README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - natural-language-inference
paperswithcode_id: hans
pretty_name: Heuristic Analysis for NLI Systems
dataset_info:
  features:
    - name: premise
      dtype: string
    - name: hypothesis
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': entailment
            '1': non-entailment
    - name: parse_premise
      dtype: string
    - name: parse_hypothesis
      dtype: string
    - name: binary_parse_premise
      dtype: string
    - name: binary_parse_hypothesis
      dtype: string
    - name: heuristic
      dtype: string
    - name: subcase
      dtype: string
    - name: template
      dtype: string
  config_name: plain_text
  splits:
    - name: train
      num_bytes: 15916371
      num_examples: 30000
    - name: validation
      num_bytes: 15893137
      num_examples: 30000
  download_size: 30947358
  dataset_size: 31809508

Dataset Card for "hans"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• 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://github.com/tommccoy1/hans
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 30.94 MB
• Size of the generated dataset: 31.81 MB
• Total amount of disk used: 62.76 MB

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

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

plain_text

• Size of downloaded dataset files: 30.94 MB
• Size of the generated dataset: 31.81 MB
• Total amount of disk used: 62.76 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

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

More Information Needed

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

@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.