relational_similarity.py

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,
        )