import json from itertools import product import datasets logger = datasets.logging.get_logger(__name__) _DESCRIPTION = """T-Rex dataset.""" _NAME = "t_rex_relational_similarity" _VERSION = "0.0.2" _CITATION = """ @inproceedings{elsahar2018t, title={T-rex: A large scale alignment of natural language with knowledge base triples}, author={Elsahar, Hady and Vougiouklis, Pavlos and Remaci, Arslen and Gravier, Christophe and Hare, Jonathon and Laforest, Frederique and Simperl, Elena}, booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year={2018} } """ _HOME_PAGE = "https://github.com/asahi417/relbert" _URL = f'https://huggingface.co/datasets/relbert/{_NAME}/resolve/main/data' MIN_ENTITY_FREQ = [1, 2, 3, 4] MAX_PREDICATE_FREQ = [100, 50, 25, 10] _TYPES = [f"filter_unified.min_entity_{a}_max_predicate_{b}" for a, b in product(MIN_ENTITY_FREQ, MAX_PREDICATE_FREQ)] _URLS = {i: { str(datasets.Split.TRAIN): [f'{_URL}/{i}.train.jsonl'], str(datasets.Split.VALIDATION): [f'{_URL}/{i}.validation.jsonl'], str(datasets.Split.TEST): [f'{_URL}/filter_unified.test.jsonl'] } for i in _TYPES} class TREXRelationalSimilarityConfig(datasets.BuilderConfig): """BuilderConfig""" def __init__(self, **kwargs): """BuilderConfig. Args: **kwargs: keyword arguments forwarded to super. """ super(TREXRelationalSimilarityConfig, self).__init__(**kwargs) class TREXRelationalSimilarity(datasets.GeneratorBasedBuilder): """Dataset.""" BUILDER_CONFIGS = [ TREXRelationalSimilarityConfig(name=i, version=datasets.Version(_VERSION), description=_DESCRIPTION) for i in sorted(_TYPES) ] def _split_generators(self, dl_manager): downloaded_file = dl_manager.download_and_extract(_URLS[self.config.name]) 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( { "relation_type": datasets.Value("string"), "positives": datasets.Sequence(datasets.Sequence(datasets.Value("string"))), "negatives": datasets.Sequence(datasets.Sequence(datasets.Value("string"))), } ), supervised_keys=None, homepage=_HOME_PAGE, citation=_CITATION, )