Convert dataset to Parquet

README.md

@@ -23,36 +23,46 @@ task_ids:
 - text-scoring
 paperswithcode_id: glue
 pretty_name: GLUE (General Language Understanding Evaluation benchmark)
+config_names:
+- ax
+- cola
+- mnli
+- mnli_matched
+- mnli_mismatched
+- mrpc
+- qnli
+- qqp
+- rte
+- sst2
+- stsb
+- wnli
 tags:
 - qa-nli
 - coreference-nli
 - paraphrase-identification
 dataset_info:
-- config_name: cola
+- config_name: ax
   features:
-  - name: sentence
+  - name: premise
+    dtype: string
+  - name: hypothesis
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
-          '0': unacceptable
-          '1': acceptable
+          '0': entailment
+          '1': neutral
+          '2': contradiction
   - name: idx
     dtype: int32
   splits:
   - name: test
-    num_bytes: 61049
-    num_examples: 1063
-  - name: train
-    num_bytes: 489149
-    num_examples: 8551
-  - name: validation
-    num_bytes: 60850
-    num_examples: 1043
-  download_size: 376971
-  dataset_size: 611048
-- config_name: sst2
+    num_bytes: 237694
+    num_examples: 1104
+  download_size: 80767
+  dataset_size: 237694
+- config_name: cola
   features:
   - name: sentence
     dtype: string
@@ -60,97 +70,56 @@ dataset_info:
     dtype:
       class_label:
         names:
-          '0': negative
-          '1': positive
+          '0': unacceptable
+          '1': acceptable
   - name: idx
     dtype: int32
   splits:
-  - name: test
-    num_bytes: 217556
-    num_examples: 1821
   - name: train
-    num_bytes: 4715283
-    num_examples: 67349
+    num_bytes: 484869
+    num_examples: 8551
   - name: validation
-    num_bytes: 106692
-    num_examples: 872
-  download_size: 7439277
-  dataset_size: 5039531
-- config_name: mrpc
-  features:
-  - name: sentence1
-    dtype: string
-  - name: sentence2
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': not_equivalent
-          '1': equivalent
-  - name: idx
-    dtype: int32
-  splits:
+    num_bytes: 60322
+    num_examples: 1043
   - name: test
-    num_bytes: 443498
-    num_examples: 1725
-  - name: train
-    num_bytes: 946146
-    num_examples: 3668
-  - name: validation
-    num_bytes: 106142
-    num_examples: 408
-  download_size: 1494541
-  dataset_size: 1495786
-- config_name: qqp
+    num_bytes: 60513
+    num_examples: 1063
+  download_size: 326394
+  dataset_size: 605704
+- config_name: mnli
   features:
-  - name: question1
+  - name: premise
     dtype: string
-  - name: question2
+  - name: hypothesis
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
-          '0': not_duplicate
-          '1': duplicate
-  - name: idx
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 50901116
-    num_examples: 363846
-  - name: validation
-    num_bytes: 5653794
-    num_examples: 40430
-  - name: test
-    num_bytes: 55171431
-    num_examples: 390965
-  download_size: 41696084
-  dataset_size: 111726341
-- config_name: stsb
-  features:
-  - name: sentence1
-    dtype: string
-  - name: sentence2
-    dtype: string
-  - name: label
-    dtype: float32
+          '0': entailment
+          '1': neutral
+          '2': contradiction
   - name: idx
     dtype: int32
   splits:
-  - name: test
-    num_bytes: 170847
-    num_examples: 1379
   - name: train
-    num_bytes: 758394
-    num_examples: 5749
-  - name: validation
-    num_bytes: 217012
-    num_examples: 1500
-  download_size: 802872
-  dataset_size: 1146253
-- config_name: mnli
+    num_bytes: 74619646
+    num_examples: 392702
+  - name: validation_matched
+    num_bytes: 1833783
+    num_examples: 9815
+  - name: validation_mismatched
+    num_bytes: 1949231
+    num_examples: 9832
+  - name: test_matched
+    num_bytes: 1848654
+    num_examples: 9796
+  - name: test_mismatched
+    num_bytes: 1950703
+    num_examples: 9847
+  download_size: 57168425
+  dataset_size: 82202017
+- config_name: mnli_matched
   features:
   - name: premise
     dtype: string
@@ -166,23 +135,14 @@ dataset_info:
   - name: idx
     dtype: int32
   splits:
-  - name: test_matched
-    num_bytes: 1854787
-    num_examples: 9796
-  - name: test_mismatched
-    num_bytes: 1956866
-    num_examples: 9847
-  - name: train
-    num_bytes: 74865118
-    num_examples: 392702
-  - name: validation_matched
-    num_bytes: 1839926
+  - name: validation
+    num_bytes: 1833783
     num_examples: 9815
-  - name: validation_mismatched
-    num_bytes: 1955384
-    num_examples: 9832
-  download_size: 312783507
-  dataset_size: 82472081
+  - name: test
+    num_bytes: 1848654
+    num_examples: 9796
+  download_size: 2435055
+  dataset_size: 3682437
 - config_name: mnli_mismatched
   features:
   - name: premise
@@ -199,38 +159,40 @@ dataset_info:
   - name: idx
     dtype: int32
   splits:
-  - name: test
-    num_bytes: 1956866
-    num_examples: 9847
   - name: validation
-    num_bytes: 1955384
+    num_bytes: 1949231
     num_examples: 9832
-  download_size: 312783507
-  dataset_size: 3912250
-- config_name: mnli_matched
+  - name: test
+    num_bytes: 1950703
+    num_examples: 9847
+  download_size: 2509009
+  dataset_size: 3899934
+- config_name: mrpc
   features:
-  - name: premise
+  - name: sentence1
     dtype: string
-  - name: hypothesis
+  - name: sentence2
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
-          '0': entailment
-          '1': neutral
-          '2': contradiction
+          '0': not_equivalent
+          '1': equivalent
   - name: idx
     dtype: int32
   splits:
-  - name: test
-    num_bytes: 1854787
-    num_examples: 9796
+  - name: train
+    num_bytes: 943843
+    num_examples: 3668
   - name: validation
-    num_bytes: 1839926
-    num_examples: 9815
-  download_size: 312783507
-  dataset_size: 3694713
+    num_bytes: 105879
+    num_examples: 408
+  - name: test
+    num_bytes: 442410
+    num_examples: 1725
+  download_size: 1033400
+  dataset_size: 1492132
 - config_name: qnli
