# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pathlib import Path from typing import Dict, List, Tuple import datasets from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses _CITATION = """\ @INPROCEEDINGS{ramli2022indokepler, author={Ramli, Inigo and Krisnadhi, Adila Alfa and Prasojo, Radityo Eko}, booktitle={2022 7th International Workshop on Big Data and Information Security (IWBIS)}, title={IndoKEPLER, IndoWiki, and IndoLAMA: A Knowledge-enhanced Language Model, Dataset, and Benchmark for the Indonesian Language}, year={2022}, volume={}, number={}, pages={19-26}, doi={10.1109/IWBIS56557.2022.9924844}} """ _DATASETNAME = "indowiki" _DESCRIPTION = """\ IndoWiki is a knowledge-graph dataset taken from WikiData and aligned with Wikipedia Bahasa Indonesia as it's corpus. """ _HOMEPAGE = "https://github.com/IgoRamli/IndoWiki" _LANGUAGES = ["ind"] _LICENSE = Licenses.MIT.value _LOCAL = False _URLS = { "inductive": { "train": "https://drive.google.com/uc?export=download&id=1S3vNx9By5CWKGkObjtXaI6Jr4xri2Tz3", "valid": "https://drive.google.com/uc?export=download&id=1cP-zDIxp9a-Bw9uYd40K9IN-4wg4dOgy", "test": "https://drive.google.com/uc?export=download&id=1pLcoJgYmgQiN4Gv9tRcI26zM7-OgHcuZ", }, "transductive": { "train": "https://drive.google.com/uc?export=download&id=1KXDVwboo1h2yk_kAqv7IPYnHXCK6g-6X", "valid": "https://drive.google.com/uc?export=download&id=1eRwpuRPYOnA-7FZ-YNZjRJ2DHuJsfUIE", "test": "https://drive.google.com/uc?export=download&id=1cy9FwDMB_U-js8P8u4IWolvNeIFkQVDh", }, "text": "https://drive.usercontent.google.com/download?id=1YC4P_IPSo1AsEwm5Z_4GBjDdwCbvokxX&export=download&authuser=0&confirm=t&uuid=36aa95f5-e1b6-43c1-a34f-754d14d8b473&at=APZUnTWD7fwarBs4ZVRy_QdKbDXi%3A1709478240158", } # none of the tasks in schema # dataset is used to learn knowledge embedding _SUPPORTED_TASKS = [] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class IndoWiki(datasets.GeneratorBasedBuilder): """IndoWiki knowledge base dataset from https://github.com/IgoRamli/IndoWiki""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [ # inductive setting SEACrowdConfig( name=f"{_DATASETNAME}_inductive_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=_DATASETNAME, ), # transductive setting SEACrowdConfig( name=f"{_DATASETNAME}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=_DATASETNAME, ), ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "ent1": datasets.Value("string"), "ent2": datasets.Value("string"), "ent1_text": datasets.Value("string"), "ent2_text": datasets.Value("string"), "relation": datasets.Value("string"), } ) else: raise NotImplementedError() return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" if "inductive" in self.config.name: setting = "inductive" data_paths = { "inductive": { "train": Path(dl_manager.download_and_extract(_URLS["inductive"]["train"])), "valid": Path(dl_manager.download_and_extract(_URLS["inductive"]["valid"])), "test": Path(dl_manager.download_and_extract(_URLS["inductive"]["test"])), }, "text": Path(dl_manager.download_and_extract(_URLS["text"])), } else: setting = "transductive" data_paths = { "transductive": { "train": Path(dl_manager.download_and_extract(_URLS["transductive"]["train"])), "valid": Path(dl_manager.download_and_extract(_URLS["transductive"]["valid"])), "test": Path(dl_manager.download_and_extract(_URLS["transductive"]["test"])), }, "text": Path(dl_manager.download_and_extract(_URLS["text"])), } return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "triplets_filepath": data_paths[setting]["train"], "text_filepath": data_paths["text"], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "triplets_filepath": data_paths[setting]["test"], "text_filepath": data_paths["text"], "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "triplets_filepath": data_paths[setting]["valid"], "text_filepath": data_paths["text"], "split": "dev", }, ), ] def _generate_examples(self, triplets_filepath: Path, text_filepath: Path, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" # read triplets file with open(triplets_filepath, "r", encoding="utf-8") as triplets_file: triplets_data = triplets_file.readlines() triplets_data = [s.strip("\n").split("\t") for s in triplets_data] # read text description file with open(text_filepath, "r", encoding="utf-8") as text_file: text_data = text_file.readlines() # dictionary of entity: text description of entity text_dict = {s.split("\t")[0]: s.split("\t")[1].strip("\n") for s in text_data} num_sample = len(triplets_data) for i in range(num_sample): if self.config.schema == "source": example = { "id": str(i), "ent1": triplets_data[i][0], "ent2": triplets_data[i][2], "ent1_text": text_dict[triplets_data[i][0]], "ent2_text": text_dict[triplets_data[i][2]], "relation": triplets_data[i][1], } yield i, example