openbookqa

• Description:

The dataset contains 5,957 4-way multiple choice questions. Additionally, they provide 5,167 crowd-sourced common knowledge facts, and an expanded version of the train/dev/test questions where each question is associated with its originating core fact, a human accuracy score, a clarity score, and an anonymized crowd-worker ID.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://leaderboard.allenai.org/open_book_qa/submissions/get-started

• Source code: tfds.datasets.openbookqa.Builder

• Versions:

  • 0.1.0 (default): No release notes.

• Download size: 1.38 MiB

• Dataset size: 2.40 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	500
'train'	4,957
'validation'	500

• Feature structure:

FeaturesDict({
    'answerKey': ClassLabel(shape=(), dtype=int64, num_classes=4),
    'clarity': float32,
    'fact1': Text(shape=(), dtype=string),
    'humanScore': float32,
    'question': FeaturesDict({
        'choice_A': Text(shape=(), dtype=string),
        'choice_B': Text(shape=(), dtype=string),
        'choice_C': Text(shape=(), dtype=string),
        'choice_D': Text(shape=(), dtype=string),
        'stem': Text(shape=(), dtype=string),
    }),
    'turkIdAnonymized': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
answerKey	ClassLabel		int64
clarity	Tensor		float32
fact1	Text		string
humanScore	Tensor		float32
question	FeaturesDict
question/choice_A	Text		string
question/choice_B	Text		string
question/choice_C	Text		string
question/choice_D	Text		string
question/stem	Text		string
turkIdAnonymized	Text		string

• Supervised keys (See as_supervised doc): ('question', 'answerKey')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@article{mihaylov2018can,
  title={Can a suit of armor conduct electricity? a new dataset for open book question answering},
  author={Mihaylov, Todor and Clark, Peter and Khot, Tushar and Sabharwal, Ashish},
  journal={arXiv preprint arXiv:1809.02789},
  year={2018}
}