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Datasets:
natural_questions

Tasks:
Question Answering
Sub-tasks: open-domain-qa
Languages: English
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: crowdsourced
Annotations Creators: no-annotation
Source Datasets: original
Tags:
License:
Dataset card Viewer Files Community
8
Dataset Viewer (First 5GB)
Auto-converted to Parquet
Viewer
id
string
document
dict
question
dict
long_answer_candidates
sequence
annotations
sequence
4549465242785278785
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{"text":"when is the last episode of season 8 of the walking dead","tokens":["when","is","the","last(...TRUNCATED)
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{"html":"<!DOCTYPE html>\n<HTML class=\"client-js ve-not-available\" lang=\"en\" dir=\"ltr\"><HEAD>\(...TRUNCATED)
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{"text":"benefits of colonial life for single celled organisms","tokens":["benefits","of","colonial"(...TRUNCATED)
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{"text":"how many season of the man in the high castle","tokens":["how","many","season","of","the","(...TRUNCATED)
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{"html":"<!DOCTYPE html>\n<HTML class=\"client-js ve-not-available\" lang=\"en\" dir=\"ltr\"><HEAD>\(...TRUNCATED)
{"text":"who was the first ministry head of state in nigeria","tokens":["who","was","the","first","m(...TRUNCATED)
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{"text":"how many nominations does game of thrones have","tokens":["how","many","nominations","does"(...TRUNCATED)
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{"text":"what is the name of the most important jewish text","tokens":["what","is","the","name","of"(...TRUNCATED)
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{"html":"<!DOCTYPE html>\n<HTML class=\"client-js ve-not-available\" lang=\"en\" dir=\"ltr\"><HEAD>\(...TRUNCATED)
{"text":"until we meet again filipino soap episode 10","tokens":["until","we","meet","again","filipi(...TRUNCATED)
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{"html":"<!DOCTYPE html>\n<HTML class=\"client-js ve-not-available\" lang=\"en\" dir=\"ltr\"><HEAD>\(...TRUNCATED)
{"text":"what is the name of spain's most famous soccer team","tokens":["what","is","the","name","of(...TRUNCATED)
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{"html":"<!DOCTYPE html>\n<HTML class=\"client-js ve-not-available\" lang=\"en\" dir=\"ltr\"><HEAD>\(...TRUNCATED)
{"text":"when was the first robot used in surgery","tokens":["when","was","the","first","robot","use(...TRUNCATED)
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End of preview. Expand in Dataset Viewer.

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

https://ai.google.com/research/NaturalQuestions

Languages

en

Dataset Structure

Data Instances

  • Size of downloaded dataset files: 45.07 GB
  • Size of the generated dataset: 99.80 GB
  • Total amount of disk used: 144.87 GB

An example of 'train' looks as follows. This is a toy example.

{
  "id": "797803103760793766",
  "document": {
    "title": "Google",
    "url": "http://www.wikipedia.org/Google",
    "html": "<html><body><h1>Google Inc.</h1><p>Google was founded in 1998 By:<ul><li>Larry</li><li>Sergey</li></ul></p></body></html>",
    "tokens":[
      {"token": "<h1>", "start_byte": 12, "end_byte": 16, "is_html": True},
      {"token": "Google", "start_byte": 16, "end_byte": 22, "is_html": False},
      {"token": "inc", "start_byte": 23, "end_byte": 26, "is_html": False},
      {"token": ".", "start_byte": 26, "end_byte": 27, "is_html": False},
      {"token": "</h1>", "start_byte": 27, "end_byte": 32, "is_html": True},
      {"token": "<p>", "start_byte": 32, "end_byte": 35, "is_html": True},
      {"token": "Google", "start_byte": 35, "end_byte": 41, "is_html": False},
      {"token": "was", "start_byte": 42, "end_byte": 45, "is_html": False},
      {"token": "founded", "start_byte": 46, "end_byte": 53, "is_html": False},
      {"token": "in", "start_byte": 54, "end_byte": 56, "is_html": False},
      {"token": "1998", "start_byte": 57, "end_byte": 61, "is_html": False},
      {"token": "by", "start_byte": 62, "end_byte": 64, "is_html": False},
      {"token": ":", "start_byte": 64, "end_byte": 65, "is_html": False},
      {"token": "<ul>", "start_byte": 65, "end_byte": 69, "is_html": True},
      {"token": "<li>", "start_byte": 69, "end_byte": 73, "is_html": True},
      {"token": "Larry", "start_byte": 73, "end_byte": 78, "is_html": False},
      {"token": "</li>", "start_byte": 78, "end_byte": 83, "is_html": True},
      {"token": "<li>", "start_byte": 83, "end_byte": 87, "is_html": True},
      {"token": "Sergey", "start_byte": 87, "end_byte": 92, "is_html": False},
      {"token": "</li>", "start_byte": 92, "end_byte": 97, "is_html": True},
      {"token": "</ul>", "start_byte": 97, "end_byte": 102, "is_html": True},
      {"token": "</p>", "start_byte": 102, "end_byte": 106, "is_html": True}
    ],
  },
  "question" :{
    "text": "who founded google",
    "tokens": ["who", "founded", "google"]
  },
  "long_answer_candidates": [
    {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "top_level": True},
    {"start_byte": 65, "end_byte": 102, "start_token": 13, "end_token": 21, "top_level": False},
    {"start_byte": 69, "end_byte": 83, "start_token": 14, "end_token": 17, "top_level": False},
    {"start_byte": 83, "end_byte": 92, "start_token": 17, "end_token": 20 , "top_level": False}
  ],
  "annotations": [{
    "id": "6782080525527814293",
    "long_answer": {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "candidate_index": 0},
    "short_answers": [
      {"start_byte": 73, "end_byte": 78, "start_token": 15, "end_token": 16, "text": "Larry"},
      {"start_byte": 87, "end_byte": 92, "start_token": 18, "end_token": 19, "text": "Sergey"}
    ],
    "yes_no_answer": -1
  }]
}

Data Fields

The data fields are the same among all splits.

default

  • id: a string feature.
  • document a dictionary feature containing:
    • 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.
      • start_byte: a int64 feature.
      • end_byte: a int64 feature.
  • question: a dictionary feature containing:
    • text: a string feature.
    • tokens: a list of string features.
  • long_answer_candidates: a dictionary feature containing:
    • start_token: a int64 feature.
    • end_token: a int64 feature.
    • start_byte: a int64 feature.
    • end_byte: a int64 feature.
    • top_level: a bool feature.
  • annotations: a dictionary feature containing:
    • id: a string feature.
    • long_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.
      • candidate_index: 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?

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

Creative Commons Attribution-ShareAlike 3.0 Unported.

Citation Information 


@article{47761,
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}
}

Contributions

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

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Homepage:
ai.google.com
Repository:
github.com
Paper:
research.google
Size of downloaded dataset files:
45.07 GB
Size of the auto-converted Parquet files (First 5GB per split):
4.49 GB
Number of rows (First 5GB per split):
26,299

Models trained or fine-tuned on natural_questions

Browse 94 models trained on this dataset