Datasets:
docred

	"""DocRED: A Large-Scale Document-Level Relation Extraction Dataset"""


	import json

	import datasets


	_CITATION = """\
	@inproceedings{yao-etal-2019-docred,
	title = "{D}oc{RED}: 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 the 57th Annual Meeting of the Association for Computational Linguistics",
	month = jul,
	year = "2019",
	address = "Florence, Italy",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/P19-1074",
	doi = "10.18653/v1/P19-1074",
	pages = "764--777",
	}
	"""

	_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:
	- 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.
	"""

	_URLS = {
	"dev": "data/dev.json.gz",
	"train_distant": "data/train_distant.json.gz",
	"train_annotated": "data/train_annotated.json.gz",
	"test": "data/test.json.gz",
	"rel_info": "data/rel_info.json.gz",
	}


	class DocRed(datasets.GeneratorBasedBuilder):
	"""DocRED: A Large-Scale Document-Level Relation Extraction Dataset"""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"title": datasets.Value("string"),
	"sents": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
	"vertexSet": [
	[
	{
	"name": datasets.Value("string"),
	"sent_id": datasets.Value("int32"),
	"pos": datasets.features.Sequence(datasets.Value("int32")),
	"type": datasets.Value("string"),
	}
	]
	],
	"labels": datasets.features.Sequence(
	{
	"head": datasets.Value("int32"),
	"tail": datasets.Value("int32"),
	"relation_id": datasets.Value("string"),
	"relation_text": datasets.Value("string"),
	"evidence": datasets.features.Sequence(datasets.Value("int32")),
	}
	),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/thunlp/DocRED",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	downloads = dl_manager.download_and_extract(_URLS)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": downloads["dev"], "rel_info": downloads["rel_info"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"], "rel_info": downloads["rel_info"]}
	),
	datasets.SplitGenerator(
	name="train_annotated",
	gen_kwargs={"filepath": downloads["train_annotated"], "rel_info": downloads["rel_info"]},
	),
	datasets.SplitGenerator(
	name="train_distant",
	gen_kwargs={"filepath": downloads["train_distant"], "rel_info": downloads["rel_info"]},
	),
	]

	def _generate_examples(self, filepath, rel_info):
	"""Generate DocRED examples."""

	with open(rel_info, encoding="utf-8") as f:
	relation_name_map = json.load(f)
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)

	for idx, example in enumerate(data):

	# Test set has no labels - Results need to be uploaded to Codalab
	if "labels" not in example.keys():
	example["labels"] = []

	for label in example["labels"]:
	# Rename and include full relation names
	label["relation_text"] = relation_name_map[label["r"]]
	label["relation_id"] = label["r"]
	label["head"] = label["h"]
	label["tail"] = label["t"]
	del label["r"]
	del label["h"]
	del label["t"]

	yield idx, example