Back to all datasets
Dataset: multi_nli 🏷
Update multi_nli.py on GitHub

How to load this dataset directly with the πŸ€—/datasets library:

				
Copy to clipboard
from datasets import load_dataset dataset = load_dataset("multi_nli")

Tags  

None yet.

You can create or edit a tag set using our tagging app.

Models trained or fine-tuned on multi_nli



Dataset Card for "multi_nli"

Table of Contents

Dataset Description

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

More Information Needed

Languages

More Information Needed

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: 73.39 MB
  • Total amount of disk used: 289.74 MB

An example of 'validation_matched' looks as follows.

{
    "hypothesis": "flammable",
    "label": 0,
    "premise": "inflammable"
}

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).

Data Splits Sample Size

name train validation_matched validation_mismatched
plain_text 392702 9815 9832

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

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"
}