Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
100K - 1M
Tags:
structure-prediction
License:
| 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 |