Datasets:
Languages:
English
Multilinguality:
monolingual
Language Creators:
machine-generated
Annotations Creators:
crowdsourced
Source Datasets:
original
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Tags:
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languages:
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paperswithcode_id: triviaqa
Dataset Card for "trivia_qa"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: http://nlp.cs.washington.edu/triviaqa/
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 8833.35 MB
- Size of the generated dataset: 43351.32 MB
- Total amount of disk used: 52184.66 MB
Dataset Summary
TriviaqQA is a reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaqQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions.
Supported Tasks and Leaderboards
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
rc
- Size of downloaded dataset files: 2542.29 MB
- Size of the generated dataset: 15275.31 MB
- Total amount of disk used: 17817.60 MB
An example of 'train' looks as follows.
rc.nocontext
- Size of downloaded dataset files: 2542.29 MB
- Size of the generated dataset: 120.42 MB
- Total amount of disk used: 2662.71 MB
An example of 'train' looks as follows.
unfiltered
- Size of downloaded dataset files: 3145.53 MB
- Size of the generated dataset: 27884.47 MB
- Total amount of disk used: 31030.00 MB
An example of 'validation' looks as follows.
unfiltered.nocontext
- Size of downloaded dataset files: 603.25 MB
- Size of the generated dataset: 71.11 MB
- Total amount of disk used: 674.35 MB
An example of 'train' looks as follows.
Data Fields
The data fields are the same among all splits.
rc
question: astringfeature.question_id: astringfeature.question_source: astringfeature.entity_pages: a dictionary feature containing:doc_source: astringfeature.filename: astringfeature.title: astringfeature.wiki_context: astringfeature.
search_results: a dictionary feature containing:description: astringfeature.filename: astringfeature.rank: aint32feature.title: astringfeature.url: astringfeature.search_context: astringfeature.
aliases: alistofstringfeatures.normalized_aliases: alistofstringfeatures.matched_wiki_entity_name: astringfeature.normalized_matched_wiki_entity_name: astringfeature.normalized_value: astringfeature.type: astringfeature.value: astringfeature.
rc.nocontext
question: astringfeature.question_id: astringfeature.question_source: astringfeature.entity_pages: a dictionary feature containing:doc_source: astringfeature.filename: astringfeature.title: astringfeature.wiki_context: astringfeature.
search_results: a dictionary feature containing:description: astringfeature.filename: astringfeature.rank: aint32feature.title: astringfeature.url: astringfeature.search_context: astringfeature.
aliases: alistofstringfeatures.normalized_aliases: alistofstringfeatures.matched_wiki_entity_name: astringfeature.normalized_matched_wiki_entity_name: astringfeature.normalized_value: astringfeature.type: astringfeature.value: astringfeature.
unfiltered
question: astringfeature.question_id: astringfeature.question_source: astringfeature.entity_pages: a dictionary feature containing:doc_source: astringfeature.filename: astringfeature.title: astringfeature.wiki_context: astringfeature.
search_results: a dictionary feature containing:description: astringfeature.filename: astringfeature.rank: aint32feature.title: astringfeature.url: astringfeature.search_context: astringfeature.
aliases: alistofstringfeatures.normalized_aliases: alistofstringfeatures.matched_wiki_entity_name: astringfeature.normalized_matched_wiki_entity_name: astringfeature.normalized_value: astringfeature.type: astringfeature.value: astringfeature.
unfiltered.nocontext
question: astringfeature.question_id: astringfeature.question_source: astringfeature.entity_pages: a dictionary feature containing:doc_source: astringfeature.filename: astringfeature.title: astringfeature.wiki_context: astringfeature.
search_results: a dictionary feature containing:description: astringfeature.filename: astringfeature.rank: aint32feature.title: astringfeature.url: astringfeature.search_context: astringfeature.
aliases: alistofstringfeatures.normalized_aliases: alistofstringfeatures.matched_wiki_entity_name: astringfeature.normalized_matched_wiki_entity_name: astringfeature.normalized_value: astringfeature.type: astringfeature.value: astringfeature.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| rc | 138384 | 18669 | 17210 |
| rc.nocontext | 138384 | 18669 | 17210 |
| unfiltered | 87622 | 11313 | 10832 |
| unfiltered.nocontext | 87622 | 11313 | 10832 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@article{2017arXivtriviaqa,
author = {{Joshi}, Mandar and {Choi}, Eunsol and {Weld},
Daniel and {Zettlemoyer}, Luke},
title = "{triviaqa: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension}",
journal = {arXiv e-prints},
year = 2017,
eid = {arXiv:1705.03551},
pages = {arXiv:1705.03551},
archivePrefix = {arXiv},
eprint = {1705.03551},
}
Contributions
Thanks to @thomwolf, @patrickvonplaten, @lewtun for adding this dataset.