Languages: en

Dataset Card for "art"

Dataset Summary

the Abductive Natural Language Inference Dataset from AI2

Supported Tasks and Leaderboards

More Information Needed


More Information Needed

Dataset Structure

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

Data Instances


  • Size of downloaded dataset files: 4.88 MB
  • Size of the generated dataset: 32.77 MB
  • Total amount of disk used: 37.65 MB

An example of 'train' looks as follows.

    "hypothesis_1": "Chad's car had all sorts of other problems besides alignment.",
    "hypothesis_2": "Chad's car had all sorts of benefits other than being sexy.",
    "label": 1,
    "observation_1": "Chad went to get the wheel alignment measured on his car.",
    "observation_2": "The mechanic provided a working alignment with new body work."

Data Fields

The data fields are the same among all splits.


  • observation_1: a string feature.
  • observation_2: a string feature.
  • hypothesis_1: a string feature.
  • hypothesis_2: a string feature.
  • label: a classification label, with possible values including 0 (0), 1 (1), 2 (2).

Data Splits

name train validation
anli 169654 1532

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


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

  author = "Chandra, Bhagavatula
    and Ronan, Le Bras
    and Chaitanya, Malaviya
    and Keisuke, Sakaguchi
    and Ari, Holtzman
    and Hannah, Rashkin
    and Doug, Downey
    and Scott, Wen-tau Yih
    and Yejin, Choi",
  title = "Abductive Commonsense Reasoning",
  year = "2020",


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

Models trained or fine-tuned on art