   features:
   - name: question
@@ -246,17 +208,43 @@ dataset_info:
   - name: idx
     dtype: int32
   splits:
-  - name: test
-    num_bytes: 1376516
-    num_examples: 5463
   - name: train
-    num_bytes: 25677924
+    num_bytes: 25612443
     num_examples: 104743
   - name: validation
-    num_bytes: 1371727
+    num_bytes: 1368304
+    num_examples: 5463
+  - name: test
+    num_bytes: 1373093
     num_examples: 5463
-  download_size: 10627589
-  dataset_size: 28426167
+  download_size: 19278324
+  dataset_size: 28353840
+- config_name: qqp
+  features:
+  - name: question1
+    dtype: string
+  - name: question2
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': not_duplicate
+          '1': duplicate
+  - name: idx
+    dtype: int32
+  splits:
+  - name: train
+    num_bytes: 50900820
+    num_examples: 363846
+  - name: validation
+    num_bytes: 5653754
+    num_examples: 40430
+  - name: test
+    num_bytes: 55171111
+    num_examples: 390965
+  download_size: 73982265
+  dataset_size: 111725685
 - config_name: rte
   features:
   - name: sentence1
@@ -272,64 +260,182 @@ dataset_info:
   - name: idx
     dtype: int32
   splits:
-  - name: test
-    num_bytes: 975936
-    num_examples: 3000
   - name: train
-    num_bytes: 848888
+    num_bytes: 847320
     num_examples: 2490
   - name: validation
-    num_bytes: 90911
+    num_bytes: 90728
     num_examples: 277
-  download_size: 697150
-  dataset_size: 1915735
-- config_name: wnli
+  - name: test
+    num_bytes: 974053
+    num_examples: 3000
+  download_size: 1274409
+  dataset_size: 1912101
+- config_name: sst2
   features:
-  - name: sentence1
-    dtype: string
-  - name: sentence2
+  - name: sentence
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
-          '0': not_entailment
-          '1': entailment
+          '0': negative
+          '1': positive
   - name: idx
     dtype: int32
   splits:
+  - name: train
+    num_bytes: 4681603
+    num_examples: 67349
+  - name: validation
+    num_bytes: 106252
+    num_examples: 872
   - name: test
-    num_bytes: 37992
-    num_examples: 146
+    num_bytes: 216640
+    num_examples: 1821
+  download_size: 3331080
+  dataset_size: 5004495
+- config_name: stsb
+  features:
+  - name: sentence1
+    dtype: string
+  - name: sentence2
+    dtype: string
+  - name: label
+    dtype: float32
+  - name: idx
+    dtype: int32
+  splits:
   - name: train
-    num_bytes: 107517
-    num_examples: 635
+    num_bytes: 754791
+    num_examples: 5749
   - name: validation
-    num_bytes: 12215
-    num_examples: 71
-  download_size: 28999
-  dataset_size: 157724
-- config_name: ax
+    num_bytes: 216064
+    num_examples: 1500
+  - name: test
+    num_bytes: 169974
+    num_examples: 1379
+  download_size: 766983
+  dataset_size: 1140829
+- config_name: wnli
   features:
-  - name: premise
+  - name: sentence1
     dtype: string
-  - name: hypothesis
+  - name: sentence2
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
-          '0': entailment
-          '1': neutral
-          '2': contradiction
+          '0': not_entailment
+          '1': entailment
   - name: idx
     dtype: int32
   splits:
+  - name: train
+    num_bytes: 107109
+    num_examples: 635
+  - name: validation
+    num_bytes: 12162
+    num_examples: 71
   - name: test
-    num_bytes: 238392
-    num_examples: 1104
-  download_size: 222257
-  dataset_size: 238392
+    num_bytes: 37889
+    num_examples: 146
+  download_size: 63522
+  dataset_size: 157160
+configs:
+- config_name: ax
+  data_files:
+  - split: test
+    path: ax/test-*
+- config_name: cola
+  data_files:
+  - split: train
+    path: cola/train-*
+  - split: validation
+    path: cola/validation-*
+  - split: test
+    path: cola/test-*
+- config_name: mnli
+  data_files:
+  - split: train
+    path: mnli/train-*
+  - split: validation_matched
+    path: mnli/validation_matched-*
+  - split: validation_mismatched
+    path: mnli/validation_mismatched-*
+  - split: test_matched
+    path: mnli/test_matched-*
+  - split: test_mismatched
+    path: mnli/test_mismatched-*
+- config_name: mnli_matched
+  data_files:
+  - split: validation
+    path: mnli_matched/validation-*
+  - split: test
+    path: mnli_matched/test-*
+- config_name: mnli_mismatched
+  data_files:
+  - split: validation
+    path: mnli_mismatched/validation-*
+  - split: test
+    path: mnli_mismatched/test-*
+- config_name: mrpc
+  data_files:
+  - split: train
+    path: mrpc/train-*
+  - split: validation
+    path: mrpc/validation-*
+  - split: test
+    path: mrpc/test-*
+- config_name: qnli
+  data_files:
+  - split: train
+    path: qnli/train-*
+  - split: validation
+    path: qnli/validation-*
+  - split: test
+    path: qnli/test-*
+- config_name: qqp
+  data_files:
+  - split: train
+    path: qqp/train-*
+  - split: validation
+    path: qqp/validation-*
+  - split: test
+    path: qqp/test-*
+- config_name: rte
+  data_files:
+  - split: train
+    path: rte/train-*
+  - split: validation
+    path: rte/validation-*
+  - split: test
+    path: rte/test-*
+- config_name: sst2
+  data_files:
+  - split: train
+    path: sst2/train-*
+  - split: validation
+    path: sst2/validation-*
+  - split: test
+    path: sst2/test-*
+- config_name: stsb
+  data_files:
+  - split: train
+    path: stsb/train-*
+  - split: validation
+    path: stsb/validation-*
+  - split: test
+    path: stsb/test-*
+- config_name: wnli
+  data_files:
+  - split: train
+    path: wnli/train-*
+  - split: validation
+    path: wnli/validation-*
+  - split: test
+    path: wnli/test-*
 train-eval-index:
 - config: cola
   task: text-classification
@@ -439,19 +545,6 @@ train-eval-index:
     sentence1: text1
     sentence2: text2
     label: target
-config_names:
-- ax
-- cola
-- mnli
-- mnli_matched
-- mnli_mismatched
-- mrpc
-- qnli
-- qqp
-- rte
-- sst2
-- stsb
-- wnli
 ---
 
