Datasets:

nyu-mll
/

glue

@@ -6,7 +6,7 @@ language_creators:
 language:
 - en
 license:
-- other
 multilinguality:
 - monolingual
 size_categories:
@@ -23,46 +23,36 @@ 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: ax
   features:
-  - name: premise
-    dtype: string
-  - name: hypothesis
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
-          '0': entailment
-          '1': neutral
-          '2': contradiction
   - name: idx
     dtype: int32
   splits:
   - name: test
-    num_bytes: 237694
-    num_examples: 1104
-  download_size: 80767
-  dataset_size: 237694
-- config_name: cola
   features:
   - name: sentence
     dtype: string
@@ -70,56 +60,97 @@ dataset_info:
     dtype:
       class_label:
         names:
-          '0': unacceptable
-          '1': acceptable
   - name: idx
     dtype: int32
   splits:
   - name: train
-    num_bytes: 484869
-    num_examples: 8551
   - name: validation
-    num_bytes: 60322
-    num_examples: 1043
   - name: test
-    num_bytes: 60513
-    num_examples: 1063
-  download_size: 326394
-  dataset_size: 605704
-- config_name: mnli
   features:
-  - name: premise
     dtype: string
-  - name: hypothesis
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
-          '0': entailment
-          '1': neutral
-          '2': contradiction
   - name: idx
     dtype: int32
   splits:
   - name: train
-    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
@@ -135,14 +166,23 @@ dataset_info:
   - name: idx
     dtype: int32
   splits:
-  - name: validation
-    num_bytes: 1833783
-    num_examples: 9815
-  - name: test
-    num_bytes: 1848654
     num_examples: 9796
-  download_size: 2435055
-  dataset_size: 3682437
 - config_name: mnli_mismatched
   features:
   - name: premise
@@ -159,40 +199,38 @@ dataset_info:
   - name: idx
     dtype: int32
   splits:
-  - name: validation
-    num_bytes: 1949231
-    num_examples: 9832
   - name: test
-    num_bytes: 1950703
     num_examples: 9847
-  download_size: 2509009
-  dataset_size: 3899934
-- 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:
-  - name: train
-    num_bytes: 943843
-    num_examples: 3668
-  - name: validation
-    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
@@ -208,43 +246,17 @@ dataset_info:
   - name: idx
     dtype: int32
   splits:
-  - name: train
-    num_bytes: 25612443
-    num_examples: 104743
-  - name: validation
-    num_bytes: 1368304
-    num_examples: 5463
   - name: test
-    num_bytes: 1373093
     num_examples: 5463
-  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
@@ -260,182 +272,64 @@ dataset_info:
   - name: idx
     dtype: int32
   splits:
   - name: train
-    num_bytes: 847320
     num_examples: 2490
   - name: validation
-    num_bytes: 90728
     num_examples: 277
-  - name: test
-    num_bytes: 974053
-    num_examples: 3000
-  download_size: 1274409
-  dataset_size: 1912101
-- config_name: sst2
   features:
-  - name: sentence
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
-          '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: 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: 754791
-    num_examples: 5749
   - name: validation
-    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: sentence1
     dtype: string
-  - name: sentence2
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
-          '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: 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
@@ -545,6 +439,19 @@ train-eval-index:
     sentence1: text1
     sentence2: text2
     label: target
 ---
 # Dataset Card for GLUE
@@ -629,10 +536,9 @@ train-eval-index:
 ## Dataset Description
-- **Homepage:** https://gluebenchmark.com/
-- **Repository:** https://github.com/nyu-mll/GLUE-baselines
-- **Paper:** https://arxiv.org/abs/1804.07461
-- **Leaderboard:** https://gluebenchmark.com/leaderboard
 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 - **Size of downloaded dataset files:** 1.00 GB
 - **Size of the generated dataset:** 240.84 MB
@@ -775,7 +681,7 @@ An example of 'test' looks as follows.
 ```
 {
   "premise": "What have you decided, what are you going to do?",
-  "hypothesis": "So what's your decision?",
   "label": -1,
   "idx": 0
 }
@@ -783,114 +689,31 @@ An example of 'test' looks as follows.
 #### mrpc
-- **Size of downloaded dataset files:** ??
-- **Size of the generated dataset:** 1.5 MB
-- **Total amount of disk used:** ??
-An example of 'train' looks as follows.
-```
-{
-  "sentence1": "Amrozi accused his brother, whom he called "the witness", of deliberately distorting his evidence.",
-  "sentence2": "Referring to him as only "the witness", Amrozi accused his brother of deliberately distorting his evidence.",
