Dataset:



Dataset Card for "esnli"

Dataset Summary

The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to include human-annotated natural language explanations of the entailment relations.

Supported Tasks

More Information Needed

Languages

More Information Needed

Dataset Structure

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

Data Instances

plain_text

  • Size of downloaded dataset files: 195.04 MB
  • Size of the generated dataset: 109.52 MB
  • Total amount of disk used: 304.56 MB

An example of 'validation' looks as follows.

{
    "explanation_1": "A woman must be present to smile.",
    "explanation_2": "A woman smiling implies that she is present.",
    "explanation_3": "A smiling woman is also present.",
    "hypothesis": "A woman is present.",
    "label": 0,
    "premise": "A woman smiles at the child."
}

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), neutral (1), contradiction (2).
  • explanation_1: a string feature.
  • explanation_2: a string feature.
  • explanation_3: a string feature.

Data Splits Sample Size

name train validation test
plain_text 549367 9842 9824

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

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


@incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {9539--9549},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
}

Contributions

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

Update on GitHub
Explore dataset Edit Model Tags

Models trained or fine-tuned on esnli

None yet