first working version (v0.0.1)

README.md

@@ -5,7 +5,11 @@ language_creators:
 - machine-generated
 source_datasets:
   - glue
-pretty_name: GLUE for European Portuguese (machine translated)
+pretty_name: The General Language Understanding Evaluation (GLUE) benchmark translated to European Portuguese (pt_PT)
 size_categories:
 - 10K<n<100K
 ---
+
+See [gluebenchmark.com](https://gluebenchmark.com/) for information about the General Language Understanding Evaluation (GLUE) dataset.
+
+

glue_data_ptpt_v0.0.1.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64e0750f2b7ecfb20c42d8e6f48798dfa45039b1f887521cc9878a9c36a1b7c8
+size 2010782

glueptpt.py

@@ -0,0 +1,662 @@
+# 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
+# https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py
+"""The General Language Understanding Evaluation (GLUE) benchmark translated to European Portuguese (pt_PT)."""
+
+
+import csv
+import os
+import textwrap
+
+import numpy as np
+
+import datasets
+
+
+_GLUEPTPT_CITATION = """\
+@misc{Gomes2023,
+  author = {Gomes, Luís M. S. and Silva, João R. and Santos, Rodrigo and Rodrigues, João and Branco, António H.},
+  title = {The General Language Understanding Evaluation (GLUE) benchmark translated to European Portuguese (pt_PT)},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\\url{https://github.com/nlx-group/glueptpt}},
+  commit = {CURRENT_COMMIT}
+}
+"""
+
+_GLUEPTPT_DESCRIPTION = """\
+GLUEPTPT is an European Portuguese translation of the GLUE benchmark using DeepL Pro.
+"""
+
+
+_MNLI_BASE_KWARGS = dict(
+    text_features={"premise": "sentence1", "hypothesis": "sentence2",},
+    label_classes=["entailment", "neutral", "contradiction"],
+    label_column="gold_label",
+    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 GLUEPTPTConfig(datasets.BuilderConfig):
+    """BuilderConfig for GLUE."""
+
+    def __init__(
+        self,
+        text_features,
+        label_column,
+        data_dir,
+        citation,
+        url,
+        label_classes=None,
+        process_label=lambda x: x,
+        **kwargs,
+    ):
+        """BuilderConfig for GLUEPTPT.
+
+        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(GLUEPTPTConfig, self).__init__(
+            version=datasets.Version("0.0.1", ""), **kwargs
+        )
+        self.text_features = text_features
+        self.label_column = label_column
+        self.label_classes = label_classes
+        self.data_url = (
+            "/workspace/glueptpt/glue_data_ptpt_v0.0.1.tar.gz"
+        # "https://github.com/nlx-group/glueptpt/archive/refs/tags/v0.0.1.zip"
+        )
+        self.data_dir = data_dir
+        self.citation = citation
+        self.url = url
+        self.process_label = process_label
+
+
+class GLUEPTPT(datasets.GeneratorBasedBuilder):
+    """The General Language Understanding Evaluation (GLUE) benchmark."""
+
+    BUILDER_CONFIGS = [
+        GLUEPTPTConfig(
+            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_dir="glue_data_ptpt/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/",
+        ),
+        GLUEPTPTConfig(
+            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_dir="glue_data_ptpt/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",
+        ),
+        GLUEPTPTConfig(
+            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_dir="glue_data_ptpt/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",
+        ),
+        GLUEPTPTConfig(
+            name="qqp_v2",
+            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_dir="glue_data_ptpt/QQP_v2",
+            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",
+        ),
+        GLUEPTPTConfig(
+            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_dir="glue_data_ptpt/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,
+        ),
+        GLUEPTPTConfig(
+            name="snli",
+            description=textwrap.dedent(
+                """\
+            The SNLI corpus (version 1.0) is a collection of 570k human-written English
+            sentence pairs manually labeled for balanced classification with the labels
+            entailment, contradiction, and neutral, supporting the task of natural language
+            inference (NLI), also known as recognizing textual entailment (RTE).
+            """
+            ),
+            text_features={"premise": "sentence1", "hypothesis": "sentence2",},
+            label_classes=["entailment", "neutral", "contradiction"],
+            label_column="gold_label",
+            data_dir="SNLI",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{snli:emnlp2015,
+                Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
+                Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+                Publisher = {Association for Computational Linguistics},
+                Title = {A large annotated corpus for learning natural language inference},
+                Year = {2015}
+            }
+            """
+            ),
+            url="https://nlp.stanford.edu/projects/snli/",
+        ),
+        GLUEPTPTConfig(
