Datasets:
metadata
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
languages:
- en
licenses:
- cc-by-3-0
- cc-by-sa-3-0-at
- mit
- other-Open Portion of the American National Corpus
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-scoring
task_ids:
- semantic-similarity-scoring
paperswithcode_id: multinli
pretty_name: Multi-Genre Natural Language Inference
Dataset Card for Multi-Genre Natural Language Inference (Mismatched only)
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
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: 216.34 MB
- Size of the generated dataset: 74.02 MB
- Total amount of disk used: 290.36 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
Languages
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: astringfeature.hypothesis: astringfeature.label: astringfeature.
Data Splits
| name | train | validation |
|---|---|---|
| plain_text | 392702 | 10000 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
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
@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.