-  "label": 1,
-  "idx": 0
-}
-```
 #### qnli
-- **Size of downloaded dataset files:** ??
-- **Size of the generated dataset:** 28 MB
-- **Total amount of disk used:** ??
-An example of 'train' looks as follows.
-```
-{
-  "question": "When did the third Digimon series begin?",
-  "sentence": "Unlike the two seasons before it and most of the seasons that followed, Digimon Tamers takes a darker and more realistic approach to its story featuring Digimon who do not reincarnate after their deaths and more complex character development in the original Japanese.",
-  "label": 1,
-  "idx": 0
-}
-```
 #### qqp
-- **Size of downloaded dataset files:** ??
-- **Size of the generated dataset:** 107 MB
-- **Total amount of disk used:** ??
-An example of 'train' looks as follows.
-```
-{
-  "question1": "How is the life of a math student? Could you describe your own experiences?",
-  "question2": "Which level of prepration is enough for the exam jlpt5?",
-  "label": 0,
-  "idx": 0
-}
-```
 #### rte
-- **Size of downloaded dataset files:** ??
-- **Size of the generated dataset:** 1.9 MB
-- **Total amount of disk used:** ??
-An example of 'train' looks as follows.
-```
-{
-  "sentence1": "No Weapons of Mass Destruction Found in Iraq Yet.",
-  "sentence2": "Weapons of Mass Destruction Found in Iraq.",
-  "label": 1,
-  "idx": 0
-}
-```
 #### sst2
-- **Size of downloaded dataset files:** ??
-- **Size of the generated dataset:** 4.9 MB
-- **Total amount of disk used:** ??
-An example of 'train' looks as follows.
-```
-{
-  "sentence": "hide new secretions from the parental units",
-  "label": 0,
-  "idx": 0
-}
-```
 #### stsb
-- **Size of downloaded dataset files:** ??
-- **Size of the generated dataset:** 1.2 MB
-- **Total amount of disk used:** ??
-An example of 'train' looks as follows.
-```
-{
-  "sentence1": "A plane is taking off.",
-  "sentence2": "An air plane is taking off.",
-  "label": 5.0,
-  "idx": 0
-}
-```
 #### wnli
-- **Size of downloaded dataset files:** ??
-- **Size of the generated dataset:** 0.18 MB
-- **Total amount of disk used:** ??
-An example of 'train' looks as follows.
-```
-{
-  "sentence1": "I stuck a pin through a carrot. When I pulled the pin out, it had a hole.",
-  "sentence2": "The carrot had a hole.",
-  "label": 1,
-  "idx": 0
-}
-```
 ### Data Fields
@@ -927,51 +750,31 @@ The data fields are the same among all splits.
 #### mrpc
-- `sentence1`: a `string` feature.
-- `sentence2`: a `string` feature.
-- `label`: a classification label, with possible values including `not_equivalent` (0), `equivalent` (1).
-- `idx`: a `int32` feature.
 #### qnli
-- `question`: a `string` feature.
-- `sentence`: a `string` feature.
-- `label`: a classification label, with possible values including `entailment` (0), `not_entailment` (1).
-- `idx`: a `int32` feature.
 #### qqp
-- `question1`: a `string` feature.
-- `question2`: a `string` feature.
-- `label`: a classification label, with possible values including `not_duplicate` (0), `duplicate` (1).
-- `idx`: a `int32` feature.
 #### rte
-- `sentence1`: a `string` feature.
-- `sentence2`: a `string` feature.
-- `label`: a classification label, with possible values including `entailment` (0), `not_entailment` (1).
-- `idx`: a `int32` feature.
 #### sst2
-- `sentence`: a `string` feature.
-- `label`: a classification label, with possible values including `negative` (0), `positive` (1).
-- `idx`: a `int32` feature.
 #### stsb
-- `sentence1`: a `string` feature.
-- `sentence2`: a `string` feature.
-- `label`: a float32 regression label, with possible values from 0 to 5.
-- `idx`: a `int32` feature.
 #### wnli
-- `sentence1`: a `string` feature.
-- `sentence2`: a `string` feature.
-- `label`: a classification label, with possible values including `not_entailment` (0), `entailment` (1).
-- `idx`: a `int32` feature.
 ### Data Splits
@@ -1085,102 +888,26 @@ The data fields are the same among all splits.
 ### Licensing Information
-The primary GLUE tasks are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset.
 ### Citation Information
-If you use GLUE, please cite all the datasets you use.
-In addition, we encourage you to use the following BibTeX citation for GLUE itself:
 ```
 @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}
 }
-```
-If you evaluate using GLUE, we also highly recommend citing the papers that originally introduced the nine GLUE tasks, both to give the original authors their due credit and because venues will expect papers to describe the data they evaluate on.
-The following provides BibTeX for all of the GLUE tasks, except QQP, for which we recommend adding a footnote to this page: https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs
