Datasets:
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- other
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-scoring
paperswithcode_id: glue
pretty_name: GLUE (General Language Understanding Evaluation benchmark)
config_names:
- ax
- cola
- mnli
- mnli_matched
- mnli_mismatched
- mrpc
- qnli
- qqp
- rte
- sst2
- stsb
- wnli
tags:
- qa-nli
- coreference-nli
- paraphrase-identification
dataset_info:
- config_name: ax
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
- name: test
num_bytes: 237694
num_examples: 1104
download_size: 80767
dataset_size: 237694
- config_name: cola
features:
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': unacceptable
'1': acceptable
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 484869
num_examples: 8551
- name: validation
num_bytes: 60322
num_examples: 1043
- name: test
num_bytes: 60513
num_examples: 1063
download_size: 326394
dataset_size: 605704
- config_name: mnli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 74619646
num_examples: 392702
- name: validation_matched
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num_examples: 9815
- name: validation_mismatched
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num_examples: 9832
- name: test_matched
num_bytes: 1848654
num_examples: 9796
- name: test_mismatched
num_bytes: 1950703
num_examples: 9847
download_size: 57168425
dataset_size: 82202017
- config_name: mnli_matched
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 1833783
num_examples: 9815
- name: test
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num_examples: 9796
download_size: 2435055
dataset_size: 3682437
- config_name: mnli_mismatched
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 1949231
num_examples: 9832
- name: test
num_bytes: 1950703
num_examples: 9847
download_size: 2509009
dataset_size: 3899934
- config_name: mrpc
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': not_equivalent
'1': equivalent
- name: idx
dtype: int32
splits:
- name: train
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num_examples: 3668
- name: validation
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num_examples: 408
- name: test
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num_examples: 1725
download_size: 1033400
dataset_size: 1492132
- config_name: qnli
features:
- name: question
dtype: string
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': not_entailment
- name: idx
dtype: int32
splits:
- name: train
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num_examples: 104743
- name: validation
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num_examples: 5463
- name: test
num_bytes: 1373093
num_examples: 5463
download_size: 19278324
dataset_size: 28353840
- config_name: qqp
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype:
class_label:
names:
'0': not_duplicate
'1': duplicate
- name: idx
dtype: int32
splits:
- name: train
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num_examples: 363846
- name: validation
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num_examples: 40430
- name: test
num_bytes: 55171111
num_examples: 390965
download_size: 73982265
dataset_size: 111725685
- config_name: rte
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': not_entailment
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 847320
num_examples: 2490
- name: validation
num_bytes: 90728
num_examples: 277
- name: test
num_bytes: 974053
num_examples: 3000
download_size: 1274409
dataset_size: 1912101
- config_name: sst2
features:
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 4681603
num_examples: 67349
- name: validation
num_bytes: 106252
num_examples: 872
- name: test
num_bytes: 216640
num_examples: 1821
download_size: 3331080
dataset_size: 5004495
- config_name: stsb
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float32
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 754791
num_examples: 5749
- name: validation
num_bytes: 216064
num_examples: 1500
- name: test
num_bytes: 169974
num_examples: 1379
download_size: 766983
dataset_size: 1140829
- config_name: wnli
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': not_entailment
'1': entailment
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 107109
num_examples: 635
- name: validation
num_bytes: 12162
num_examples: 71
- name: test
num_bytes: 37889
num_examples: 146
download_size: 63522
dataset_size: 157160
configs:
