File size: 3,830 Bytes
e1ff3c2 cdfc2d5 e1ff3c2 14243d9 e1ff3c2 d106dd9 e1ff3c2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """NELL-one for relational similarity"""
_NAME = "nell_relational_similarity"
_VERSION = "0.0.3"
_CITATION = """@inproceedings{xiong-etal-2018-one,
title = "One-Shot Relational Learning for Knowledge Graphs",
author = "Xiong, Wenhan and
Yu, Mo and
Chang, Shiyu and
Guo, Xiaoxiao and
Wang, William Yang",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1223",
doi = "10.18653/v1/D18-1223",
pages = "1980--1990",
abstract = "Knowledge graphs (KG) are the key components of various natural language processing applications. To further expand KGs{'} coverage, previous studies on knowledge graph completion usually require a large number of positive examples for each relation. However, we observe long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.",
}
"""
_HOME_PAGE = "https://github.com/asahi417/relbert"
_URL = f'https://huggingface.co/datasets/relbert/{_NAME}/raw/main/data'
_URLS = {
str(datasets.Split.TRAIN): [f'{_URL}/train.jsonl'],
str(datasets.Split.VALIDATION): [f'{_URL}/validation.jsonl'],
str(datasets.Split.TEST): [f'{_URL}/test.jsonl'],
}
class NELLNetRelationalSimilarityConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(NELLNetRelationalSimilarityConfig, self).__init__(**kwargs)
class NELLNetRelationalSimilarity(datasets.GeneratorBasedBuilder):
"""Dataset."""
BUILDER_CONFIGS = [
NELLNetRelationalSimilarityConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
]
def _split_generators(self, dl_manager):
downloaded_file = dl_manager.download_and_extract(_URLS)
return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[i]}) for i in _URLS.keys()]
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,
)
|