57:17.198872Z", - "iopub.status.idle": "2024-09-26T14:57:26.446906Z", - "shell.execute_reply": "2024-09-26T14:57:26.446343Z" + "iopub.execute_input": "2024-09-26T16:52:46.327592Z", + "iopub.status.busy": "2024-09-26T16:52:46.327316Z", + "iopub.status.idle": "2024-09-26T16:52:55.436229Z", + "shell.execute_reply": "2024-09-26T16:52:55.435663Z" } }, "outputs": [], @@ -432,10 +378,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:26.449170Z", - "iopub.status.busy": "2024-09-26T14:57:26.448693Z", - "iopub.status.idle": "2024-09-26T14:57:26.454297Z", - "shell.execute_reply": "2024-09-26T14:57:26.453763Z" + "iopub.execute_input": "2024-09-26T16:52:55.438417Z", + "iopub.status.busy": "2024-09-26T16:52:55.438126Z", + "iopub.status.idle": "2024-09-26T16:52:55.443647Z", + "shell.execute_reply": "2024-09-26T16:52:55.443211Z" }, "nbsphinx": "hidden" }, @@ -475,10 +421,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:26.456078Z", - "iopub.status.busy": "2024-09-26T14:57:26.455769Z", - "iopub.status.idle": "2024-09-26T14:57:26.817319Z", - "shell.execute_reply": "2024-09-26T14:57:26.816634Z" + "iopub.execute_input": "2024-09-26T16:52:55.445403Z", + "iopub.status.busy": "2024-09-26T16:52:55.445074Z", + "iopub.status.idle": "2024-09-26T16:52:55.783623Z", + "shell.execute_reply": "2024-09-26T16:52:55.783066Z" } }, "outputs": [], @@ -515,10 +461,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:26.819374Z", - "iopub.status.busy": "2024-09-26T14:57:26.819176Z", - "iopub.status.idle": "2024-09-26T14:57:26.823791Z", - "shell.execute_reply": "2024-09-26T14:57:26.823316Z" + "iopub.execute_input": "2024-09-26T16:52:55.785701Z", + "iopub.status.busy": "2024-09-26T16:52:55.785338Z", + "iopub.status.idle": "2024-09-26T16:52:55.789881Z", + "shell.execute_reply": "2024-09-26T16:52:55.789413Z" } }, "outputs": [ @@ -590,10 +536,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:26.825588Z", - "iopub.status.busy": "2024-09-26T14:57:26.825150Z", - "iopub.status.idle": "2024-09-26T14:57:29.558927Z", - "shell.execute_reply": "2024-09-26T14:57:29.558069Z" + "iopub.execute_input": "2024-09-26T16:52:55.791534Z", + "iopub.status.busy": "2024-09-26T16:52:55.791208Z", + "iopub.status.idle": "2024-09-26T16:52:58.424549Z", + "shell.execute_reply": "2024-09-26T16:52:58.423726Z" } }, "outputs": [], @@ -615,10 +561,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:29.561613Z", - "iopub.status.busy": "2024-09-26T14:57:29.560961Z", - "iopub.status.idle": "2024-09-26T14:57:29.565280Z", - "shell.execute_reply": "2024-09-26T14:57:29.564687Z" + "iopub.execute_input": "2024-09-26T16:52:58.427547Z", + "iopub.status.busy": "2024-09-26T16:52:58.426730Z", + "iopub.status.idle": "2024-09-26T16:52:58.430710Z", + "shell.execute_reply": "2024-09-26T16:52:58.430197Z" } }, "outputs": [ @@ -654,10 +600,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:29.567105Z", - "iopub.status.busy": "2024-09-26T14:57:29.566772Z", - "iopub.status.idle": "2024-09-26T14:57:29.572163Z", - "shell.execute_reply": "2024-09-26T14:57:29.571688Z" + "iopub.execute_input": "2024-09-26T16:52:58.432406Z", + "iopub.status.busy": "2024-09-26T16:52:58.432073Z", + "iopub.status.idle": "2024-09-26T16:52:58.437486Z", + "shell.execute_reply": "2024-09-26T16:52:58.437029Z" } }, "outputs": [ @@ -835,10 +781,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:29.573957Z", - "iopub.status.busy": "2024-09-26T14:57:29.573552Z", - "iopub.status.idle": "2024-09-26T14:57:29.601023Z", - "shell.execute_reply": "2024-09-26T14:57:29.600416Z" + "iopub.execute_input": "2024-09-26T16:52:58.439340Z", + "iopub.status.busy": "2024-09-26T16:52:58.439008Z", + "iopub.status.idle": "2024-09-26T16:52:58.465679Z", + "shell.execute_reply": "2024-09-26T16:52:58.465240Z" } }, "outputs": [ @@ -940,10 +886,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:29.602952Z", - "iopub.status.busy": "2024-09-26T14:57:29.602606Z", - "iopub.status.idle": "2024-09-26T14:57:29.607644Z", - "shell.execute_reply": "2024-09-26T14:57:29.607163Z" + "iopub.execute_input": "2024-09-26T16:52:58.467381Z", + "iopub.status.busy": "2024-09-26T16:52:58.467051Z", + "iopub.status.idle": "2024-09-26T16:52:58.470976Z", + "shell.execute_reply": "2024-09-26T16:52:58.470534Z" } }, "outputs": [ @@ -1017,10 +963,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:29.609321Z", - "iopub.status.busy": "2024-09-26T14:57:29.608970Z", - "iopub.status.idle": "2024-09-26T14:57:31.052597Z", - "shell.execute_reply": "2024-09-26T14:57:31.052050Z" + "iopub.execute_input": "2024-09-26T16:52:58.472587Z", + "iopub.status.busy": "2024-09-26T16:52:58.472255Z", + "iopub.status.idle": "2024-09-26T16:52:59.920706Z", + "shell.execute_reply": "2024-09-26T16:52:59.920179Z" } }, "outputs": [ @@ -1192,10 +1138,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-09-26T14:57:31.054589Z", - "iopub.status.busy": "2024-09-26T14:57:31.054180Z", - "iopub.status.idle": "2024-09-26T14:57:31.058507Z", - "shell.execute_reply": "2024-09-26T14:57:31.057947Z" + "iopub.execute_input": "2024-09-26T16:52:59.922589Z", + "iopub.status.busy": "2024-09-26T16:52:59.922209Z", + "iopub.status.idle": "2024-09-26T16:52:59.926355Z", + "shell.execute_reply": "2024-09-26T16:52:59.925893Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index 4f9ffb8d0..95d644e58 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { - version_number: "v2.6.6", - commit_hash: "82901442916cd9aa0a85cf88d058b89f5506a1fb", + version_number: "v2.7.0", + commit_hash: "fcb653811c1ddd4afd960ba2710e443cf6edb384", }; \ No newline at end of file