mrpc test

.idea/.gitignore

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+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

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README.md

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+---
+annotations_creators:
+- other
+language_creators:
+- other
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- acceptability-classification
+- natural-language-inference
+- semantic-similarity-scoring
+- sentiment-classification
+- text-classification-other-coreference-nli
+- text-classification-other-paraphrase-identification
+- text-classification-other-qa-nli
+- text-scoring
+paperswithcode_id: glue
+pretty_name: GLUE (General Language Understanding Evaluation benchmark)
+train-eval-index:
+- config: cola
+  task: text-classification
+  task_id: binary_classification
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    sentence: text
+    label: target
+- config: sst2
+  task: text-classification
+  task_id: binary_classification
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    sentence: text
+    label: target
+- config: mrpc
+  task: text-classification
+  task_id: natural_language_inference
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    sentence1: text1
+    sentence2: text2
+    label: target
+- config: qqp
+  task: text-classification
+  task_id: natural_language_inference
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    question1: text1
+    question2: text2
+    label: target
+- config: stsb
+  task: text-classification
+  task_id: natural_language_inference
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    sentence1: text1
+    sentence2: text2
+    label: target
+- config: mnli
+  task: text-classification
+  task_id: natural_language_inference
+  splits:
+    train_split: train
+    eval_split: validation_matched
+  col_mapping:
+    premise: text1
+    hypothesis: text2
+    label: target
+- config: mnli_mismatched
+  task: text-classification
+  task_id: natural_language_inference
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    premise: text1
+    hypothesis: text2
+    label: target
+- config: mnli_matched
+  task: text-classification
+  task_id: natural_language_inference
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    premise: text1
+    hypothesis: text2
+    label: target
+- config: qnli
+  task: text-classification
+  task_id: natural_language_inference
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    question: text1
+    sentence: text2
+    label: target
+- config: rte
+  task: text-classification
+  task_id: natural_language_inference
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    sentence1: text1
+    sentence2: text2
+    label: target
+- config: wnli
+  task: text-classification
+  task_id: natural_language_inference
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    sentence1: text1
+    sentence2: text2
+    label: target
+configs:
+- ax
+- cola
+- mnli
+- mnli_matched
+- mnli_mismatched
+- mrpc
+- qnli
+- qqp
+- rte
+- sst2
+- stsb
+- wnli
+---
+
+# Dataset Card for GLUE
+
+## Table of Contents
+- [Dataset Card for GLUE](#dataset-card-for-glue)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+      - [ax](#ax)
+      - [cola](#cola)
+      - [mnli](#mnli)
+      - [mnli_matched](#mnli_matched)
+      - [mnli_mismatched](#mnli_mismatched)
+      - [mrpc](#mrpc)
+      - [qnli](#qnli)
+      - [qqp](#qqp)
+      - [rte](#rte)
+      - [sst2](#sst2)
+      - [stsb](#stsb)
+      - [wnli](#wnli)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+      - [ax](#ax-1)
+      - [cola](#cola-1)
+      - [mnli](#mnli-1)
+      - [mnli_matched](#mnli_matched-1)
+      - [mnli_mismatched](#mnli_mismatched-1)
+      - [mrpc](#mrpc-1)
+      - [qnli](#qnli-1)
+      - [qqp](#qqp-1)
+      - [rte](#rte-1)
+      - [sst2](#sst2-1)
+      - [stsb](#stsb-1)
+      - [wnli](#wnli-1)
+    - [Data Fields](#data-fields)
+      - [ax](#ax-2)
+      - [cola](#cola-2)
+      - [mnli](#mnli-2)
+      - [mnli_matched](#mnli_matched-2)
+      - [mnli_mismatched](#mnli_mismatched-2)
+      - [mrpc](#mrpc-2)
+      - [qnli](#qnli-2)
+      - [qqp](#qqp-2)
+      - [rte](#rte-2)
+      - [sst2](#sst2-2)
+      - [stsb](#stsb-2)
+      - [wnli](#wnli-2)
+    - [Data Splits](#data-splits)
+      - [ax](#ax-3)
+      - [cola](#cola-3)
+      - [mnli](#mnli-3)
+      - [mnli_matched](#mnli_matched-3)
+      - [mnli_mismatched](#mnli_mismatched-3)
+      - [mrpc](#mrpc-3)
+      - [qnli](#qnli-3)
+      - [qqp](#qqp-3)
+      - [rte](#rte-3)
+      - [sst2](#sst2-3)
+      - [stsb](#stsb-3)
+      - [wnli](#wnli-3)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://nyu-mll.github.io/CoLA/](https://nyu-mll.github.io/CoLA/)
+- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of downloaded dataset files:** 955.33 MB
+- **Size of the generated dataset:** 229.68 MB
+- **Total amount of disk used:** 1185.01 MB
+
+### Dataset Summary
+
+GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
+
+### Supported Tasks and Leaderboards
+
+The leaderboard for the GLUE benchmark can be found [at this address](https://gluebenchmark.com/). It comprises the following tasks:
+
+#### ax
+
+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.
+
+#### cola
+
+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.
+
+#### mnli
+
+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. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.
+
+#### mnli_matched
+
+The matched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
+
+#### mnli_mismatched
+
+The mismatched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
+
+#### mrpc
+
+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.
+
+#### qnli
+
+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). The authors of the benchmark 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.
+
+#### qqp
+
+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.
+
+#### rte
+
