
Datasets:
The dataset preview is not available for this split.
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
Languages
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
: astring
feature.sents
: a dictionary feature containing:feature
: astring
feature.
name
: astring
feature.sent_id
: aint32
feature.pos
: alist
ofint32
features.type
: astring
feature.labels
: a dictionary feature containing:head
: aint32
feature.tail
: aint32
feature.relation_id
: astring
feature.relation_text
: astring
feature.evidence
: alist
ofint32
features.
Data Splits
name | train_annotated | train_distant | validation | test |
---|---|---|---|---|
default | 3053 | 101873 | 998 | 1000 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
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, @thomwolf, @lhoestq for adding this dataset.
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