Datasets:
hans

Languages: en

Dataset Card for "hans"

Dataset Summary

The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

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

Data Instances

plain_text

  • Size of downloaded dataset files: 29.51 MB
  • Size of the generated dataset: 30.34 MB
  • Total amount of disk used: 59.85 MB

An example of 'train' looks as follows.


Data Fields

The data fields are the same among all splits.

plain_text

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), non-entailment (1).
  • parse_premise: a string feature.
  • parse_hypothesis: a string feature.
  • binary_parse_premise: a string feature.
  • binary_parse_hypothesis: a string feature.
  • heuristic: a string feature.
  • subcase: a string feature.
  • template: a string feature.

Data Splits

name train validation
plain_text 30000 30000

Dataset Creation

Curation Rationale

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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?

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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

@article{DBLP:journals/corr/abs-1902-01007,
  author    = {R. Thomas McCoy and
               Ellie Pavlick and
               Tal Linzen},
  title     = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural
               Language Inference},
  journal   = {CoRR},
  volume    = {abs/1902.01007},
  year      = {2019},
  url       = {http://arxiv.org/abs/1902.01007},
  archivePrefix = {arXiv},
  eprint    = {1902.01007},
  timestamp = {Tue, 21 May 2019 18:03:36 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Contributions

Thanks to @TevenLeScao, @thomwolf for adding this dataset.

Models trained or fine-tuned on hans

None yet