- config_name: ax
data_files:
- split: test
path: ax/test-*
- config_name: cola
data_files:
- split: train
path: cola/train-*
- split: validation
path: cola/validation-*
- split: test
path: cola/test-*
- config_name: mnli
data_files:
- split: train
path: mnli/train-*
- split: validation_matched
path: mnli/validation_matched-*
- split: validation_mismatched
path: mnli/validation_mismatched-*
- split: test_matched
path: mnli/test_matched-*
- split: test_mismatched
path: mnli/test_mismatched-*
- config_name: mnli_matched
data_files:
- split: validation
path: mnli_matched/validation-*
- split: test
path: mnli_matched/test-*
- config_name: mnli_mismatched
data_files:
- split: validation
path: mnli_mismatched/validation-*
- split: test
path: mnli_mismatched/test-*
- config_name: mrpc
data_files:
- split: train
path: mrpc/train-*
- split: validation
path: mrpc/validation-*
- split: test
path: mrpc/test-*
- config_name: qnli
data_files:
- split: train
path: qnli/train-*
- split: validation
path: qnli/validation-*
- split: test
path: qnli/test-*
- config_name: qqp
data_files:
- split: train
path: qqp/train-*
- split: validation
path: qqp/validation-*
- split: test
path: qqp/test-*
- config_name: rte
data_files:
- split: train
path: rte/train-*
- split: validation
path: rte/validation-*
- split: test
path: rte/test-*
- config_name: sst2
data_files:
- split: train
path: sst2/train-*
- split: validation
path: sst2/validation-*
- split: test
path: sst2/test-*
- config_name: stsb
data_files:
- split: train
path: stsb/train-*
- split: validation
path: stsb/validation-*
- split: test
path: stsb/test-*
- config_name: wnli
data_files:
- split: train
path: wnli/train-*
- split: validation
path: wnli/validation-*
- split: test
path: wnli/test-*
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
Dataset Card for GLUE
Table of Contents
- Dataset Card for GLUE
Dataset Description
- Homepage: https://gluebenchmark.com/
- Repository: https://github.com/nyu-mll/GLUE-baselines
- Paper: https://arxiv.org/abs/1804.07461
- Leaderboard: https://gluebenchmark.com/leaderboard
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 1.00 GB
- Size of the generated dataset: 240.84 MB
- Total amount of disk used: 1.24 GB
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: 0.22 MB
- Size of the generated dataset: 0.24 MB
- Total amount of disk used: 0.46 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: 0.38 MB
- Size of the generated dataset: 0.61 MB
- Total amount of disk used: 0.99 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: 312.78 MB
- Size of the generated dataset: 82.47 MB
- Total amount of disk used: 395.26 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: 312.78 MB
- Size of the generated dataset: 3.69 MB
- Total amount of disk used: 316.48 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: 312.78 MB
- Size of the generated dataset: 3.91 MB
- Total amount of disk used: 316.69 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
- Size of downloaded dataset files: ??
- Size of the generated dataset: 1.5 MB
- Total amount of disk used: ??
An example of 'train' looks as follows.
{
"sentence1": "Amrozi accused his brother, whom he called "the witness", of deliberately distorting his evidence.",
"sentence2": "Referring to him as only "the witness", Amrozi accused his brother of deliberately distorting his evidence.",
"label": 1,
"idx": 0
}
qnli
- Size of downloaded dataset files: ??
- Size of the generated dataset: 28 MB
- Total amount of disk used: ??
An example of 'train' looks as follows.
{
"question": "When did the third Digimon series begin?",
"sentence": "Unlike the two seasons before it and most of the seasons that followed, Digimon Tamers takes a darker and more realistic approach to its story featuring Digimon who do not reincarnate after their deaths and more complex character development in the original Japanese.",
"label": 1,
"idx": 0
}
qqp
- Size of downloaded dataset files: ??
- Size of the generated dataset: 107 MB
- Total amount of disk used: ??
An example of 'train' looks as follows.
{
"question1": "How is the life of a math student? Could you describe your own experiences?",
"question2": "Which level of prepration is enough for the exam jlpt5?",
"label": 0,
"idx": 0
}
rte
- Size of downloaded dataset files: ??
- Size of the generated dataset: 1.9 MB
- Total amount of disk used: ??
An example of 'train' looks as follows.
