""" 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 = """[FIN NER dataset](https://aclanthology.org/U15-1010.pdf)""" _NAME = "fin" _VERSION = "1.2.0" _CITATION = """ @inproceedings{salinas-alvarado-etal-2015-domain, title = "Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment", author = "Salinas Alvarado, Julio Cesar and Verspoor, Karin and Baldwin, Timothy", booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015", month = dec, year = "2015", address = "Parramatta, Australia", url = "https://aclanthology.org/U15-1010", pages = "84--90", } """ _LABEL2ID = {"O": 0, "B-PER": 1, "B-LOC": 2, "B-ORG": 3, "B-MISC": 4, "I-PER": 5, "I-LOC": 6, "I-ORG": 7, "I-MISC": 8} _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 FinConfig(datasets.BuilderConfig): """BuilderConfig""" def __init__(self, **kwargs): """BuilderConfig. Args: **kwargs: keyword arguments forwarded to super. """ super(FinConfig, self).__init__(**kwargs) class Fin(datasets.GeneratorBasedBuilder): """Dataset.""" BUILDER_CONFIGS = [ FinConfig(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 )