- 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}
}