Datasets:

nyu-mll
/

multi_nli_mismatch

Tasks:

Text Classification

Sub-tasks:

natural-language-inference

multi-input-text-classification

Languages:

English

Size:

100K<n<1M

License:

Dataset card Files Files and versions Community

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Dataset Card for Multi-Genre Natural Language Inference (Mismatched only)

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 and Leaderboards

More Information Needed

Languages

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Dataset Structure

Data Instances

plain_text

Size of downloaded dataset files: 226.85 MB
Size of the generated dataset: 77.62 MB
Total amount of disk used: 304.46 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

name	train	validation
plain_text	392702	10000

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

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

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"
}