 # Dataset Card for GLUE

ax/test-00000-of-00001.parquet

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+oid sha256:a07b802fe2d4968a1f7ccce9406826dc77e0d1dc53fea9491664bd8ebba8571a
+size 80767

cola/test-00000-of-00001.parquet

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+oid sha256:3c4d526b6f49f432621de43569f9ecf6af41f639baaf4a9d821b95d745def61d
+size 37719

cola/train-00000-of-00001.parquet

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+oid sha256:2e7538afa2000e63f5343f16a758d75c452661a384208399d2035cd2fce45c33
+size 251124

cola/validation-00000-of-00001.parquet

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+oid sha256:c14b7219a7d9f9fe3dd291fd000f6623ee413805eb108c9c49578ed50873e4ba
+size 37551

dataset_infos.json

@@ -1 +0,0 @@
-{"cola": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "@article{warstadt2018neural,\n  title={Neural Network Acceptability Judgments},\n  author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1805.12471},\n  year={2018}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. Please see the source to see\nthe correct citation for each contained dataset.", "homepage": "https://nyu-mll.github.io/CoLA/", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["unacceptable", "acceptable"], "names_file": null, "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "cola", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 61049, "num_examples": 1063, "dataset_name": "glue"}, "train": {"name": "train", "num_bytes": 489149, "num_examples": 8551, "dataset_name": "glue"}, "validation": {"name": "validation", "num_bytes": 60850, "num_examples": 1043, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/CoLA.zip": {"num_bytes": 376971, "checksum": "f212fcd832b8f7b435fb991f101abf89f96b933ab400603bf198960dfc32cbff"}}, "download_size": 376971, "post_processing_size": null, "dataset_size": 611048, "size_in_bytes": 988019}, "sst2": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "@inproceedings{socher2013recursive,\n  title={Recursive deep models for semantic compositionality over a sentiment treebank},\n  author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},\n  booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},\n  pages={1631--1642},\n  year={2013}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. Please see the source to see\nthe correct citation for each contained dataset.", "homepage": "https://nlp.stanford.edu/sentiment/index.html", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["negative", "positive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "sst2", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 217556, "num_examples": 1821, "dataset_name": "glue"}, "train": {"name": "train", "num_bytes": 4715283, "num_examples": 67349, "dataset_name": "glue"}, "validation": {"name": "validation", "num_bytes": 106692, "num_examples": 872, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/SST-2.zip": {"num_bytes": 7439277, "checksum": "d67e16fb55739c1b32cdce9877596db1c127dc322d93c082281f64057c16deaa"}}, "download_size": 7439277, "post_processing_size": null, "dataset_size": 5039531, "size_in_bytes": 12478808}, "mrpc": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "@inproceedings{dolan2005automatically,\n  title={Automatically constructing a corpus of sentential paraphrases},\n  author={Dolan, William B and Brockett, Chris},\n  booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n  year={2005}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. Please see the source to see\nthe correct citation for each contained dataset.", "homepage": "https://www.microsoft.com/en-us/download/details.aspx?id=52398", "license": "", "features": {"sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["not_equivalent", "equivalent"], "names_file": null, "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "mrpc", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 443498, "num_examples": 1725, "dataset_name": "glue"}, "train": {"name": "train", "num_bytes": 946146, "num_examples": 3668, "dataset_name": "glue"}, "validation": {"name": "validation", "num_bytes": 106142, "num_examples": 408, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv": {"num_bytes": 6222, "checksum": "971d7767d81b997fd9060ade0ec23c4fc31cbb226a55d1bd4a1bac474eb81dc7"}, "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt": {"num_bytes": 1047044, "checksum": "60a9b09084528f0673eedee2b69cb941920f0b8cd0eeccefc464a98768457f89"}, "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt": {"num_bytes": 441275, "checksum": "a04e271090879aaba6423d65b94950c089298587d9c084bf9cd7439bd785f784"}}, "download_size": 1494541, "post_processing_size": null, "dataset_size": 1495786, "size_in_bytes": 2990327}, "qqp": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "@online{WinNT,\n  author = {Iyer, Shankar and Dandekar, Nikhil and Csernai, Kornel},\n  title = {First Quora Dataset Release: Question Pairs},\n  year = {2017},\n  url = {https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs},\n  urldate = {2019-04-03}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n", "homepage": "https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs", "license": "", "features": {"question1": {"dtype": "string", "id": null, "_type": "Value"}, "question2": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["not_duplicate", "duplicate"], "names_file": null, "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "qqp", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 50901116, "num_examples": 363846, "dataset_name": "glue"}, "validation": {"name": "validation", "num_bytes": 5653794, "num_examples": 40430, "dataset_name": "glue"}, "test": {"name": "test", "num_bytes": 55171431, "num_examples": 390965, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/QQP-clean.zip": {"num_bytes": 41696084, "checksum": "40e7c862c04eb26ee04b67fd900e76c45c6ba8e6d8fab4f8f1f8072a1a3fbae0"}}, "download_size": 41696084, "post_processing_size": null, "dataset_size": 111726341, "size_in_bytes": 153422425}, "stsb": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "@article{cer2017semeval,\n  title={Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation},\n  author={Cer, Daniel and Diab, Mona and Agirre, Eneko and Lopez-Gazpio, Inigo and Specia, Lucia},\n  journal={arXiv preprint arXiv:1708.00055},\n  year={2017}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. Please see the source to see\nthe correct citation for each contained dataset.", "homepage": "http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark", "license": "", "features": {"sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "float32", "id": null, "_type": "Value"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "stsb", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 170847, "num_examples": 1379, "dataset_name": "glue"}, "train": {"name": "train", "num_bytes": 758394, "num_examples": 5749, "dataset_name": "glue"}, "validation": {"name": "validation", "num_bytes": 217012, "num_examples": 1500, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/STS-B.zip": {"num_bytes": 802872, "checksum": "e60a6393de5a8b5b9bac5020a1554b54e3691f9d600b775bd131e613ac179c85"}}, "download_size": 802872, "post_processing_size": null, "dataset_size": 1146253, "size_in_bytes": 1949125}, "mnli": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "@InProceedings{N18-1101,\n  author = \"Williams, Adina\n            and Nangia, Nikita\n            and Bowman, Samuel\",\n  title = \"A Broad-Coverage Challenge Corpus for\n           Sentence Understanding through Inference\",\n  booktitle = \"Proceedings of the 2018 Conference of\n               the North American Chapter of the\n               Association for Computational Linguistics:\n               Human Language Technologies, Volume 1 (Long\n               Papers)\",\n  year = \"2018\",\n  publisher = \"Association for Computational Linguistics\",\n  pages = \"1112--1122\",\n  location = \"New Orleans, Louisiana\",\n  url = \"http://aclweb.org/anthology/N18-1101\"\n}\n@article{bowman2015large,\n  title={A large annotated corpus for learning natural language inference},\n  author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},\n  journal={arXiv preprint arXiv:1508.05326},\n  year={2015}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. Please