README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
  - found
language:
  - en
license:
  - cc-by-3.0
  - cc-by-sa-3.0
  - mit
  - other
license_details: Open Portion of the American National Corpus
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - natural-language-inference
  - multi-input-text-classification
paperswithcode_id: multinli
pretty_name: Multi-Genre Natural Language Inference
dataset_info:
  features:
    - name: premise
      dtype: string
    - name: hypothesis
      dtype: string
    - name: label
      dtype: string
  config_name: plain_text
  splits:
    - name: train
      num_bytes: 75601459
      num_examples: 392702
    - name: validation
      num_bytes: 2009444
      num_examples: 10000
  download_size: 226850426
  dataset_size: 77610903

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

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://www.nyu.edu/projects/bowman/multinli/
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 226.85 MB
• Size of the generated dataset: 77.62 MB
• Total amount of disk used: 304.46 MB

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

More Information Needed

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

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

@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.