Languages: English
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: crowdsourced
Annotations Creators: expert-generated
Source Datasets: original
License: mit
docred /
albertvillanova's picture
Reorder split names (#1)
  - expert-generated
  - crowdsourced
  - en
  - mit
  - monolingual
paperswithcode_id: docred
pretty_name: DocRED
  - 100K<n<1M
  - original
  - text-retrieval
  - entity-linking-retrieval
    - name: title
      dtype: string
    - name: sents
        sequence: string
    - name: vertexSet
          - name: name
            dtype: string
          - name: sent_id
            dtype: int32
          - name: pos
            sequence: int32
          - name: type
            dtype: string
    - name: labels
        - name: head
          dtype: int32
        - name: tail
          dtype: int32
        - name: relation_id
          dtype: string
        - name: relation_text
          dtype: string
        - name: evidence
          sequence: int32
    - 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 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


More Information Needed

Dataset Structure

Data Instances


  • Size of downloaded dataset files: 20.03 MB
  • Size of the generated dataset: 19.19 MB
  • Total amount of disk used: 39.23 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.


  • 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

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed


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

More Information Needed

Citation Information

  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},


Thanks to @ghomasHudson, @thomwolf, @lhoestq for adding this dataset.