Dataset Card for "multi_nli_mismatch"

Dataset Summary

The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation. The corpus served as the basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.

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: 216.34 MB
  • Size of the generated dataset: 74.02 MB
  • Total amount of disk used: 290.36 MB

An example of 'train' looks as follows.

{
    "hypothesis": "independence",
    "label": "contradiction",
    "premise": "correlation"
}

Data Fields

The data fields are the same among all splits.

plain_text

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a string feature.

Data Splits Sample Size

name train validation
plain_text 392702 10000

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

@InProceedings{N18-1101,
  author = "Williams, Adina
            and Nangia, Nikita
            and Bowman, Samuel",
  title = "A Broad-Coverage Challenge Corpus for
           Sentence Understanding through Inference",
  booktitle = "Proceedings of the 2018 Conference of
               the North American Chapter of the
               Association for Computational Linguistics:
               Human Language Technologies, Volume 1 (Long
               Papers)",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  pages = "1112--1122",
  location = "New Orleans, Louisiana",
  url = "http://aclweb.org/anthology/N18-1101"
}

Contributions

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

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