Dataset Card for "super_glue"

Dataset Summary

SuperGLUE ( https://super.gluebenchmark.com/ ) is a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, improved resources, and a new public leaderboard.

BoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short passage and a yes/no question about the passage. The questions are provided anonymously and unsolicited by users of the Google search engine, and afterwards paired with a paragraph from a Wikipedia article containing the answer. Following the original work, we evaluate with accuracy.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

axb

• Size of downloaded dataset files: 0.03 MB
• Size of the generated dataset: 0.24 MB
• Total amount of disk used: 0.27 MB

An example of 'test' looks as follows.

axg

• Size of downloaded dataset files: 0.01 MB
• Size of the generated dataset: 0.05 MB
• Total amount of disk used: 0.06 MB

An example of 'test' looks as follows.

boolq

• Size of downloaded dataset files: 4.12 MB
• Size of the generated dataset: 10.40 MB
• Total amount of disk used: 14.52 MB

An example of 'train' looks as follows.

cb

• Size of downloaded dataset files: 0.07 MB
• Size of the generated dataset: 0.20 MB
• Total amount of disk used: 0.28 MB

An example of 'train' looks as follows.

copa

• Size of downloaded dataset files: 0.04 MB
• Size of the generated dataset: 0.13 MB
• Total amount of disk used: 0.17 MB

An example of 'train' looks as follows.

Data Fields

The data fields are the same among all splits.

axb

• sentence1 : a string feature.
• sentence2 : a string feature.
• idx : a int32 feature.
• label : a classification label, with possible values including entailment (0), not_entailment (1).

axg

• premise : a string feature.
• hypothesis : a string feature.
• idx : a int32 feature.
• label : a classification label, with possible values including entailment (0), not_entailment (1).

boolq

• question : a string feature.
• passage : a string feature.
• idx : a int32 feature.
• label : a classification label, with possible values including False (0), True (1).

cb

• premise : a string feature.
• hypothesis : a string feature.
• idx : a int32 feature.
• label : a classification label, with possible values including entailment (0), contradiction (1), neutral (2).

copa

• premise : a string feature.
• choice1 : a string feature.
• choice2 : a string feature.
• question : a string feature.
• idx : a int32 feature.
• label : a classification label, with possible values including choice1 (0), choice2 (1).

Data Splits

axb

test
axb	1104

axg

test
axg	356

boolq

train	validation	test
boolq	9427	3270	3245

cb

train	validation	test
cb	250	56	250

copa

train	validation	test
copa	400	100	500

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

More Information Needed

Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@inproceedings{clark2019boolq,
  title={BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
  author={Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
  booktitle={NAACL},
  year={2019}
}
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}

Note that each SuperGLUE dataset has its own citation. Please see the source to
get the correct citation for each contained dataset.

Contributions

Thanks to @thomwolf , @lewtun , @patrickvonplaten for adding this dataset.