Datasets:

nyu-mll
/

glue

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