see the source to see\nthe correct citation for each contained dataset.", "homepage": "http://www.nyu.edu/projects/bowman/multinli/", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "mnli", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test_matched": {"name": "test_matched", "num_bytes": 1854787, "num_examples": 9796, "dataset_name": "glue"}, "test_mismatched": {"name": "test_mismatched", "num_bytes": 1956866, "num_examples": 9847, "dataset_name": "glue"}, "train": {"name": "train", "num_bytes": 74865118, "num_examples": 392702, "dataset_name": "glue"}, "validation_matched": {"name": "validation_matched", "num_bytes": 1839926, "num_examples": 9815, "dataset_name": "glue"}, "validation_mismatched": {"name": "validation_mismatched", "num_bytes": 1955384, "num_examples": 9832, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/MNLI.zip": {"num_bytes": 312783507, "checksum": "e7c1d896d26ed6caf700110645df426cc2d8ebf02a5ab743d5a5c68ac1c83633"}}, "download_size": 312783507, "post_processing_size": null, "dataset_size": 82472081, "size_in_bytes": 395255588}, "mnli_mismatched": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "@InProceedings{N18-1101,\n  author = \"Williams, Adina\n            and Nangia, Nikita\n            and Bowman, Samuel\",\n  title = \"A Broad-Coverage Challenge Corpus for\n           Sentence Understanding through Inference\",\n  booktitle = \"Proceedings of the 2018 Conference of\n               the North American Chapter of the\n               Association for Computational Linguistics:\n               Human Language Technologies, Volume 1 (Long\n               Papers)\",\n  year = \"2018\",\n  publisher = \"Association for Computational Linguistics\",\n  pages = \"1112--1122\",\n  location = \"New Orleans, Louisiana\",\n  url = \"http://aclweb.org/anthology/N18-1101\"\n}\n@article{bowman2015large,\n  title={A large annotated corpus for learning natural language inference},\n  author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},\n  journal={arXiv preprint arXiv:1508.05326},\n  year={2015}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. 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Please see the source to see\nthe correct citation for each contained dataset.", "homepage": "http://www.nyu.edu/projects/bowman/multinli/", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "mnli_matched", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1854787, "num_examples": 9796, "dataset_name": "glue"}, "validation": {"name": "validation", "num_bytes": 1839926, "num_examples": 9815, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/MNLI.zip": {"num_bytes": 312783507, "checksum": "e7c1d896d26ed6caf700110645df426cc2d8ebf02a5ab743d5a5c68ac1c83633"}}, "download_size": 312783507, "post_processing_size": null, "dataset_size": 3694713, "size_in_bytes": 316478220}, "qnli": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "@article{rajpurkar2016squad,\n  title={Squad: 100,000+ questions for machine comprehension of text},\n  author={Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy},\n  journal={arXiv preprint arXiv:1606.05250},\n  year={2016}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. Please see the source to see\nthe correct citation for each contained dataset.", "homepage": "https://rajpurkar.github.io/SQuAD-explorer/", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["entailment", "not_entailment"], "names_file": null, "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "qnli", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1376516, "num_examples": 5463, "dataset_name": "glue"}, "train": {"name": "train", "num_bytes": 25677924, "num_examples": 104743, "dataset_name": "glue"}, "validation": {"name": "validation", "num_bytes": 1371727, "num_examples": 5463, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/QNLIv2.zip": {"num_bytes": 10627589, "checksum": "e634e78627a29adaecd4f955359b22bf5e70f2cbd93b493f2d624138a0c0e5f5"}}, "download_size": 10627589, "post_processing_size": null, "dataset_size": 28426167, "size_in_bytes": 39053756}, "rte": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "@inproceedings{dagan2005pascal,\n  title={The PASCAL recognising textual entailment challenge},\n  author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},\n  booktitle={Machine Learning Challenges Workshop},\n  pages={177--190},\n  year={2005},\n  organization={Springer}\n}\n@inproceedings{bar2006second,\n  title={The second pascal recognising textual entailment challenge},\n  author={Bar-Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan},\n  booktitle={Proceedings of the second PASCAL challenges workshop on recognising textual entailment},\n  volume={6},\n  number={1},\n  pages={6--4},\n  year={2006},\n  organization={Venice}\n}\n@inproceedings{giampiccolo2007third,\n  title={The third pascal recognizing textual entailment challenge},\n  author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill},\n  booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing},\n  pages={1--9},\n  year={2007},\n  organization={Association for Computational Linguistics}\n}\n@inproceedings{bentivogli2009fifth,\n  title={The Fifth PASCAL Recognizing Textual Entailment Challenge.},\n  author={Bentivogli, Luisa and Clark, Peter and Dagan, Ido and Giampiccolo, Danilo},\n  booktitle={TAC},\n  year={2009}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. Please see the source to see\nthe correct citation for each contained dataset.", "homepage": "https://aclweb.org/aclwiki/Recognizing_Textual_Entailment", "license": "", "features": {"sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["entailment", "not_entailment"], "names_file": null, "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "rte", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 975936, "num_examples": 3000, "dataset_name": "glue"}, "train": {"name": "train", "num_bytes": 848888, "num_examples": 2490, "dataset_name": "glue"}, "validation": {"name": "validation", "num_bytes": 90911, "num_examples": 277, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/RTE.zip": {"num_bytes": 697150, "checksum": "6bf86de103ecd335f3441bd43574d23fef87ecc695977a63b82d5efb206556ee"}}, "download_size": 697150, "post_processing_size": null, "dataset_size": 1915735, "size_in_bytes": 2612885}, "wnli": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "@inproceedings{levesque2012winograd,\n  title={The winograd schema challenge},\n  author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},\n  booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},\n  year={2012}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. Please see the source to see\nthe correct citation for each contained dataset.", "homepage": "https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html", "license": "", "features": {"sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["not_entailment", "entailment"], "names_file": null, "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "wnli", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 37992, "num_examples": 146, "dataset_name": "glue"}, "train": {"name": "train", "num_bytes": 107517, "num_examples": 635, "dataset_name": "glue"}, "validation": {"name": "validation", "num_bytes": 12215, "num_examples": 71, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/WNLI.zip": {"num_bytes": 28999, "checksum": "ae0e8e4d16f4d46d4a0a566ec7ecceccfd3fbfaa4a7a4b4e02848c0f2561ac46"}}, "download_size": 28999, "post_processing_size": null, "dataset_size": 157724, "size_in_bytes": 186723}, "ax": {"description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n", "citation": "\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. Please see the source to see\nthe correct citation for each contained dataset.", "homepage": "https://gluebenchmark.com/diagnostics", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "glue", "config_name": "ax", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 238392, "num_examples": 1104, "dataset_name": "glue"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/AX.tsv": {"num_bytes": 222257, "checksum": "0e13510b1bb14436ff7e2ee82338f0efb0133ecf2e73507a697dc210db3f05fd"}}, "download_size": 222257, "post_processing_size": null, "dataset_size": 238392, "size_in_bytes": 460649}}