-```
-@article{warstadt2018neural,
-  title={Neural Network Acceptability Judgments},
-  author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R.},
-  journal={arXiv preprint 1805.12471},
-  year={2018}
-}
-@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 EMNLP},
-  pages={1631--1642},
-  year={2013}
-}
-@inproceedings{dolan2005automatically,
-  title={Automatically constructing a corpus of sentential paraphrases},
-  author={Dolan, William B and Brockett, Chris},
-  booktitle={Proceedings of the International Workshop on Paraphrasing},
-  year={2005}
-}
-@book{agirre2007semantic,
-  editor    = {Agirre, Eneko and M`arquez, Llu'{i}s and Wicentowski, Richard},
-  title     = {Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)},
-  month     = {June},
-  year      = {2007},
-  address   = {Prague, Czech Republic},
-  publisher = {Association for Computational Linguistics},
-}
-@inproceedings{williams2018broad,
-  author    = {Williams, Adina and Nangia, Nikita and Bowman, Samuel R.},
-  title = {A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference},
-  booktitle = {Proceedings of NAACL-HLT},
-  year = 2018
-}
-@inproceedings{rajpurkar2016squad,
-  author = {Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy}
-  title = {{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text},
-  booktitle = {Proceedings of EMNLP}
-  year = {2016},
-  publisher = {Association for Computational Linguistics},
-  pages = {2383--2392},
-  location = {Austin, Texas},
-}
-@incollection{dagan2006pascal,
-  title={The {PASCAL} recognising textual entailment challenge},
-  author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},
-  booktitle={Machine learning challenges. evaluating predictive uncertainty, visual object classification, and recognising tectual entailment},
-  pages={177--190},
-  year={2006},
-  publisher={Springer}
-}
-@article{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},
-  year={2006}
-}
-@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},
-}
-@article{bentivogli2009fifth,
-  title={The Fifth {PASCAL} Recognizing Textual Entailment Challenge},
-  author={Bentivogli, Luisa and Dagan, Ido and Dang, Hoa Trang and Giampiccolo, Danilo and Magnini, Bernardo},
-  booktitle={TAC},
-  year={2009}
-}
-@inproceedings{levesque2011winograd,
-  title={The {W}inograd schema challenge},
-  author={Levesque, Hector J and Davis, Ernest and Morgenstern, Leora},
-  booktitle={{AAAI} Spring Symposium: Logical Formalizations of Commonsense Reasoning},
-  volume={46},
-  pages={47},
-  year={2011}
-}
 ```

 language:
 - en
 license:
+- cc-by-4.0
 multilinguality:
 - monolingual
 size_categories:
 - text-scoring
 paperswithcode_id: glue
 pretty_name: GLUE (General Language Understanding Evaluation benchmark)
 tags:
 - qa-nli
 - coreference-nli
 - paraphrase-identification
 dataset_info:
+- config_name: cola
   features:
+  - name: sentence
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
+          '0': unacceptable
+          '1': acceptable
   - 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
   features:
   - name: sentence
     dtype: string
     dtype:
       class_label:
         names:
+          '0': negative
+          '1': positive
   - name: idx
     dtype: int32
   splits:
+  - name: test
+    num_bytes: 217556
+    num_examples: 1821
   - name: train
+    num_bytes: 4715283
+    num_examples: 67349
   - 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:
   - 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
   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: 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
+  - 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
   features:
   - name: premise
     dtype: string
   - 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
+    num_examples: 9815
+  - name: validation_mismatched
+    num_bytes: 1955384
+    num_examples: 9832
+  download_size: 312783507
+  dataset_size: 82472081
 - config_name: mnli_mismatched
   features:
   - name: premise
   - name: idx
     dtype: int32
   splits:
   - name: test
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     num_examples: 9847
+  - name: validation
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+  download_size: 312783507
+  dataset_size: 3912250
+- config_name: mnli_matched
   features:
+  - name: premise
     dtype: string
+  - name: hypothesis
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
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+          '1': neutral
+          '2': contradiction
