Datasets:
docred

Languages: English
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: crowdsourced
Annotations Creators: expert-generated
Source Datasets: original
License: mit
system
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Update files from the datasets library (from 1.0.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

Files changed (4) hide show
  1. .gitattributes +27 -0
  2. dataset_infos.json +1 -0
  3. docred.py +123 -0
  4. dummy/0.0.0/dummy_data.zip +3 -0
.gitattributes ADDED
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
dataset_infos.json ADDED
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+ {"default": {"description": "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:\n - DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.\n - DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.\n - 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.\n", "citation": "@inproceedings{yao2019DocRED,\n title={{DocRED}: A Large-Scale Document-Level Relation Extraction Dataset},\n 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},\n booktitle={Proceedings of ACL 2019},\n year={2019}\n}\n", "homepage": "https://github.com/thunlp/DocRED", "license": "", "features": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "sents": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "vertexSet": [[{"name": {"dtype": "string", "id": null, "_type": "Value"}, "sent_id": {"dtype": "int32", "id": null, "_type": "Value"}, "pos": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "type": {"dtype": "string", "id": null, "_type": "Value"}}]], "labels": {"feature": {"head": {"dtype": "int32", "id": null, "_type": "Value"}, "tail": {"dtype": "int32", "id": null, "_type": "Value"}, "relation_id": {"dtype": "string", "id": null, "_type": "Value"}, "relation_text": {"dtype": "string", "id": null, "_type": "Value"}, "evidence": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "doc_red", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 3435087, "num_examples": 1000, "dataset_name": "doc_red"}, "test": {"name": "test", "num_bytes": 2843877, "num_examples": 1000, "dataset_name": "doc_red"}, "train_annotated": {"name": "train_annotated", "num_bytes": 10413156, "num_examples": 3053, "dataset_name": "doc_red"}, "train_distant": {"name": "train_distant", "num_bytes": 3435087, "num_examples": 1000, "dataset_name": "doc_red"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7": {"num_bytes": 4299810, "checksum": "85691c5ca1df0048bffab1c1cf53d7d35b5de40f3de0a2c563c03da28746d5cb"}, "https://drive.google.com/uc?export=download&id=1NN33RzyETbanw4Dg2sRrhckhWpzuBQS9": {"num_bytes": 13029595, "checksum": "7e706348a02cf91f38bd8c379f934ab61aedadc901fca10d962c1d82ab78e95b"}, "https://drive.google.com/uc?export=download&id=1lAVDcD94Sigx7gR3jTfStI66o86cflum": {"num_bytes": 3674242, "checksum": "09386b5cb58249d8e087863c379ebd64557169c52ee502193d2f4f215e704ae8"}, "https://drive.google.com/uc?id=1y9A0zKrvETc1ddUFuFhBg3Xfr7FEL4dW&export=download": {"num_bytes": 2452, "checksum": "5ecf4e5e55c179fc83a3a3d19baa01efffecb26ba5edc0b4ac5a54ddf61fe3de"}}, "download_size": 21006099, "dataset_size": 20127207, "size_in_bytes": 41133306}}
docred.py ADDED
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+ """DocRED: A Large-Scale Document-Level Relation Extraction Dataset"""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import json
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+ import os
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+
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+ import datasets
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+
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+
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+ _CITATION = """\
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+ @inproceedings{yao2019DocRED,
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+ title={{DocRED}: A Large-Scale Document-Level Relation Extraction Dataset},
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+ author={Yao, Yuan and Ye, Deming and Li, Peng and Han, Xu and Lin, Yankai and Liu, Zhenghao and Liu, \
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+ Zhiyuan and Huang, Lixin and Zhou, Jie and Sun, Maosong},
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+ booktitle={Proceedings of ACL 2019},
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+ year={2019}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by \
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+ existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single \
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+ entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed \
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+ from Wikipedia and Wikidata with three features:
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+ - DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.
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+ - DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.
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+ - 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.
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+ """
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+
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+ _URLS = {
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+ "dev": "https://drive.google.com/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7",
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+ "train_distant": "https://drive.google.com/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7",
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+ "train_annotated": "https://drive.google.com/uc?export=download&id=1NN33RzyETbanw4Dg2sRrhckhWpzuBQS9",
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+ "test": "https://drive.google.com/uc?export=download&id=1lAVDcD94Sigx7gR3jTfStI66o86cflum",
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+ "rel_info": "https://drive.google.com/uc?id=1y9A0zKrvETc1ddUFuFhBg3Xfr7FEL4dW&export=download",
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+ }
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+
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+
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+ class DocRed(datasets.GeneratorBasedBuilder):
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+ """DocRED: A Large-Scale Document-Level Relation Extraction Dataset"""
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "title": datasets.Value("string"),
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+ "sents": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
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+ "vertexSet": [
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+ [
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+ {
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+ "name": datasets.Value("string"),
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+ "sent_id": datasets.Value("int32"),
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+ "pos": datasets.features.Sequence(datasets.Value("int32")),
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+ "type": datasets.Value("string"),
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+ }
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+ ]
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+ ],
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+ "labels": datasets.features.Sequence(
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+ {
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+ "head": datasets.Value("int32"),
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+ "tail": datasets.Value("int32"),
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+ "relation_id": datasets.Value("string"),
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+ "relation_text": datasets.Value("string"),
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+ "evidence": datasets.features.Sequence(datasets.Value("int32")),
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+ }
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+ ),
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+ }
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+ ),
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+ supervised_keys=None,
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+ homepage="https://github.com/thunlp/DocRED",
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ downloads = {}
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+ for key in _URLS.keys():
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+ downloads[key] = dl_manager.download_and_extract(_URLS[key])
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+ # Fix for dummy data
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+ if os.path.isdir(downloads[key]):
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+ downloads[key] = os.path.join(downloads[key], key + ".json")
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+
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ gen_kwargs={"filepath": downloads["dev"], "rel_info": downloads["rel_info"]},
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"], "rel_info": downloads["rel_info"]}
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+ ),
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+ datasets.SplitGenerator(
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+ name="train_annotated",
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+ gen_kwargs={"filepath": downloads["train_annotated"], "rel_info": downloads["rel_info"]},
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+ ),
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+ datasets.SplitGenerator(
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+ name="train_distant",
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+ gen_kwargs={"filepath": downloads["train_distant"], "rel_info": downloads["rel_info"]},
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+ ),
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+ ]
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+
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+ def _generate_examples(self, filepath, rel_info):
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+ """Generate DocRED examples."""
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+ relation_name_map = json.load(open(rel_info))
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+ data = json.load(open(filepath))
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+
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+ for idx, example in enumerate(data):
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+
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+ # Test set has no labels - Results need to be uploaded to Codalab
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+ if "labels" not in example.keys():
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+ example["labels"] = []
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+
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+ for label in example["labels"]:
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+ # Rename and include full relation names
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+ label["relation_text"] = relation_name_map[label["r"]]
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+ label["relation_id"] = label["r"]
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+ label["head"] = label["h"]
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+ label["tail"] = label["t"]
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+ del label["r"]
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+ del label["h"]
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+ del label["t"]
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+
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+ yield idx, example
dummy/0.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:ad5f87bbf1d936f40558f6ec5949e2e3a58c5c902c585082cb17519cc0005006
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+ size 1888