Datasets:

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{yao2019DocRED,
	title={{DocRED}: 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 ACL 2019},
	year={2019}
	}

	```


	### Contributions

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