glue.py

@@ -1,628 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""The General Language Understanding Evaluation (GLUE) benchmark."""
-
-
-import csv
-import os
-import textwrap
-
-import numpy as np
-
-import datasets
-
-
-_GLUE_CITATION = """\
-@inproceedings{wang2019glue,
-  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
-  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
-  note={In the Proceedings of ICLR.},
-  year={2019}
-}
-"""
-
-_GLUE_DESCRIPTION = """\
-GLUE, the General Language Understanding Evaluation benchmark
-(https://gluebenchmark.com/) is a collection of resources for training,
-evaluating, and analyzing natural language understanding systems.
-
-"""
-
-_MRPC_DEV_IDS = "https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv"
-_MRPC_TRAIN = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
-_MRPC_TEST = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"
-
-_MNLI_BASE_KWARGS = dict(
-    text_features={
-        "premise": "sentence1",
-        "hypothesis": "sentence2",
-    },
-    label_classes=["entailment", "neutral", "contradiction"],
-    label_column="gold_label",
-    data_url="https://dl.fbaipublicfiles.com/glue/data/MNLI.zip",
-    data_dir="MNLI",
-    citation=textwrap.dedent(
-        """\
-      @InProceedings{N18-1101,
-        author = "Williams, Adina
-                  and Nangia, Nikita
-                  and Bowman, Samuel",
-        title = "A Broad-Coverage Challenge Corpus for
-                 Sentence Understanding through Inference",
-        booktitle = "Proceedings of the 2018 Conference of
-                     the North American Chapter of the
-                     Association for Computational Linguistics:
-                     Human Language Technologies, Volume 1 (Long
-                     Papers)",
-        year = "2018",
-        publisher = "Association for Computational Linguistics",
-        pages = "1112--1122",
-        location = "New Orleans, Louisiana",
-        url = "http://aclweb.org/anthology/N18-1101"
-      }
-      @article{bowman2015large,
-        title={A large annotated corpus for learning natural language inference},
-        author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},
-        journal={arXiv preprint arXiv:1508.05326},
-        year={2015}
-      }"""
-    ),
-    url="http://www.nyu.edu/projects/bowman/multinli/",
-)
-
-
-class GlueConfig(datasets.BuilderConfig):
-    """BuilderConfig for GLUE."""
-
-    def __init__(
-        self,
-        text_features,
-        label_column,
-        data_url,
-        data_dir,
-        citation,
-        url,
-        label_classes=None,
-        process_label=lambda x: x,
-        **kwargs,
-    ):
-        """BuilderConfig for GLUE.
-
-        Args:
-          text_features: `dict[string, string]`, map from the name of the feature
-            dict for each text field to the name of the column in the tsv file
-          label_column: `string`, name of the column in the tsv file corresponding
-            to the label
-          data_url: `string`, url to download the zip file from
-          data_dir: `string`, the path to the folder containing the tsv files in the
-            downloaded zip
-          citation: `string`, citation for the data set
-          url: `string`, url for information about the data set
-          label_classes: `list[string]`, the list of classes if the label is
-            categorical. If not provided, then the label will be of type
-            `datasets.Value('float32')`.
-          process_label: `Function[string, any]`, function  taking in the raw value
-            of the label and processing it to the form required by the label feature
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(GlueConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-        self.text_features = text_features
-        self.label_column = label_column
-        self.label_classes = label_classes
-        self.data_url = data_url
-        self.data_dir = data_dir
-        self.citation = citation
-        self.url = url
-        self.process_label = process_label
-
-
-class Glue(datasets.GeneratorBasedBuilder):
-    """The General Language Understanding Evaluation (GLUE) benchmark."""
-
-    BUILDER_CONFIGS = [
-        GlueConfig(
-            name="cola",
-            description=textwrap.dedent(
-                """\
-            The Corpus of Linguistic Acceptability consists of English
-            acceptability judgments drawn from books and journal articles on
-            linguistic theory. Each example is a sequence of words annotated
-            with whether it is a grammatical English sentence."""
-            ),
-            text_features={"sentence": "sentence"},
-            label_classes=["unacceptable", "acceptable"],
-            label_column="is_acceptable",
-            data_url="https://dl.fbaipublicfiles.com/glue/data/CoLA.zip",
-            data_dir="CoLA",
-            citation=textwrap.dedent(
-                """\
-            @article{warstadt2018neural,
-              title={Neural Network Acceptability Judgments},
-              author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
-              journal={arXiv preprint arXiv:1805.12471},
-              year={2018}
-            }"""
-            ),
-            url="https://nyu-mll.github.io/CoLA/",
-        ),
-        GlueConfig(
-            name="sst2",
-            description=textwrap.dedent(
-                """\
-            The Stanford Sentiment Treebank consists of sentences from movie reviews and
-            human annotations of their sentiment. The task is to predict the sentiment of a
-            given sentence. We use the two-way (positive/negative) class split, and use only
-            sentence-level labels."""
-            ),
-            text_features={"sentence": "sentence"},
-            label_classes=["negative", "positive"],
-            label_column="label",
-            data_url="https://dl.fbaipublicfiles.com/glue/data/SST-2.zip",
-            data_dir="SST-2",
-            citation=textwrap.dedent(
-                """\
-            @inproceedings{socher2013recursive,
-              title={Recursive deep models for semantic compositionality over a sentiment treebank},
-              author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
-              booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},
-              pages={1631--1642},
-              year={2013}
-            }"""
-            ),
-            url="https://datasets.stanford.edu/sentiment/index.html",
-        ),
-        GlueConfig(
-            name="mrpc",
-            description=textwrap.dedent(
-                """\
-            The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of
-            sentence pairs automatically extracted from online news sources, with human annotations
