Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
100K - 1M
Tags:
structure-prediction
License:
File size: 11,479 Bytes
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import os
import json
import datasets
from tqdm 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).
"""
# the original data files (zip of .txt) can be downloaded from tsinghua cloud
_URLs = {
"supervised": "https://cloud.tsinghua.edu.cn/f/09265750ae6340429827/?dl=1",
"intra": "https://cloud.tsinghua.edu.cn/f/a0d3efdebddd4412b07c/?dl=1",
"inter": "https://cloud.tsinghua.edu.cn/f/165693d5e68b43558f9b/?dl=1",
}
# 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."""
urls_to_download = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(
urls_to_download[self.config.name],
self.config.name,
"train.txt",
)
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(
urls_to_download[self.config.name], self.config.name, "dev.txt"
)
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(
urls_to_download[self.config.name], 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))
id = 0
with open(filepath, "r") 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 |