+The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined 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). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.
+
+#### sst2
+
+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. It uses the two-way (positive/negative) class split, with only sentence-level labels.
+
+#### stsb
+
+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.
+
+#### wnli
+
+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, the authors of the benchmark 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. They 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. The authors of the benchmark call converted dataset WNLI (Winograd NLI).
+
+### Languages
+
+The language data in GLUE is in English (BCP-47 `en`)
+
+## Dataset Structure
+
+### Data Instances
+
+#### ax
+
+- **Size of downloaded dataset files:** 0.21 MB
+- **Size of the generated dataset:** 0.23 MB
+- **Total amount of disk used:** 0.44 MB
+
+An example of 'test' looks as follows.
+```
+{
+  "premise": "The cat sat on the mat.",
+  "hypothesis": "The cat did not sit on the mat.",
+  "label": -1,
+  "idx: 0
+}
+```
+
+#### cola
+
+- **Size of downloaded dataset files:** 0.36 MB
+- **Size of the generated dataset:** 0.58 MB
+- **Total amount of disk used:** 0.94 MB
+
+An example of 'train' looks as follows.
+```
+{
+  "sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
+  "label": 1,
+  "id": 0
+}
+```
+
+#### mnli
+
+- **Size of downloaded dataset files:** 298.29 MB
+- **Size of the generated dataset:** 78.65 MB
+- **Total amount of disk used:** 376.95 MB
+
+An example of 'train' looks as follows.
+```
+{
+  "premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
+  "hypothesis": "Product and geography are what make cream skimming work.",
+  "label": 1,
+  "idx": 0
+}
+```
+
+#### mnli_matched
+
+- **Size of downloaded dataset files:** 298.29 MB
+- **Size of the generated dataset:** 3.52 MB
+- **Total amount of disk used:** 301.82 MB
+
+An example of 'test' looks as follows.
+```
+{
+  "premise": "Hierbas, ans seco, ans dulce, and frigola are just a few names worth keeping a look-out for.",
+  "hypothesis": "Hierbas is a name worth looking out for.",
+  "label": -1,
+  "idx": 0
+}
+```
+
+#### mnli_mismatched
+
+- **Size of downloaded dataset files:** 298.29 MB
+- **Size of the generated dataset:** 3.73 MB
+- **Total amount of disk used:** 302.02 MB
+
+An example of 'test' looks as follows.
+```
+{
+  "premise": "What have you decided, what are you going to do?",
+  "hypothesis": "So what's your decision?,
+  "label": -1,
+  "idx": 0
+}
+```
+
+#### mrpc
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qqp
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### rte
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### sst2
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### stsb
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### wnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### ax
+- `premise`: a `string` feature.
+- `hypothesis`: a `string` feature.
+- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
+- `idx`: a `int32` feature.
+
+#### cola
+- `sentence`: a `string` feature.
+- `label`: a classification label, with possible values including `unacceptable` (0), `acceptable` (1).
+- `idx`: a `int32` feature.
+
+#### mnli
+- `premise`: a `string` feature.
+- `hypothesis`: a `string` feature.
+- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
+- `idx`: a `int32` feature.
+
+#### mnli_matched
+- `premise`: a `string` feature.
+- `hypothesis`: a `string` feature.
+- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
+- `idx`: a `int32` feature.
+
+#### mnli_mismatched
+- `premise`: a `string` feature.
+- `hypothesis`: a `string` feature.
+- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
+- `idx`: a `int32` feature.
+
+#### mrpc
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qqp
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### rte
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### sst2
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### stsb
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### wnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Data Splits
+
+#### ax
+
+|   |test|
+|---|---:|
+|ax |1104|
+
+#### cola
+
+|    |train|validation|test|
+|----|----:|---------:|---:|
+|cola| 8551|      1043|1063|
+
+#### mnli
+
+|    |train |validation_matched|validation_mismatched|test_matched|test_mismatched|
+|----|-----:|-----------------:|--------------------:|-----------:|--------------:|
+|mnli|392702|              9815|                 9832|        9796|           9847|
+
+#### mnli_matched
+
+|            |validation|test|
+|------------|---------:|---:|
+|mnli_matched|      9815|9796|
+
+#### mnli_mismatched
+
+|               |validation|test|
+|---------------|---------:|---:|
+|mnli_mismatched|      9832|9847|
+
+#### mrpc
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qqp
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### rte
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### sst2
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### stsb
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### wnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@article{warstadt2018neural,
+  title={Neural Network Acceptability Judgments},
+  author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
+  journal={arXiv preprint arXiv:1805.12471},
+  year={2018}
+}
+@inproceedings{wang2019glue,
+  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
+  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
+  note={In the Proceedings of ICLR.},
+  year={2019}
+}
+
+Note that each GLUE dataset has its own citation. Please see the source to see
+the correct citation for each contained dataset.
+```
+
+
+### Contributions
+
+Thanks to [@patpizio](https://github.com/patpizio), [@jeswan](https://github.com/jeswan), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

