| from __future__ import annotations | |
| import json | |
| import random | |
| from typing import Generator | |
| import datasets | |
| _CITATION = """ | |
| @inproceedings{omi-2021-wikipedia, | |
| title = "Wikipediaを用いた日本語の固有表現抽出のデータセットの構築", | |
| author = "近江 崇宏", | |
| booktitle = "言語処理学会第27回年次大会", | |
| year = "2021", | |
| url = "https://anlp.jp/proceedings/annual_meeting/2021/pdf_dir/P2-7.pdf", | |
| } | |
| """ | |
| _DESCRIPTION = "This is a dataset of Wikipedia articles with named entity labels created by Stockmark Inc." | |
| _HOMEPAGE = "https://github.com/stockmarkteam/ner-wikipedia-dataset" | |
| _LICENSE = "CC-BY-SA 3.0" | |
| _URL = "https://raw.githubusercontent.com/stockmarkteam/ner-wikipedia-dataset/main/ner.json" | |
| class NerWikipediaDatasetConfig(datasets.BuilderConfig): | |
| def __init__( | |
| self, | |
| name: str = "default", | |
| version: datasets.Version | str | None = datasets.Version("0.0.0"), | |
| data_dir: str | None = None, | |
| data_files: datasets.data_files.DataFilesDict | None = None, | |
| description: str | None = _DESCRIPTION, | |
| shuffle: bool = True, | |
| seed: int = 42, | |
| train_ratio: float = 0.8, | |
| validation_ratio: float = 0.1, | |
| ) -> None: | |
| super().__init__( | |
| name=name, | |
| version=version, | |
| data_dir=data_dir, | |
| data_files=data_files, | |
| description=description, | |
| ) | |
| self.shuffle = shuffle | |
| self.seed = seed | |
| self.train_ratio = train_ratio | |
| self.validation_ratio = validation_ratio | |
| class NerWikipediaDataset(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIG_CLASS = NerWikipediaDatasetConfig | |
| def _info(self) -> datasets.DatasetInfo: | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "curid": datasets.Value("string"), | |
| "text": datasets.Value("string"), | |
| "entities": [ | |
| { | |
| "name": datasets.Value("string"), | |
| "span": datasets.Sequence( | |
| datasets.Value("int64"), length=2 | |
| ), | |
| "type": datasets.Value("string"), | |
| } | |
| ], | |
| } | |
| ), | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators( | |
| self, dl_manager: datasets.DownloadManager | |
| ) -> list[datasets.SplitGenerator]: | |
| dataset_dir = str(dl_manager.download_and_extract(_URL)) | |
| with open(dataset_dir, "r", encoding="utf-8") as f: | |
| data = json.load(f) | |
| if self.config.shuffle == True: | |
| random.seed(self.config.seed) | |
| random.shuffle(data) | |
| num_data = len(data) | |
| num_train_data = int(num_data * self.config.train_ratio) | |
| num_validation_data = int(num_data * self.config.validation_ratio) | |
| train_data = data[:num_train_data] | |
| validation_data = data[num_train_data : num_train_data + num_validation_data] | |
| test_data = data[num_train_data + num_validation_data :] | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"data": train_data}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={"data": validation_data}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={"data": test_data}, | |
| ), | |
| ] | |
| def _generate_examples(self, data: list[dict[str, str]]) -> Generator: | |
| for i, d in enumerate(data): | |
| yield i, { | |
| "curid": d["curid"], | |
| "text": d["text"], | |
| "entities": d["entities"], | |
| } | |