wiki-entity-similarity.py

import datasets

from dataclasses import dataclass
import csv

_DESCRIPTION = '''WES: Learning Semantic Similarity from 6M Names for 1M Entities'''
_CITE = '''\
@inproceedings{exr0n2022WES
    author={Exr0n},
    title={WES: Learning Semantic Similarity from 6M Names for 1M Entities},
    year={2022}
}
'''

_HUGGINGFACE_REPO = "https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/"

@dataclass
class WikiEntitySimilarityConfig(datasets.BuilderConfig):
    """BuilderConfig for CSV."""
    year: int = None
    type: str = None
    threshhold: int = None
    # path: str = None

class WikiEntitySimilarity(datasets.GeneratorBasedBuilder):
    """WES: Learning semantic similarity from 6M names for 1M entities"""
    BUILDER_CONFIG_CLASS = WikiEntitySimilarityConfig
    BUILDER_CONFIGS = [
        WikiEntitySimilarityConfig(
            name='2018thresh5corpus',
            description='raw link corpus (all true): min 5 inbound links, lowest quality',
            year=2018,
            type='corpus',
            threshhold=5,
            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/link_synonyms-2018-thresh_5.csv"
        ),
        WikiEntitySimilarityConfig(
            name='2018thresh10corpus',
            description='raw link corpus (all true): min 10 inbound links, medium quality',
            year=2018,
            type='corpus',
            threshhold=10,
            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/link_synonyms-2018-thresh_10.csv"
        ),
        WikiEntitySimilarityConfig(
            name='2018thresh20corpus',
            description='raw link corpus (all true): min 20 inbound links, high quality',
            year=2018,
            type='corpus',
            threshhold=20,
            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/link_synonyms-2018-thresh_20.csv"
        ),
        WikiEntitySimilarityConfig(
            name='2018thresh5pairs',
            description='training pairs based on min 5 inbound links, lowest quality',
            year=2018,
            type='pairs',
            threshhold=5,
            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/2018thresh5"
        ),
        WikiEntitySimilarityConfig(
            name='2018thresh10pairs',
            description='training pairs based on min 10 inbound links, medium quality',
            year=2018,
            type='pairs',
            threshhold=10,
            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/2018thresh10"
        ),
        WikiEntitySimilarityConfig(
            name='2018thresh20pairs',
            description='training pairs based on min 20 inbound links, high quality',
            year=2018,
            type='pairs',
            threshhold=20,
            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/2018thresh20"
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    'article': datasets.Value('string'),
                    'link_text': datasets.Value('string'),
                    'is_same': datasets.Value('uint8'),
                }
            ),
            citation=_CITE,
            homepage="https://github.com/Exr0nProjects/wiki-entity-similarity",
        )

    def _split_generators(self, dl_manager):
        path = _HUGGINGFACE_REPO + f"{self.config.year}thresh{self.config.threshhold}"
        if self.config.type == 'corpus':
            filepath = dl_manager.download(path + 'corpus.csv')
            return [ datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                             gen_kwargs={ 'path': filepath }) ]
        elif self.config.type == 'pairs':
            ret = []
            for n, e in zip(['train', 'dev', 'test'],
                            [datasets.Split.TRAIN,
                             datasets.Split.VALIDATION,
                             datasets.Split.TEST]):
                fp = dl_manager.download(path + n + '.csv')
                ret.append( datasets.SplitGenerator(name=e, gen_kwargs={ 'path': fp }) )
            return ret
        else:
            raise ValueError(f"invalid dataset type '{self.config.type}', expected 'corpus' for raw links or 'pairs' for trainable pairs with negative examples")

    def _generate_examples(self, path):
        with open(path, 'r') as rf:
            reader = csv.DictReader(rf)
            for i, row in enumerate(reader):
                yield i, row