bool_q

• Description:

BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring, they are generated in unprompted and unconstrained settings.

Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://github.com/google-research-datasets/boolean-questions

• Source code: tfds.datasets.bool_q.Builder

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: 8.36 MiB

• Dataset size: 8.51 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'train'	9,427
'validation'	3,270

• Feature structure:

FeaturesDict({
    'answer': bool,
    'passage': Text(shape=(), dtype=string),
    'question': Text(shape=(), dtype=string),
    'title': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
answer	Tensor		bool
passage	Text		string
question	Text		string
title	Text		string

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@inproceedings{clark2019boolq,
  title =     {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
  author =    {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
  booktitle = {NAACL},
  year =      {2019},
}