NaturalQuestionsV2 / README.md
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metadata
annotations_creators:
  - no-annotation
language_creators:
  - crowdsourced
language:
  - en
license:
  - cc-by-sa-3.0
multilinguality:
  - monolingual
pretty_name: Natural Questions
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - open-domain-qa
paperswithcode_id: natural-questions

Dataset Card for Natural Questions

Table of Contents

Dataset Description

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

Languages

More Information Needed

Dataset Structure

Data Instances

default

  • Size of downloaded dataset files: 42981 MB
  • Size of the generated dataset: 139706 MB
  • Total amount of disk used: 182687 MB

An example of 'train' looks as follows.


Data Fields

The data fields are the same among all splits.

default

"id": datasets.Value("string"),
"document": {
    "title": datasets.Value("string"),
    "url": datasets.Value("string"),
    "html": datasets.Value("string"),
    "tokens": datasets.features.Sequence(
        {
            "token": datasets.Value("string"),
        "is_html": datasets.Value("bool"),  
            "start_byte": datasets.Value("int64"),
        "end_byte": datasets.Value("int64"),
    }
    ),
},
"question": {
    "text": datasets.Value("string"),
    "tokens": datasets.features.Sequence(datasets.Value("string")),
},
"long_answer_candidates": datasets.features.Sequence(
    {
        "start_token": datasets.Value("int64"),
        "end_token": datasets.Value("int64"),
        "start_byte": datasets.Value("int64"),
        "end_byte": datasets.Value("int64"),
        "top_level": datasets.Value("bool"),
    }
),
"annotations": datasets.features.Sequence(
    {
        "id": datasets.Value("string"),
        "long_answer": {
            "start_token": datasets.Value("int64"),
            "end_token": datasets.Value("int64"),
            "start_byte": datasets.Value("int64"),
            "end_byte": datasets.Value("int64"),
            "candidate_index": datasets.Value("int64")
        },
        "short_answers": datasets.features.Sequence(
            {
                "start_token": datasets.Value("int64"),
                "end_token": datasets.Value("int64"),
                "start_byte": datasets.Value("int64"),
                "end_byte": datasets.Value("int64"),
                "text": datasets.Value("string"),
            }
        ),
        "yes_no_answer": datasets.features.ClassLabel(
            names=["NO", "YES"]
        ),  # Can also be -1 for NONE.
    }
)

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