{
"sentence1": "No Weapons of Mass Destruction Found in Iraq Yet.",
"sentence2": "Weapons of Mass Destruction Found in Iraq.",
"label": 1,
"idx": 0
}
sst2
- Size of downloaded dataset files: ??
- Size of the generated dataset: 4.9 MB
- Total amount of disk used: ??
An example of 'train' looks as follows.
{
"sentence": "hide new secretions from the parental units",
"label": 0,
"idx": 0
}
stsb
- Size of downloaded dataset files: ??
- Size of the generated dataset: 1.2 MB
- Total amount of disk used: ??
An example of 'train' looks as follows.
{
"sentence1": "A plane is taking off.",
"sentence2": "An air plane is taking off.",
"label": 5.0,
"idx": 0
}
wnli
- Size of downloaded dataset files: ??
- Size of the generated dataset: 0.18 MB
- Total amount of disk used: ??
An example of 'train' looks as follows.
{
"sentence1": "I stuck a pin through a carrot. When I pulled the pin out, it had a hole.",
"sentence2": "The carrot had a hole.",
"label": 1,
"idx": 0
}
Data Fields
The data fields are the same among all splits.
ax
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
cola
sentence: astringfeature.label: a classification label, with possible values includingunacceptable(0),acceptable(1).idx: aint32feature.
mnli
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
mnli_matched
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
mnli_mismatched
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
mrpc
sentence1: astringfeature.sentence2: astringfeature.label: a classification label, with possible values includingnot_equivalent(0),equivalent(1).idx: aint32feature.
qnli
question: astringfeature.sentence: astringfeature.label: a classification label, with possible values includingentailment(0),not_entailment(1).idx: aint32feature.
qqp
question1: astringfeature.question2: astringfeature.label: a classification label, with possible values includingnot_duplicate(0),duplicate(1).idx: aint32feature.
rte
sentence1: astringfeature.sentence2: astringfeature.label: a classification label, with possible values includingentailment(0),not_entailment(1).idx: aint32feature.
sst2
sentence: astringfeature.label: a classification label, with possible values includingnegative(0),positive(1).idx: aint32feature.
stsb
sentence1: astringfeature.sentence2: astringfeature.label: a float32 regression label, with possible values from 0 to 5.idx: aint32feature.
wnli
sentence1: astringfeature.sentence2: astringfeature.label: a classification label, with possible values includingnot_entailment(0),entailment(1).idx: aint32feature.
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
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
The primary GLUE tasks are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset.
Citation Information
If you use GLUE, please cite all the datasets you use.
In addition, we encourage you to use the following BibTeX citation for GLUE itself:
@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}
}
If you evaluate using GLUE, we also highly recommend citing the papers that originally introduced the nine GLUE tasks, both to give the original authors their due credit and because venues will expect papers to describe the data they evaluate on. The following provides BibTeX for all of the GLUE tasks, except QQP, for which we recommend adding a footnote to this page: https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs
@article{warstadt2018neural,
title={Neural Network Acceptability Judgments},
author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R.},
journal={arXiv preprint 1805.12471},
year={2018}
}
@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 EMNLP},
pages={1631--1642},
year={2013}
}
@inproceedings{dolan2005automatically,
title={Automatically constructing a corpus of sentential paraphrases},
author={Dolan, William B and Brockett, Chris},
booktitle={Proceedings of the International Workshop on Paraphrasing},
year={2005}
}
@book{agirre2007semantic,
editor = {Agirre, Eneko and M`arquez, Llu'{i}s and Wicentowski, Richard},
title = {Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)},
month = {June},
year = {2007},
address = {Prague, Czech Republic},
publisher = {Association for Computational Linguistics},
}
@inproceedings{williams2018broad,
author = {Williams, Adina and Nangia, Nikita and Bowman, Samuel R.},
title = {A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference},
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Contributions
Thanks to @patpizio, @jeswan, @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset.