Datasets:
trivia_qa

Task Categories: question-answering
Languages: en
Multilinguality: monolingual
Licenses: unknown
Language Creators: machine-generated
Annotations Creators: crowdsourced
Source Datasets: original
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Dataset Card for "trivia_qa"

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

More Information Needed

Languages

English.

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: a string feature.
  • question_id: a string feature.
  • question_source: a string feature.
  • entity_pages: a dictionary feature containing:
    • doc_source: a string feature.
    • filename: a string feature.
    • title: a string feature.
    • wiki_context: a string feature.
  • search_results: a dictionary feature containing:
    • description: a string feature.
    • filename: a string feature.
    • rank: a int32 feature.
    • title: a string feature.
    • url: a string feature.
    • search_context: a string feature.
  • aliases: a list of string features.
  • normalized_aliases: a list of string features.
  • matched_wiki_entity_name: a string feature.
  • normalized_matched_wiki_entity_name: a string feature.
  • normalized_value: a string feature.
  • type: a string feature.
  • value: a string feature.

rc.nocontext

  • question: a string feature.
  • question_id: a string feature.
  • question_source: a string feature.
  • entity_pages: a dictionary feature containing:
    • doc_source: a string feature.
    • filename: a string feature.
    • title: a string feature.
    • wiki_context: a string feature.
  • search_results: a dictionary feature containing:
    • description: a string feature.
    • filename: a string feature.
    • rank: a int32 feature.
    • title: a string feature.
    • url: a string feature.
    • search_context: a string feature.
  • aliases: a list of string features.
  • normalized_aliases: a list of string features.
  • matched_wiki_entity_name: a string feature.
  • normalized_matched_wiki_entity_name: a string feature.
  • normalized_value: a string feature.
  • type: a string feature.
  • value: a string feature.

unfiltered

  • question: a string feature.
  • question_id: a string feature.
  • question_source: a string feature.
  • entity_pages: a dictionary feature containing:
    • doc_source: a string feature.
    • filename: a string feature.
    • title: a string feature.
    • wiki_context: a string feature.
  • search_results: a dictionary feature containing:
    • description: a string feature.
    • filename: a string feature.
    • rank: a int32 feature.
    • title: a string feature.
    • url: a string feature.
    • search_context: a string feature.
  • aliases: a list of string features.
  • normalized_aliases: a list of string features.
  • matched_wiki_entity_name: a string feature.
  • normalized_matched_wiki_entity_name: a string feature.
  • normalized_value: a string feature.
  • type: a string feature.
  • value: a string feature.

unfiltered.nocontext

  • question: a string feature.
  • question_id: a string feature.
  • question_source: a string feature.
  • entity_pages: a dictionary feature containing:
    • doc_source: a string feature.
    • filename: a string feature.
    • title: a string feature.
    • wiki_context: a string feature.
  • search_results: a dictionary feature containing:
    • description: a string feature.
    • filename: a string feature.
    • rank: a int32 feature.
    • title: a string feature.
    • url: a string feature.
    • search_context: a string feature.
  • aliases: a list of string features.
  • normalized_aliases: a list of string features.
  • matched_wiki_entity_name: a string feature.
  • normalized_matched_wiki_entity_name: a string feature.
  • normalized_value: a string feature.
  • type: a string feature.
  • value: a string feature.

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

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

The University of Washington does not own the copyright of the questions and documents included in TriviaQA.

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.

Models trained or fine-tuned on trivia_qa