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     dtype: int32
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   - name: test
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+    num_examples: 9796
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+  dataset_size: 3694713
 - config_name: qnli
   features:
   - name: question
   - name: idx
     dtype: int32
   splits:
   - name: test
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     num_examples: 5463
   - name: train
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   - name: validation
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+  dataset_size: 28426167
 - config_name: rte
   features:
   - name: sentence1
   - name: idx
     dtype: int32
   splits:
+  - name: test
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+    num_examples: 3000
   - name: train
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     num_examples: 2490
   - name: validation
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     num_examples: 277
+  download_size: 697150
+  dataset_size: 1915735
+- config_name: wnli
   features:
+  - name: sentence1
+    dtype: string
+  - name: sentence2
     dtype: string
   - name: label
     dtype:
       class_label:
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+          '1': entailment
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   - name: train
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+    num_examples: 635
   - name: validation
+    num_bytes: 12215
+    num_examples: 71
+  download_size: 28999
+  dataset_size: 157724
+- config_name: ax
   features:
+  - name: premise
     dtype: string
+  - name: hypothesis
     dtype: string
   - name: label
     dtype:
       class_label:
         names:
+          '0': entailment
+          '1': neutral
+          '2': contradiction
   - name: idx
     dtype: int32
   splits:
   - name: test
+    num_bytes: 238392
+    num_examples: 1104
+  download_size: 222257
+  dataset_size: 238392
 train-eval-index:
 - config: cola
   task: text-classification
     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
 ## Dataset Description
+- **Homepage:** [https://nyu-mll.github.io/CoLA/](https://nyu-mll.github.io/CoLA/)
+- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 - **Size of downloaded dataset files:** 1.00 GB
 - **Size of the generated dataset:** 240.84 MB
 ```
 {
   "premise": "What have you decided, what are you going to do?",
+  "hypothesis": "So what's your decision?,
   "label": -1,
   "idx": 0
 }
 #### mrpc
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### qnli
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### qqp
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### rte
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### sst2
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### stsb
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### wnli
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 ### Data Fields
 #### mrpc
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### qnli
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### qqp
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### rte
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### sst2
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### stsb
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 #### wnli
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 ### Data Splits
 ### Licensing Information
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 ### Citation Information
 ```
+@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}
+}
 @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}
 }
+Note that each GLUE dataset has its own citation. Please see the source to see
+the correct citation for each contained dataset.
 ```

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dataset_infos.json ADDED Viewed

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+ {"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. 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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. 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_mismatched", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1956866, "num_examples": 9847, "dataset_name": "glue"}, "validation": {"name": "validation", "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": 3912250, "size_in_bytes": 316695757}, "mnli_matched": {"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_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 ADDED Viewed

	@@ -0,0 +1,628 @@

+# 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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