Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Tags:
License:
| """ NER dataset compiled by T-NER library https://github.com/asahi417/tner/tree/master/tner """ | |
| import json | |
| from itertools import chain | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _DESCRIPTION = """[Bio Creative 5 CDR NER dataset](https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true)""" | |
| _NAME = "bc5cdr" | |
| _VERSION = "1.0.0" | |
| _CITATION = """ | |
| @article{wei2016assessing, | |
| title={Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task}, | |
| author={Wei, Chih-Hsuan and Peng, Yifan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Li, Jiao and Wiegers, Thomas C and Lu, Zhiyong}, | |
| journal={Database}, | |
| volume={2016}, | |
| year={2016}, | |
| publisher={Oxford Academic} | |
| } | |
| """ | |
| _HOME_PAGE = "https://github.com/asahi417/tner" | |
| _URL = f'https://huggingface.co/datasets/tner/{_NAME}/raw/main/dataset' | |
| _URLS = { | |
| str(datasets.Split.TEST): [f'{_URL}/test.json'], | |
| str(datasets.Split.TRAIN): [f'{_URL}/train.json'], | |
| str(datasets.Split.VALIDATION): [f'{_URL}/valid.json'], | |
| } | |
| class BC5CDRConfig(datasets.BuilderConfig): | |
| """BuilderConfig""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(BC5CDRConfig, self).__init__(**kwargs) | |
| class BC5CDR(datasets.GeneratorBasedBuilder): | |
| """Dataset.""" | |
| BUILDER_CONFIGS = [ | |
| BC5CDRConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION), | |
| ] | |
| def _split_generators(self, dl_manager): | |
| downloaded_file = dl_manager.download_and_extract(_URLS) | |
| return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]}) | |
| for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]] | |
| def _generate_examples(self, filepaths): | |
| _key = 0 | |
| for filepath in filepaths: | |
| logger.info(f"generating examples from = {filepath}") | |
| with open(filepath, encoding="utf-8") as f: | |
| _list = [i for i in f.read().split('\n') if len(i) > 0] | |
| for i in _list: | |
| data = json.loads(i) | |
| yield _key, data | |
| _key += 1 | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "tokens": datasets.Sequence(datasets.Value("string")), | |
| "tags": datasets.Sequence(datasets.Value("int32")), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOME_PAGE, | |
| citation=_CITATION, | |
| ) | |