| import json | |
| from itertools import combinations | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _DESCRIPTION = """TBA""" | |
| _NAME = "relational_similarity" | |
| _VERSION = "0.0.2" | |
| _CITATION = """TBA""" | |
| _HOME_PAGE = "https://github.com/asahi417/relbert" | |
| _URL = f'https://huggingface.co/datasets/relbert/{_NAME}/raw/main/data' | |
| _DATA_ALL = [ | |
| 'semeval2012_relational_similarity', | |
| 'nell_relational_similarity', | |
| 't_rex_relational_similarity', | |
| 'conceptnet_relational_similarity', | |
| ] | |
| _ALL_TYPES = [] | |
| for n in range(2, len(_DATA_ALL) + 1): | |
| _ALL_TYPES += list(combinations(_DATA_ALL, n)) | |
| _ALL_TYPES = [sorted(i) for i in _ALL_TYPES] | |
| _ALL_TYPES_DICT = {'.'.join(i): i for i in _ALL_TYPES} | |
| _URLS = { | |
| k: { | |
| str(datasets.Split.TRAIN): [f'{_URL}/{_v}.train.jsonl' for _v in v], | |
| str(datasets.Split.VALIDATION): [f'{_URL}/{_v}.validation.jsonl' for _v in v] | |
| } | |
| for k, v in _ALL_TYPES_DICT.items()} | |
| class RelationalSimilarityConfig(datasets.BuilderConfig): | |
| """BuilderConfig""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(RelationalSimilarityConfig, self).__init__(**kwargs) | |
| class NELLNetRelationalSimilarity(datasets.GeneratorBasedBuilder): | |
| """Dataset.""" | |
| BUILDER_CONFIGS = [ | |
| RelationalSimilarityConfig(name=i, version=datasets.Version(_VERSION), description=_DESCRIPTION) | |
| for i in sorted(_ALL_TYPES_DICT.keys()) | |
| ] | |
| 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]] | |
| 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, | |
| ) |