Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K - 100K
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, | |
) | |