-            for whether the sentences in the pair are semantically equivalent."""
-            ),  # pylint: disable=line-too-long
-            text_features={"sentence1": "", "sentence2": ""},
-            label_classes=["not_equivalent", "equivalent"],
-            label_column="Quality",
-            data_url="",  # MRPC isn't hosted by GLUE.
-            data_dir="MRPC",
-            citation=textwrap.dedent(
-                """\
-            @inproceedings{dolan2005automatically,
-              title={Automatically constructing a corpus of sentential paraphrases},
-              author={Dolan, William B and Brockett, Chris},
-              booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},
-              year={2005}
-            }"""
-            ),
-            url="https://www.microsoft.com/en-us/download/details.aspx?id=52398",
-        ),
-        GlueConfig(
-            name="qqp",
-            description=textwrap.dedent(
-                """\
-            The Quora Question Pairs2 dataset is a collection of question pairs from the
-            community question-answering website Quora. The task is to determine whether a
-            pair of questions are semantically equivalent."""
-            ),
-            text_features={
-                "question1": "question1",
-                "question2": "question2",
-            },
-            label_classes=["not_duplicate", "duplicate"],
-            label_column="is_duplicate",
-            data_url="https://dl.fbaipublicfiles.com/glue/data/QQP-clean.zip",
-            data_dir="QQP",
-            citation=textwrap.dedent(
-                """\
-          @online{WinNT,
-            author = {Iyer, Shankar and Dandekar, Nikhil and Csernai, Kornel},
-            title = {First Quora Dataset Release: Question Pairs},
-            year = {2017},
-            url = {https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs},
-            urldate = {2019-04-03}
-          }"""
-            ),
-            url="https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs",
-        ),
-        GlueConfig(
-            name="stsb",
-            description=textwrap.dedent(
-                """\
-            The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of
-            sentence pairs drawn from news headlines, video and image captions, and natural
-            language inference data. Each pair is human-annotated with a similarity score
-            from 1 to 5."""
-            ),
-            text_features={
-                "sentence1": "sentence1",
-                "sentence2": "sentence2",
-            },
-            label_column="score",
-            data_url="https://dl.fbaipublicfiles.com/glue/data/STS-B.zip",
-            data_dir="STS-B",
-            citation=textwrap.dedent(
-                """\
-            @article{cer2017semeval,
-              title={Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation},
-              author={Cer, Daniel and Diab, Mona and Agirre, Eneko and Lopez-Gazpio, Inigo and Specia, Lucia},
-              journal={arXiv preprint arXiv:1708.00055},
-              year={2017}
-            }"""
-            ),
-            url="http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark",
-            process_label=np.float32,
-        ),
-        GlueConfig(
-            name="mnli",
-            description=textwrap.dedent(
-                """\
-            The Multi-Genre Natural Language Inference Corpus is a crowdsourced
-            collection of sentence pairs with textual entailment annotations. Given a premise sentence
-            and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis
-            (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are
-            gathered from ten different sources, including transcribed speech, fiction, and government reports.
-            We use the standard test set, for which we obtained private labels from the authors, and evaluate
-            on both the matched (in-domain) and mismatched (cross-domain) section. We also use and recommend
-            the SNLI corpus as 550k examples of auxiliary training data."""
-            ),
-            **_MNLI_BASE_KWARGS,
-        ),
-        GlueConfig(
-            name="mnli_mismatched",
-            description=textwrap.dedent(
-                """\
-          The mismatched validation and test splits from MNLI.
-          See the "mnli" BuilderConfig for additional information."""
-            ),
-            **_MNLI_BASE_KWARGS,
-        ),
-        GlueConfig(
-            name="mnli_matched",
-            description=textwrap.dedent(
-                """\
-          The matched validation and test splits from MNLI.
-          See the "mnli" BuilderConfig for additional information."""
-            ),
-            **_MNLI_BASE_KWARGS,
-        ),
-        GlueConfig(
-            name="qnli",
-            description=textwrap.dedent(
-                """\
-            The Stanford Question Answering Dataset is a question-answering
-            dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn
-            from Wikipedia) contains the answer to the corresponding question (written by an annotator). We
-            convert the task into sentence pair classification by forming a pair between each question and each
-            sentence in the corresponding context, and filtering out pairs with low lexical overlap between the
-            question and the context sentence. The task is to determine whether the context sentence contains
-            the answer to the question. This modified version of the original task removes the requirement that
-            the model select the exact answer, but also removes the simplifying assumptions that the answer
-            is always present in the input and that lexical overlap is a reliable cue."""
-            ),  # pylint: disable=line-too-long
-            text_features={
-                "question": "question",
-                "sentence": "sentence",
-            },
-            label_classes=["entailment", "not_entailment"],
-            label_column="label",
-            data_url="https://dl.fbaipublicfiles.com/glue/data/QNLIv2.zip",
-            data_dir="QNLI",
-            citation=textwrap.dedent(
-                """\
-            @article{rajpurkar2016squad,
-              title={Squad: 100,000+ questions for machine comprehension of text},
-              author={Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy},
-              journal={arXiv preprint arXiv:1606.05250},
-              year={2016}
-            }"""
-            ),
-            url="https://rajpurkar.github.io/SQuAD-explorer/",
-        ),
-        GlueConfig(
-            name="rte",
-            description=textwrap.dedent(