+            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,
+        ),
+        GLUEPTPTConfig(
+            name="mnli_mismatched",
+            description=textwrap.dedent(
+                """\
+          The mismatched validation and test splits from MNLI.
+          See the "mnli" BuilderConfig for additional information."""
+            ),
+            **_MNLI_BASE_KWARGS,
+        ),
+        GLUEPTPTConfig(
+            name="mnli_matched",
+            description=textwrap.dedent(
+                """\
+          The matched validation and test splits from MNLI.
+          See the "mnli" BuilderConfig for additional information."""
+            ),
+            **_MNLI_BASE_KWARGS,
+        ),
+        GLUEPTPTConfig(
+            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_dir="glue_data_ptpt/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/",
+        ),
+        GLUEPTPTConfig(
+            name="qnli_v2",
+            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_dir="glue_data_ptpt/QNLI_v2",
+            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/",
+        ),
+        GLUEPTPTConfig(
+            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_dir="glue_data_ptpt/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",
+        ),
+        GLUEPTPTConfig(
+            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_dir="glue_data_ptpt/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",
+        ),
+        GLUEPTPTConfig(
+            name="scitail",
+            description=textwrap.dedent(
+                """\
+            The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
+and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
+retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We
+crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create
+the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples
+with neutral label"""
+            ),
+            text_features={"premise": "premise", "hypothesis": "hypothesis",},
+            label_classes=["entails", "neutral"],
+            label_column="label",
+            data_dir="glue_data_ptpt/SciTail",
+            citation=""""\
+            inproceedings{scitail,
+                Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
+                Booktitle = {AAAI},
+                Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
+                Year = {2018}
+            }
+            """,
+            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=_GLUEPTPT_DESCRIPTION,
+            features=datasets.Features(features),
+            homepage=self.config.url,
+            citation=self.config.citation + "\n" + _GLUEPTPT_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_url = self.config.data_url
+
+        dl_dir = dl_manager.download_and_extract(data_url)
+        data_dir = os.path.join(dl_dir, self.config.data_dir)
+
+        train_split = datasets.SplitGenerator(
+            name=datasets.Split.TRAIN,
+            gen_kwargs={
+                "data_file": os.path.join(data_dir, "train.tsv"),
+                "split": "train",
+            },
+        )
+        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, "dev.tsv"),
+                        "split": "dev",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "data_file": os.path.join(data_dir, "test.tsv"),
+                        "split": "test",
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, data_file, split):
+        if self.config.name in ["mrpc", "scitail"]:
+            if self.config.name == "mrpc":
+                examples = self._generate_example_mrpc_files(
+                    data_file=data_file, split=split
+                )
+            elif self.config.name == "scitail":
+                examples = self._generate_example_scitail_files(
+                    data_file=data_file, 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, data_file, split):
+        print(data_file)
+
+        with open(data_file, encoding="utf-8-sig") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for idx, row in enumerate(reader):
+                label = row["Quality"] if split != "test" else -1
+
+                yield {
+                    "sentence1": row["#1 String"],
+                    "sentence2": row["#2 String"],
+                    "label": int(label),
+                    "idx": idx,
+                }
+
+    def _generate_example_scitail_files(self, data_file, split):
+        with open(data_file, encoding="utf8") as f:
+            reader = csv.DictReader(
+                f,
+                delimiter="\t",
+                quoting=csv.QUOTE_NONE,
+                fieldnames=["premise", "hypothesis", "label"],
+            )
+            for idx, row in enumerate(reader):
+                label = row["label"] if split != "test" else -1
+
+                yield {
+                    "premise": row["premise"],
+                    "hypothesis": row["hypothesis"],
+                    "label": label,
+                    "idx": idx,
+                }
+
+
+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,
+        },
+    )
+