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import json
from itertools import product
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """T-Rex dataset."""
_NAME = "t_rex_relational_similarity"
_VERSION = "0.0.1"
_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,
) |