-                """\
-            The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual
-            entailment challenges. We combine the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim
-            et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009).4 Examples are
-            constructed based on news and Wikipedia text. We convert all datasets to a two-class split, where
-            for three-class datasets we collapse neutral and contradiction into not entailment, for consistency."""
-            ),  # pylint: disable=line-too-long
-            text_features={
-                "sentence1": "sentence1",
-                "sentence2": "sentence2",
-            },
-            label_classes=["entailment", "not_entailment"],
-            label_column="label",
-            data_url="https://dl.fbaipublicfiles.com/glue/data/RTE.zip",
-            data_dir="RTE",
-            citation=textwrap.dedent(
-                """\
-            @inproceedings{dagan2005pascal,
-              title={The PASCAL recognising textual entailment challenge},
-              author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},
-              booktitle={Machine Learning Challenges Workshop},
-              pages={177--190},
-              year={2005},
-              organization={Springer}
-            }
-            @inproceedings{bar2006second,
-              title={The second pascal recognising textual entailment challenge},
-              author={Bar-Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan},
-              booktitle={Proceedings of the second PASCAL challenges workshop on recognising textual entailment},
-              volume={6},
-              number={1},
-              pages={6--4},
-              year={2006},
-              organization={Venice}
-            }
-            @inproceedings{giampiccolo2007third,
-              title={The third pascal recognizing textual entailment challenge},
-              author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill},
-              booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing},
-              pages={1--9},
-              year={2007},
-              organization={Association for Computational Linguistics}
-            }
-            @inproceedings{bentivogli2009fifth,
-              title={The Fifth PASCAL Recognizing Textual Entailment Challenge.},
-              author={Bentivogli, Luisa and Clark, Peter and Dagan, Ido and Giampiccolo, Danilo},
-              booktitle={TAC},
-              year={2009}
-            }"""
-            ),
-            url="https://aclweb.org/aclwiki/Recognizing_Textual_Entailment",
-        ),
-        GlueConfig(
-            name="wnli",
-            description=textwrap.dedent(
-                """\
-            The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task
-            in which a system must read a sentence with a pronoun and select the referent of that pronoun from
-            a list of choices. The examples are manually constructed to foil simple statistical methods: Each
-            one is contingent on contextual information provided by a single word or phrase in the sentence.
-            To convert the problem into sentence pair classification, we construct sentence pairs by replacing
-            the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the
-            pronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of
-            new examples derived from fiction books that was shared privately by the authors of the original
-            corpus. While the included training set is balanced between two classes, the test set is imbalanced
-            between them (65% not entailment). Also, due to a data quirk, the development set is adversarial:
-            hypotheses are sometimes shared between training and development examples, so if a model memorizes the
-            training examples, they will predict the wrong label on corresponding development set
-            example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence
-            between a model's score on this task and its score on the unconverted original task. We
-            call converted dataset WNLI (Winograd NLI)."""
-            ),
-            text_features={
-                "sentence1": "sentence1",
-                "sentence2": "sentence2",
-            },
-            label_classes=["not_entailment", "entailment"],
-            label_column="label",
-            data_url="https://dl.fbaipublicfiles.com/glue/data/WNLI.zip",
-            data_dir="WNLI",
-            citation=textwrap.dedent(
-                """\
-            @inproceedings{levesque2012winograd,
-              title={The winograd schema challenge},
-              author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
-              booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
-              year={2012}
-            }"""
-            ),
-            url="https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html",
-        ),
-        GlueConfig(
-            name="ax",
-            description=textwrap.dedent(
-                """\
-            A manually-curated evaluation dataset for fine-grained analysis of
-            system performance on a broad range of linguistic phenomena. This
-            dataset evaluates sentence understanding through Natural Language
-            Inference (NLI) problems. Use a model trained on MulitNLI to produce
-            predictions for this dataset."""
-            ),
-            text_features={
-                "premise": "sentence1",
-                "hypothesis": "sentence2",
-            },
-            label_classes=["entailment", "neutral", "contradiction"],
-            label_column="",  # No label since we only have test set.
-            # We must use a URL shortener since the URL from GLUE is very long and
-            # causes issues in TFDS.
-            data_url="https://dl.fbaipublicfiles.com/glue/data/AX.tsv",
-            data_dir="",  # We are downloading a tsv.
-            citation="",  # The GLUE citation is sufficient.
-            url="https://gluebenchmark.com/diagnostics",
-        ),
-    ]
-
-    def _info(self):
-        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
-        if self.config.label_classes:
-            features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
-        else:
-            features["label"] = datasets.Value("float32")
-        features["idx"] = datasets.Value("int32")
-        return datasets.DatasetInfo(
-            description=_GLUE_DESCRIPTION,
-            features=datasets.Features(features),
-            homepage=self.config.url,
-            citation=self.config.citation + "\n" + _GLUE_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        if self.config.name == "ax":
