Datasets:

DFKI-SLT
/

few-nerd

	import os
	import json
	import datasets
	from tqdm.autonotebook import tqdm


	_CITATION = """
	@inproceedings{ding2021few,
	title={Few-NERD: A Few-Shot Named Entity Recognition Dataset},
	author={Ding, Ning and Xu, Guangwei and Chen, Yulin, and Wang, Xiaobin and Han, Xu and Xie,
	Pengjun and Zheng, Hai-Tao and Liu, Zhiyuan},
	booktitle={ACL-IJCNLP},
	year={2021}
	}
	"""

	_DESCRIPTION = """
	Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset,
	which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities
	and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the
	other two are few-shot: Few-NERD (INTRA) and Few-NERD (INTER).
	"""

	_LICENSE = "CC BY-SA 4.0"

	# the original data files (zip of .txt) can be downloaded from tsinghua cloud, but we chose to host them on huggingface.co
	# for better reliability and download speed
	_URL = "https://huggingface.co/datasets/DFKI-SLT/few-nerd/resolve/main/data"
	_URLs = {
	"supervised": f"{_URL}/supervised.zip",
	"intra": f"{_URL}/intra.zip",
	"inter": f"{_URL}/inter.zip"
	}

	# the label ids, for coarse(NER_TAGS_DICT) and fine(FINE_NER_TAGS_DICT)
	NER_TAGS_DICT = {
	"O": 0,
	"art": 1,
	"building": 2,
	"event": 3,
	"location": 4,
	"organization": 5,
	"other": 6,
	"person": 7,
	"product": 8,
	}

	FINE_NER_TAGS_DICT = {
	"O": 0,
	"art-broadcastprogram": 1,
	"art-film": 2,
	"art-music": 3,
	"art-other": 4,
	"art-painting": 5,
	"art-writtenart": 6,
	"building-airport": 7,
	"building-hospital": 8,
	"building-hotel": 9,
	"building-library": 10,
	"building-other": 11,
	"building-restaurant": 12,
	"building-sportsfacility": 13,
	"building-theater": 14,
	"event-attack/battle/war/militaryconflict": 15,
	"event-disaster": 16,
	"event-election": 17,
	"event-other": 18,
	"event-protest": 19,
	"event-sportsevent": 20,
	"location-GPE": 21,
	"location-bodiesofwater": 22,
	"location-island": 23,
	"location-mountain": 24,
	"location-other": 25,
	"location-park": 26,
	"location-road/railway/highway/transit": 27,
	"organization-company": 28,
	"organization-education": 29,
	"organization-government/governmentagency": 30,
	"organization-media/newspaper": 31,
	"organization-other": 32,
	"organization-politicalparty": 33,
	"organization-religion": 34,
	"organization-showorganization": 35,
	"organization-sportsleague": 36,
	"organization-sportsteam": 37,
	"other-astronomything": 38,
	"other-award": 39,
	"other-biologything": 40,
	"other-chemicalthing": 41,
	"other-currency": 42,
	"other-disease": 43,
	"other-educationaldegree": 44,
	"other-god": 45,
	"other-language": 46,
	"other-law": 47,
	"other-livingthing": 48,
	"other-medical": 49,
	"person-actor": 50,
	"person-artist/author": 51,
	"person-athlete": 52,
	"person-director": 53,
	"person-other": 54,
	"person-politician": 55,
	"person-scholar": 56,
	"person-soldier": 57,
	"product-airplane": 58,
	"product-car": 59,
	"product-food": 60,
	"product-game": 61,
	"product-other": 62,
	"product-ship": 63,
	"product-software": 64,
	"product-train": 65,
	"product-weapon": 66,
	}


	class FewNERDConfig(datasets.BuilderConfig):
	"""BuilderConfig for FewNERD"""

	def __init__(self, **kwargs):
	"""BuilderConfig for FewNERD.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(FewNERDConfig, self).__init__(**kwargs)


	class FewNERD(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	FewNERDConfig(name="supervised", description="Fully supervised setting."),
	FewNERDConfig(
	name="inter",
	description="Few-shot setting. Each file contains all 8 coarse "
	"types but different fine-grained types.",
	),
	FewNERDConfig(
	name="intra", description="Few-shot setting. Randomly split by coarse type."
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.features.Sequence(datasets.Value("string")),
	"ner_tags": datasets.features.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"art",
	"building",
	"event",
	"location",
	"organization",
	"other",
	"person",
	"product",
	]
	)
	),
	"fine_ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"art-broadcastprogram",
	"art-film",
	"art-music",
	"art-other",
	"art-painting",
	"art-writtenart",
	"building-airport",
	"building-hospital",
	"building-hotel",
	"building-library",
	"building-other",
	"building-restaurant",
	"building-sportsfacility",
	"building-theater",
	"event-attack/battle/war/militaryconflict",
	"event-disaster",
	"event-election",
	"event-other",
	"event-protest",
	"event-sportsevent",
	"location-GPE",
	"location-bodiesofwater",
	"location-island",
	"location-mountain",
	"location-other",
	"location-park",
	"location-road/railway/highway/transit",
	"organization-company",
	"organization-education",
	"organization-government/governmentagency",
	"organization-media/newspaper",
	"organization-other",
	"organization-politicalparty",
	"organization-religion",
	"organization-showorganization",
	"organization-sportsleague",
	"organization-sportsteam",
	"other-astronomything",
	"other-award",
	"other-biologything",
	"other-chemicalthing",
	"other-currency",
	"other-disease",
	"other-educationaldegree",
	"other-god",
	"other-language",
	"other-law",
	"other-livingthing",
	"other-medical",
	"person-actor",
	"person-artist/author",
	"person-athlete",
	"person-director",
	"person-other",
	"person-politician",
	"person-scholar",
	"person-soldier",
	"product-airplane",
	"product-car",
	"product-food",
	"product-game",
	"product-other",
	"product-ship",
	"product-software",
	"product-train",
	"product-weapon",
	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage="https://ningding97.github.io/fewnerd/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	url_to_download = dl_manager.download_and_extract(_URLs[self.config.name])
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(
	url_to_download,
	self.config.name,
	"train.txt",
	)
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(
	url_to_download, self.config.name, "dev.txt"
	)
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(
	url_to_download, self.config.name, "test.txt"
	)
	},
	),
	]

	def _generate_examples(self, filepath=None):
	# check file type
	assert filepath[-4:] == ".txt"

	num_lines = sum(1 for _ in open(filepath, encoding="utf-8"))
	id = 0

	with open(filepath, "r", encoding="utf-8") as f:
	tokens, ner_tags, fine_ner_tags = [], [], []
	for line in tqdm(f, total=num_lines):
	line = line.strip().split()

	if line:
	assert len(line) == 2
	token, fine_ner_tag = line
	ner_tag = fine_ner_tag.split("-")[0]

	tokens.append(token)
	ner_tags.append(NER_TAGS_DICT[ner_tag])
	fine_ner_tags.append(FINE_NER_TAGS_DICT[fine_ner_tag])

	elif tokens:
	# organize a record to be written into json
	record = {
	"tokens": tokens,
	"id": str(id),
	"ner_tags": ner_tags,
	"fine_ner_tags": fine_ner_tags,
	}
	tokens, ner_tags, fine_ner_tags = [], [], []
	id += 1
	yield record["id"], record

	# take the last sentence
	if tokens:
	record = {
	"tokens": tokens,
	"id": str(id),
	"ner_tags": ner_tags,
	"fine_ner_tags": fine_ner_tags,
	}
	yield record["id"], record