Update glue.py

glue.py

@@ -135,296 +135,296 @@ class Glue(datasets.GeneratorBasedBuilder):
 
     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",
-        ),
+        #     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(