| import json | |
| from itertools import product | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _DESCRIPTION = """T-Rex dataset.""" | |
| _NAME = "t_rex" | |
| _VERSION = "0.0.5" | |
| _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 = [4, 8, 12, 16] | |
| 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)] | |
| _TYPES += ["raw", "filter", "filter_unified"] | |
| _NON_SPLITS = ["raw", "filter", "filter_unified"] | |
| _URLS = {i: {str(datasets.Split.TRAIN): [f'{_URL}/t_rex.{i}.jsonl']} if i in _NON_SPLITS else { | |
| str(datasets.Split.TRAIN): [f'{_URL}/t_rex.{i}.train.jsonl'], | |
| str(datasets.Split.VALIDATION): [f'{_URL}/t_rex.{i}.validation.jsonl'], | |
| str(datasets.Split.TEST): [f'{_URL}/t_rex.{i}.test.jsonl']} | |
| for i in _TYPES} | |
| class TREXConfig(datasets.BuilderConfig): | |
| """BuilderConfig""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(TREXConfig, self).__init__(**kwargs) | |
| class TREX(datasets.GeneratorBasedBuilder): | |
| """Dataset.""" | |
| BUILDER_CONFIGS = [ | |
| TREXConfig(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]) | |
| if self.config.name in _NON_SPLITS: | |
| return [datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"filepaths": downloaded_file[str(datasets.Split.TRAIN)]})] | |
| else: | |
| 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( | |
| { | |
| "title": datasets.Value("string"), | |
| "text": datasets.Value("string"), | |
| "predicate": datasets.Value("string"), | |
| "object": datasets.Value("string"), | |
| "subject": datasets.Value("string") | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOME_PAGE, | |
| citation=_CITATION, | |
| ) |