natural_questions
收藏魔搭社区2026-05-20 更新2025-07-12 收录
下载链接：
https://modelscope.cn/datasets/google-research-datasets/natural_questions
下载链接
链接失效反馈官方服务：
资源简介：
# Dataset Card for Natural Questions
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset)
- **Repository:** [https://github.com/google-research-datasets/natural-questions](https://github.com/google-research-datasets/natural-questions)
- **Paper:** [https://research.google/pubs/pub47761/](https://research.google/pubs/pub47761/)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 45.07 GB
- **Size of the generated dataset:** 99.80 GB
- **Total amount of disk used:** 144.87 GB
### 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](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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[Creative Commons Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/).
### 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](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
# 自然问题（Natural Questions）数据集卡片
## 目录
- [数据集描述](#dataset-description)
- [数据集概述](#dataset-summary)
- [支持任务与排行榜](#supported-tasks-and-leaderboards)
- [语言](#languages)
- [数据集结构](#dataset-structure)
- [数据实例](#data-instances)
- [数据字段](#data-fields)
- [数据拆分](#data-splits)
- [数据集构建](#dataset-creation)
- [构建初衷](#curation-rationale)
- [源数据](#source-data)
- [标注信息](#annotations)
- [个人与敏感信息](#personal-and-sensitive-information)
- [数据集使用注意事项](#considerations-for-using-the-data)
- [数据集的社会影响](#social-impact-of-dataset)
- [偏差讨论](#discussion-of-biases)
- [其他已知局限性](#other-known-limitations)
- [附加信息](#additional-information)
- [数据集维护者](#dataset-curators)
- [授权信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献声明](#contributions)
## 数据集描述
- **主页：** [https://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset)
- **代码仓库：** [https://github.com/google-research-datasets/natural-questions](https://github.com/google-research-datasets/natural-questions)
- **相关论文：** [https://research.google/pubs/pub47761/](https://research.google/pubs/pub47761/)
- **联系方式：** [更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **下载数据集文件大小：** 45.07 GB
- **生成后数据集大小：** 99.80 GB
- **总磁盘占用量：** 144.87 GB
### 数据集概述
自然问题（Natural Questions，NQ）语料库收录了真实用户提出的问题，要求问答（Question Answering，QA）系统阅读并理解一整篇维基百科文章——该文章可能包含也可能不包含问题的答案。由于采用真实用户提问，且要求模型需通读整页内容以寻找答案，相较于此前的问答数据集，自然问题任务更贴合现实场景且更具挑战性。
### 支持任务与排行榜
[https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions)
### 语言
英语
## 数据集结构
### 数据实例
- **下载数据集文件大小：** 45.07 GB
- **生成后数据集大小：** 99.80 GB
- **总磁盘占用量：** 144.87 GB
训练集（train）的示例如下，此为简化样例：
{
"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
}]
}
### 数据字段
所有数据拆分下的字段格式均保持一致。
#### 默认（default）
- `id`: 字符串（string）类型特征。
- `document`: 字典类型特征，包含以下子字段：
- `title`: 字符串类型特征。
- `url`: 字符串类型特征。
- `html`: 字符串类型特征。
- `tokens`: 字典类型特征，包含：
- `token`: 字符串类型特征。
- `is_html`: 布尔类型特征。
- `start_byte`: 64位整数（int64）类型特征。
- `end_byte`: 64位整数类型特征。
- `question`: 字典类型特征，包含以下子字段：
- `text`: 字符串类型特征。
- `tokens`: 字符串列表类型特征。
- `long_answer_candidates`: 字典类型特征，包含以下子字段：
- `start_token`: 64位整数类型特征。
- `end_token`: 64位整数类型特征。
- `start_byte`: 64位整数类型特征。
- `end_byte`: 64位整数类型特征。
- `top_level`: 布尔类型特征。
- `annotations`: 字典类型特征，包含以下子字段：
- `id`: 字符串类型特征。
- `long_answers`: 字典类型特征，包含：
- `start_token`: 64位整数类型特征。
- `end_token`: 64位整数类型特征。
- `start_byte`: 64位整数类型特征。
- `end_byte`: 64位整数类型特征。
- `candidate_index`: 64位整数类型特征。
- `short_answers`: 字典类型特征，包含：
- `start_token`: 64位整数类型特征。
- `end_token`: 64位整数类型特征。
- `start_byte`: 64位整数类型特征。
- `end_byte`: 64位整数类型特征。
- `text`: 字符串类型特征。
- `yes_no_answer`: 分类标签，可选值包括`NO`（0）、`YES`（1）。
### 数据拆分
| 拆分名称 | 训练集 | 验证集 |
|---------|-------:|-----------:|
| 默认 | 307373 | 7830 |
| 开发集 | 无 | 7830 |
## 数据集构建
### 构建初衷
[更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 源数据
#### 初始数据收集与标准化
[更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### 源文本生产者是谁？
[更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 标注信息
#### 标注流程
[更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### 标注人员是谁？
[更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 个人与敏感信息
[更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## 数据集使用注意事项
### 数据集的社会影响
[更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 偏差讨论
[更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 其他已知局限性
[更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## 附加信息
### 数据集维护者
[更多信息请参见](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 授权信息
知识共享署名-相同方式共享3.0未移植版本（Creative Commons Attribution-ShareAlike 3.0 Unported）。
### 引用信息
@article{47761,
title = {NaturalQuestions: 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}
}
### 贡献声明
感谢 [@thomwolf](https://github.com/thomwolf)、[@lhoestq](https://github.com/lhoestq) 为本数据集的收录提供支持。
提供机构：
maas
创建时间：
2025-07-07
