README.md

metadata

language:
  - en
paperswithcode_id: e-snli
pretty_name: e-SNLI
dataset_info:
  features:
    - name: premise
      dtype: string
    - name: hypothesis
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': entailment
            '1': neutral
            '2': contradiction
    - name: explanation_1
      dtype: string
    - name: explanation_2
      dtype: string
    - name: explanation_3
      dtype: string
  config_name: plain_text
  splits:
    - name: test
      num_bytes: 3387169
      num_examples: 9824
    - name: train
      num_bytes: 108024142
      num_examples: 549367
    - name: validation
      num_bytes: 3423725
      num_examples: 9842
  download_size: 204516010
  dataset_size: 114835036

Dataset Card for "esnli"

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/OanaMariaCamburu/e-SNLI
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 204.51 MB
• Size of the generated dataset: 114.84 MB
• Total amount of disk used: 319.35 MB

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 and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

plain_text

• Size of downloaded dataset files: 204.51 MB
• Size of the generated dataset: 114.84 MB
• Total amount of disk used: 319.35 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

name	train	validation	test
plain_text	549367	9842	9824

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

@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.