-            data_file = dl_manager.download(self.config.data_url)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={
-                        "data_file": data_file,
-                        "split": "test",
-                    },
-                )
-            ]
-
-        if self.config.name == "mrpc":
-            data_dir = None
-            mrpc_files = dl_manager.download(
-                {
-                    "dev_ids": _MRPC_DEV_IDS,
-                    "train": _MRPC_TRAIN,
-                    "test": _MRPC_TEST,
-                }
-            )
-        else:
-            dl_dir = dl_manager.download_and_extract(self.config.data_url)
-            data_dir = os.path.join(dl_dir, self.config.data_dir)
-            mrpc_files = None
-        train_split = datasets.SplitGenerator(
-            name=datasets.Split.TRAIN,
-            gen_kwargs={
-                "data_file": os.path.join(data_dir or "", "train.tsv"),
-                "split": "train",
-                "mrpc_files": mrpc_files,
-            },
-        )
-        if self.config.name == "mnli":
-            return [
-                train_split,
-                _mnli_split_generator("validation_matched", data_dir, "dev", matched=True),
-                _mnli_split_generator("validation_mismatched", data_dir, "dev", matched=False),
-                _mnli_split_generator("test_matched", data_dir, "test", matched=True),
-                _mnli_split_generator("test_mismatched", data_dir, "test", matched=False),
-            ]
-        elif self.config.name == "mnli_matched":
-            return [
-                _mnli_split_generator("validation", data_dir, "dev", matched=True),
-                _mnli_split_generator("test", data_dir, "test", matched=True),
-            ]
-        elif self.config.name == "mnli_mismatched":
-            return [
-                _mnli_split_generator("validation", data_dir, "dev", matched=False),
-                _mnli_split_generator("test", data_dir, "test", matched=False),
-            ]
-        else:
-            return [
-                train_split,
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "data_file": os.path.join(data_dir or "", "dev.tsv"),
-                        "split": "dev",
-                        "mrpc_files": mrpc_files,
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={
-                        "data_file": os.path.join(data_dir or "", "test.tsv"),
-                        "split": "test",
-                        "mrpc_files": mrpc_files,
-                    },
-                ),
-            ]
-
-    def _generate_examples(self, data_file, split, mrpc_files=None):
-        if self.config.name == "mrpc":
-            # We have to prepare the MRPC dataset from the original sources ourselves.
-            examples = self._generate_example_mrpc_files(mrpc_files=mrpc_files, split=split)
-            for example in examples:
-                yield example["idx"], example
-        else:
-            process_label = self.config.process_label
-            label_classes = self.config.label_classes
-
-            # The train and dev files for CoLA are the only tsv files without a
-            # header.
-            is_cola_non_test = self.config.name == "cola" and split != "test"
-
-            with open(data_file, encoding="utf8") as f:
-                reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-                if is_cola_non_test:
-                    reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-
-                for n, row in enumerate(reader):
-                    if is_cola_non_test:
-                        row = {
-                            "sentence": row[3],
-                            "is_acceptable": row[1],
-                        }
-
-                    example = {feat: row[col] for feat, col in self.config.text_features.items()}
-                    example["idx"] = n
-
-                    if self.config.label_column in row:
-                        label = row[self.config.label_column]
-                        # For some tasks, the label is represented as 0 and 1 in the tsv
-                        # files and needs to be cast to integer to work with the feature.
-                        if label_classes and label not in label_classes:
-                            label = int(label) if label else None
-                        example["label"] = process_label(label)
-                    else:
-                        example["label"] = process_label(-1)
-
-                    # Filter out corrupted rows.
-                    for value in example.values():
-                        if value is None:
-                            break
-                    else:
-                        yield example["idx"], example
-
-    def _generate_example_mrpc_files(self, mrpc_files, split):
-        if split == "test":
-            with open(mrpc_files["test"], encoding="utf8") as f:
-                # The first 3 bytes are the utf-8 BOM \xef\xbb\xbf, which messes with
-                # the Quality key.
-                f.seek(3)
-                reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-                for n, row in enumerate(reader):
-                    yield {
-                        "sentence1": row["#1 String"],
-                        "sentence2": row["#2 String"],
-                        "label": int(row["Quality"]),
-                        "idx": n,
-                    }
-        else:
-            with open(mrpc_files["dev_ids"], encoding="utf8") as f:
-                reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-                dev_ids = [[row[0], row[1]] for row in reader]
-            with open(mrpc_files["train"], encoding="utf8") as f:
-                # The first 3 bytes are the utf-8 BOM \xef\xbb\xbf, which messes with
-                # the Quality key.
-                f.seek(3)
-                reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-                for n, row in enumerate(reader):
-                    is_row_in_dev = [row["#1 ID"], row["#2 ID"]] in dev_ids
-                    if is_row_in_dev == (split == "dev"):
-                        yield {
-                            "sentence1": row["#1 String"],
-                            "sentence2": row["#2 String"],
-                            "label": int(row["Quality"]),
-                            "idx": n,
-                        }
-
-
-def _mnli_split_generator(name, data_dir, split, matched):
-    return datasets.SplitGenerator(
-        name=name,
-        gen_kwargs={
-            "data_file": os.path.join(data_dir, "%s_%s.tsv" % (split, "matched" if matched else "mismatched")),
-            "split": split,
-            "mrpc_files": None,
-        },
-    )

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