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