# coding=utf-8 # Copyright 2020 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. # Lint as: python3 import json import datasets from dataclasses import dataclass _CITATION = ''' coming soon ... ''' languages = [ 'english', 'french', ] _DESCRIPTION = '100 token long Wikipedia Passages from April/May 2022' _DATASET_URLS = { lang: { 'collection': f'https://huggingface.co/datasets/ToluClassics/masakhane_wiki_100/resolve/main/masakhane_wiki_100-{lang}/corpus.jsonl', } for lang in languages } class MrTyDiCorpus(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ datasets.BuilderConfig( version=datasets.Version('1.1.0'), name=lang, description=f'Wikipedia Passages in language {lang}.' ) for lang in languages ] def _info(self): features = datasets.Features({ 'docid': datasets.Value('string'), 'text': datasets.Value('string'), 'title': datasets.Value('string'), }) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations supervised_keys=None, # Homepage of the dataset for documentation homepage='https://huggingface.co/datasets/ToluClassics/masakhane_wiki_100', # License for the dataset if available license='', # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): lang = self.config.name downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[lang]) splits = [ datasets.SplitGenerator( name='collection', gen_kwargs={ 'filepath': downloaded_files['collection'], }, ), ] return splits def _generate_examples(self, filepath): with open(filepath, encoding="utf-8") as f: for line in f: data = json.loads(line) yield data['docid'], data