thunlp/docred

---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: docred
pretty_name: DocRED
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
dataset_info:
features:
- name: title
dtype: string
- name: sents
sequence:
sequence: string
- name: vertexSet
list:
list:
- name: name
dtype: string
- name: sent_id
dtype: int32
- name: pos
sequence: int32
- name: type
dtype: string
- name: labels
sequence:
- name: head
dtype: int32
- name: tail
dtype: int32
- name: relation_id
dtype: string
- name: relation_text
dtype: string
- name: evidence
sequence: int32
splits:
- name: validation
num_bytes: 3425030
num_examples: 998
- name: test
num_bytes: 2843877
num_examples: 1000
- name: train_annotated
num_bytes: 10413156
num_examples: 3053
- name: train_distant
num_bytes: 346001876
num_examples: 101873
download_size: 458040413
dataset_size: 362683939
---

# Dataset Card for DocRED

## 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

- **Repository:** [https://github.com/thunlp/DocRED](https://github.com/thunlp/DocRED)
- **Paper:** [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://arxiv.org/abs/1906.06127)
- **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:** 21.00 MB
- **Size of the generated dataset:** 20.12 MB
- **Total amount of disk used:** 41.14 MB

### Dataset Summary

Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features:
- DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.
- DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.
- Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.

### Supported Tasks and Leaderboards

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Dataset Structure

### Data Instances

#### default

- **Size of downloaded dataset files:** 21.00 MB
- **Size of the generated dataset:** 20.12 MB
- **Total amount of disk used:** 41.14 MB

An example of 'train_annotated' looks as follows.
```
{
"labels": {
"evidence": [[0]],
"head": [0],
"relation_id": ["P1"],
"relation_text": ["is_a"],
"tail": [0]
},
"sents": [["This", "is", "a", "sentence"], ["This", "is", "another", "sentence"]],
"title": "Title of the document",
"vertexSet": [[{
"name": "sentence",
"pos": [3],
"sent_id": 0,
"type": "NN"
}, {
"name": "sentence",
"pos": [3],
"sent_id": 1,
"type": "NN"
}], [{
"name": "This",
"pos": [0],
"sent_id": 0,
"type": "NN"
}]]
}
```

### Data Fields

The data fields are the same among all splits.

#### default
- `title`: a `string` feature.
- `sents`: a dictionary feature containing:
- `feature`: a `string` feature.
- `name`: a `string` feature.
- `sent_id`: a `int32` feature.
- `pos`: a `list` of `int32` features.
- `type`: a `string` feature.
- `labels`: a dictionary feature containing:
- `head`: a `int32` feature.
- `tail`: a `int32` feature.
- `relation_id`: a `string` feature.
- `relation_text`: a `string` feature.
- `evidence`: a `list` of `int32` features.

### Data Splits

| name |train_annotated|train_distant|validation|test|
|-------|--------------:|------------:|---------:|---:|
|default| 3053| 101873| 998|1000|

## 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

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Citation Information

```
@inproceedings{yao-etal-2019-docred,
title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset",
author = "Yao, Yuan and
Ye, Deming and
Li, Peng and
Han, Xu and
Lin, Yankai and
Liu, Zhenghao and
Liu, Zhiyuan and
Huang, Lixin and
Zhou, Jie and
Sun, Maosong",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1074",
doi = "10.18653/v1/P19-1074",
pages = "764--777",
}
```

### Contributions

Thanks to [@ghomasHudson](https://github.com/ghomasHudson), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

原始信息汇总

数据集概述

数据集名称： DocRED

数据集概要： DocRED是一个从Wikipedia和Wikidata构建的大型文档级关系抽取数据集。它具有以下特点：

• 同时标注了命名实体和关系，是最大的文档级关系抽取数据集。
• 需要阅读多个文档中的句子来提取实体并推断它们的关系。
• 提供了大规模的远监督数据，适用于监督和弱监督场景。

语言： 英语（en）

许可证： MIT

多语言性： 单语

大小类别： 100K<n<1M

源数据集： 原始数据

任务类别： 文本检索

任务ID： 实体链接检索

数据集结构

数据实例

数据集包含以下字段：

• title: 字符串类型
• sents: 字符串序列
• vertexSet: 列表类型，包含以下子字段：
  • name: 字符串类型
  • sent_id: 整数类型
  • pos: 整数序列
  • type: 字符串类型
• labels: 字典类型，包含以下子字段：
  • head: 整数类型
  • tail: 整数类型
  • relation_id: 字符串类型
  • relation_text: 字符串类型
  • evidence: 整数序列

数据分割

名称	训练集（已标注）	训练集（远监督）	验证集	测试集
示例数量	3053	101873	998	1000

数据集创建

标注创建者： 专家生成

语言创建者： 众包

数据集信息：

• 下载大小：458040413字节
• 数据集大小：362683939字节
• 训练集（已标注）大小：10413156字节
• 训练集（远监督）大小：346001876字节
• 验证集大小：3425030字节
• 测试集大小：2843877字节