""" from datasets import load_dataset data = load_dataset(".") """ import json import datasets logger = datasets.logging.get_logger(__name__) _DESCRIPTION = """ Named-entities Relation Ranking. """ _NAME = "relentless" _VERSION = "0.2.2" _CITATION = """ @misc{ushio2023relentless, title={A RelEntLess Benchmark for Modelling Graded Relations between Named Entities}, author={Asahi Ushio and Jose Camacho Collados and Steven Schockaert}, year={2023}, eprint={2305.15002}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _HOME_PAGE = "https://huggingface.co/datasets/cardiffnlp/relentless" _URL = f'https://huggingface.co/datasets/cardiffnlp/{_NAME}/raw/main' _URLS = { str(datasets.Split.VALIDATION): [f'{_URL}/data/data_processed.new.validation.jsonl'], str(datasets.Split.TEST): [f'{_URL}/data/data_processed.new.test.jsonl'] } class RelentlessConfig(datasets.BuilderConfig): """BuilderConfig""" def __init__(self, **kwargs): """BuilderConfig. Args: **kwargs: keyword arguments forwarded to super. """ super(RelentlessConfig, self).__init__(**kwargs) class Relentless(datasets.GeneratorBasedBuilder): """Dataset.""" BUILDER_CONFIGS = [RelentlessConfig(version=datasets.Version(_VERSION), description=_DESCRIPTION)] def _split_generators(self, dl_manager): # downloaded_file = dl_manager.download_and_extract(_URLS[self.config.name]) 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.VALIDATION]] 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( { "pairs": datasets.Sequence(datasets.Sequence(datasets.Value("string"))), "scores_all": datasets.Sequence(datasets.Sequence(datasets.Value('int32'))), "scores_mean": datasets.Sequence(datasets.Value("float32")), "relation_type": datasets.Value("string"), "prototypical_examples": datasets.Sequence(datasets.Sequence(datasets.Value("string"))), "ranks": datasets.Sequence(datasets.Value('int32')) } ), supervised_keys=None, homepage=_HOME_PAGE, citation=_CITATION, )