sci_tail

• Description:

The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question and the correct answer choice are converted into an assertive statement to form the hypothesis. Information retrieval is used to obtain relevant text from a large text corpus of web sentences, and these sentences are used as a premise P. The annotation of such premise-hypothesis pair is crowdsourced as supports (entails) or not (neutral), in order to create the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples with neutral label.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://allenai.org/data/scitail

• Source code: tfds.datasets.sci_tail.Builder

• Versions:

  • 1.0.0 (default): Initial release.

• Download size: 13.52 MiB

• Dataset size: 6.01 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	2,126
'train'	23,097
'validation'	1,304

• 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{khot2018scitail,
    title={Scitail: A textual entailment dataset from science question answering},
    author={Khot, Tushar and Sabharwal, Ashish and Clark, Peter},
    booktitle={Proceedings of the 32th AAAI Conference on Artificial Intelligence (AAAI 2018)},
    url = "http://ai2-website.s3.amazonaws.com/publications/scitail-aaai-2018_cameraready.pdf",
    year={2018}
}