dataset_infos.json

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+    "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
+    "citation": "@inproceedings{dagan2005pascal,\n  title={The PASCAL recognising textual entailment challenge},\n  author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},\n  booktitle={Machine Learning Challenges Workshop},\n  pages={177--190},\n  year={2005},\n  organization={Springer}\n}\n@inproceedings{bar2006second,\n  title={The second pascal recognising textual entailment challenge},\n  author={Bar-Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan},\n  booktitle={Proceedings of the second PASCAL challenges workshop on recognising textual entailment},\n  volume={6},\n  number={1},\n  pages={6--4},\n  year={2006},\n  organization={Venice}\n}\n@inproceedings{giampiccolo2007third,\n  title={The third pascal recognizing textual entailment challenge},\n  author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill},\n  booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing},\n  pages={1--9},\n  year={2007},\n  organization={Association for Computational Linguistics}\n}\n@inproceedings{bentivogli2009fifth,\n  title={The Fifth PASCAL Recognizing Textual Entailment Challenge.},\n  author={Bentivogli, Luisa and Clark, Peter and Dagan, Ido and Giampiccolo, Danilo},\n  booktitle={TAC},\n  year={2009}\n}\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n\nNote that each GLUE dataset has its own citation. Please see the source to see\nthe correct citation for each contained dataset.",
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+# 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://huggingface.co/datasets/evaluate/glue-ci/resolve/main/msr_paraphrase_test.txt" #"https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
+_MRPC_TEST = "https://huggingface.co/datasets/evaluate/glue-ci/resolve/main/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 = {"dev_ids": _MRPC_DEV_IDS, "train": _MRPC_TEST, "test": _MRPC_TEST}
+            # 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,
+        },
+    )