metadata

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: sst2
    task: text-classification
    task_id: multi_class_classification
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      sentence: text
      label: target
    metrics:
      - type: glue
        name: GLUE
        config: sst2
  - config: cola
    task: text-classification
    task_id: multi_class_classification
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      sentence: text
      label: target
    metrics:
      - type: glue
        name: GLUE
        config: cola
configs:
  - ax
  - cola
  - mnli
  - mnli_matched
  - mnli_mismatched
  - mrpc
  - qnli
  - qqp
  - rte
  - sst2
  - stsb
  - wnli

Dataset Card for GLUE

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: https://nyu-mll.github.io/CoLA/
Repository: More Information Needed
Paper: More Information Needed
Point of Contact: More Information Needed
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. 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
}

Dataset Card for GLUE

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

ax

cola

mnli

mnli_matched

mnli_mismatched

mrpc

qnli

qqp

rte

sst2

stsb

wnli

Languages

Dataset Structure

Data Instances

ax

cola

mnli

mnli_matched

mnli_mismatched

mrpc

qnli

qqp

rte

sst2

stsb

wnli

Data Fields

ax

cola

mnli

mnli_matched

mnli_mismatched

mrpc

qnli

qqp

rte

sst2

stsb

wnli

Data Splits

ax

cola

mnli

mnli_matched

mnli_mismatched

mrpc

qnli

qqp

rte

sst2

stsb

wnli

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions