doc_nli

• Description:

DocNLI is a large-scale dataset for document-level natural language inference (NLI). DocNLI is transformed from a broad range of NLP problems and covers multiple genres of text. The premises always stay in the document granularity, whereas the hypotheses vary in length from single sentences to passages with hundreds of words. In contrast to some existing sentence-level NLI datasets, DocNLI has pretty limited artifacts.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://github.com/salesforce/DocNLI/

• Source code: tfds.text.docnli.DocNLI

• Versions:

  • 1.0.0 (default): Initial release.

• Download size: 313.89 MiB

• Dataset size: 3.07 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	267,086
'train'	942,314
'validation'	234,258

• Feature structure:

FeaturesDict({
    'hypothesis': Text(shape=(), dtype=string),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
    'premise': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
hypothesis	Text		string
label	ClassLabel		int64
premise	Text		string

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@inproceedings{yin-etal-2021-docnli,
    title={DocNLI: A Large-scale Dataset for Document-level Natural Language Inference},
    author={Wenpeng Yin and Dragomir Radev and Caiming Xiong},
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
}