Dataset: hans


Dataset Card for "hans"

Table of Contents

Dataset Description

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

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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 Sample Size

name train validation
plain_text 30000 30000

Dataset Creation

Curation Rationale

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Source Data

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Annotations

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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}
}

Models trained or fine-tuned on hans

None yet