Languages: English
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: crowdsourced
Annotations Creators: expert-generated
Source Datasets: original
License: mit
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The dataset preview is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    IndexError
Message:      list index out of range
Traceback:    Traceback (most recent call last):
                File "/src/workers/datasets_based/src/datasets_based/workers/", line 485, in compute_first_rows_response
                  rows = get_rows(
                File "/src/workers/datasets_based/src/datasets_based/workers/", line 120, in decorator
                  return func(*args, **kwargs)
                File "/src/workers/datasets_based/src/datasets_based/workers/", line 164, in get_rows
                  ds = load_dataset(
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/", line 1751, in load_dataset
                  return builder_instance.as_streaming_dataset(split=split)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/", line 1206, in as_streaming_dataset
                  splits_generators = { sg for sg in self._split_generators(dl_manager)}
                File "/tmp/modules-cache/datasets_modules/datasets/docred/d4f4edf2eb07bc25014d2fb7c9ac0292b65a34ab9195a8e1a738e63e3153a751/", line 78, in _split_generators
                  downloads[key] = dl_manager.download_and_extract(_URLS[key])
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/download/", line 1074, in download_and_extract
                  return self.extract(
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/download/", line 1026, in extract
                  urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/utils/", line 436, in map_nested
                  return function(data_struct)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/download/", line 1031, in _extract
                  protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/download/", line 433, in _get_extraction_protocol
                  with, **kwargs) as f:
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/", line 419, in open
                  return open_files(
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/", line 194, in __getitem__
                  out = super().__getitem__(item)
              IndexError: list index out of range

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Dataset Card for DocRED

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

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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

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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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.

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