README.md

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Dataset Card for "glue"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits Sample Size
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • 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

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

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

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.

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.

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.

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 'validation' looks as follows.

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 'validation' looks as follows.

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.

Data Splits Sample Size

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

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

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, @jeswan, @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset.