Dataset:
super_glue
1

Languages: en

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

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

Data Instances

axb

  • Size of downloaded dataset files: 0.03 MB
  • Size of the generated dataset: 0.23 MB
  • Total amount of disk used: 0.26 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: 3.93 MB
  • Size of the generated dataset: 9.92 MB
  • Total amount of disk used: 13.85 MB

An example of 'train' looks as follows.

cb

  • Size of downloaded dataset files: 0.07 MB
  • Size of the generated dataset: 0.19 MB
  • Total amount of disk used: 0.27 MB

An example of 'train' looks as follows.

copa

  • Size of downloaded dataset files: 0.04 MB
  • Size of the generated dataset: 0.12 MB
  • Total amount of disk used: 0.16 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

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

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Who are the annotators?

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

@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.

Models trained or fine-tuned on super_glue

None yet