Task Categories: question-answering
Languages: en
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: crowdsourced
Annotations Creators: no-annotation
Source Datasets: original
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Dataset Card for Natural Questions

Dataset Summary

The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets.

Supported Tasks and Leaderboards

More Information Needed


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Dataset Structure

Data Instances


  • Size of downloaded dataset files: 42981.34 MB
  • Size of the generated dataset: 95175.86 MB
  • Total amount of disk used: 138157.19 MB

An example of 'train' looks as follows.

Data Fields

The data fields are the same among all splits.


  • id: a string feature.
  • title: a string feature.
  • url: a string feature.
  • html: a string feature.
  • tokens: a dictionary feature containing:
    • token: a string feature.
    • is_html: a bool feature.
  • text: a string feature.
  • tokens: a list of string features.
  • annotations: a dictionary feature containing:
    • id: a string feature.
    • start_token: a int64 feature.
    • end_token: a int64 feature.
    • start_byte: a int64 feature.
    • end_byte: a int64 feature.
    • short_answers: a dictionary feature containing:
      • start_token: a int64 feature.
      • end_token: a int64 feature.
      • start_byte: a int64 feature.
      • end_byte: a int64 feature.
      • text: a string feature.
    • yes_no_answer: a classification label, with possible values including NO (0), YES (1).

Data Splits

name train validation
default 307373 7830
dev N/A 7830

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

More Information Needed

Licensing Information

Creative Commons Attribution-ShareAlike 3.0 Unported.

Citation Information

title    = {Natural Questions: a Benchmark for Question Answering Research},
author    = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov},
year    = {2019},
journal    = {Transactions of the Association of Computational Linguistics}


Thanks to @thomwolf, @lhoestq for adding this dataset.

Update on GitHub
Papers with Code

Models trained or fine-tuned on natural_questions