Datasets:
docred

Tasks:

Text Retrieval

Sub-tasks: entity-linking-retrieval

Languages: English

Multilinguality: monolingual

Size Categories: 100K<n<1M

Language Creators: crowdsourced

Annotations Creators: expert-generated

Source Datasets: original

ArXiv:

License: mit

Dataset card Files Files and versions Community

Dataset Preview

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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/first_rows.py", line 485, in compute_first_rows_response
                  rows = get_rows(
                File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 120, in decorator
                  return func(*args, **kwargs)
                File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 164, in get_rows
                  ds = load_dataset(
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/load.py", 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/builder.py", line 1206, in as_streaming_dataset
                  splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
                File "/tmp/modules-cache/datasets_modules/datasets/docred/d4f4edf2eb07bc25014d2fb7c9ac0292b65a34ab9195a8e1a738e63e3153a751/docred.py", 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/streaming_download_manager.py", line 1074, in download_and_extract
                  return self.extract(self.download(url_or_urls))
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", 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/py_utils.py", line 436, in map_nested
                  return function(data_struct)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", 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/streaming_download_manager.py", line 433, in _get_extraction_protocol
                  with fsspec.open(urlpath, **kwargs) as f:
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/core.py", line 419, in open
                  return open_files(
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/core.py", 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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Languages

More Information Needed

Dataset Structure

Data Instances

default

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.

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

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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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Annotations

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

@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}
}