
Datasets:
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
License:
cc-by-sa-3.0
Dataset Preview
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Error code: SplitsNamesError Exception: SplitsNotFoundError Message: The split names could not be parsed from the dataset config. Traceback: Traceback (most recent call last): File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 388, in get_dataset_config_info for split_generator in builder._split_generators( TypeError: _split_generators() missing 1 required positional argument: 'pipeline' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 437, in compute_first_rows_response split_full_names = get_dataset_split_full_names(dataset=dataset, use_auth_token=use_auth_token) File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 364, in get_dataset_split_full_names return [ File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 367, in <listcomp> for split in get_dataset_split_names(path=dataset, config_name=config, use_auth_token=use_auth_token) File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 442, in get_dataset_split_names info = get_dataset_config_info( File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 393, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.
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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
https://ai.google.com/research/NaturalQuestions
Languages
en
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. 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
: astring
feature.document
a dictionary feature containing:title
: astring
feature.url
: astring
feature.html
: astring
feature.tokens
: a dictionary feature containing:token
: astring
feature.is_html
: abool
feature.start_byte
: aint64
feature.end_byte
: aint64
feature.
question
: a dictionary feature containing:text
: astring
feature.tokens
: alist
ofstring
features.
long_answer_candidates
: a dictionary feature containing:start_token
: aint64
feature.end_token
: aint64
feature.start_byte
: aint64
feature.end_byte
: aint64
feature.top_level
: abool
feature.
annotations
: a dictionary feature containing:id
: astring
feature.long_answers
: a dictionary feature containing:start_token
: aint64
feature.end_token
: aint64
feature.start_byte
: aint64
feature.end_byte
: aint64
feature.candidate_index
: aint64
feature.
short_answers
: a dictionary feature containing:start_token
: aint64
feature.end_token
: aint64
feature.start_byte
: aint64
feature.end_byte
: aint64
feature.text
: astring
feature.
yes_no_answer
: a classification label, with possible values includingNO
(0),YES
(1).
Data Splits
name | train | validation |
---|---|---|
default | 307373 | 7830 |
dev | N/A | 7830 |
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
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
- Downloads last month
- 922
Homepage:
ai.google.com
Repository:
github.com
Paper:
research.google
Size of downloaded dataset files:
42981.34 MB
Size of the generated dataset:
95175.86 MB
Total amount of disk used:
138157.19 MB
Models trained or fine-tuned on natural_questions

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