# 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. """The Large Spanish Corpus is a compilation of Spanish corpora spanning Wikipedia to European parliament notes.""" import os import datasets _CITATION = """\ @dataset{jose_canete_2019_3247731, author = {José Cañete}, title = {Compilation of Large Spanish Unannotated Corpora}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.3247731}, url = {https://doi.org/10.5281/zenodo.3247731} } """ _DESCRIPTION = """\ The Large Spanish Corpus is a compilation of 15 unlabelled Spanish corpora spanning Wikipedia to European parliament \ notes. Each config contains the data corresponding to a different corpus. For example, "all_wiki" only includes \ examples from Spanish Wikipedia. By default, the config is set to "combined" which loads all the corpora; with this \ setting you can also specify the number of samples to return per corpus by configuring the "split" argument. """ _HOMEPAGE = "https://github.com/josecannete/spanish-corpora" _LICENSE = "MIT" _URL = "https://zenodo.org/record/3247731/files/raw.tar.bz2" _CORPORA = [ "JRC", "EMEA", "GlobalVoices", "ECB", "DOGC", "all_wikis", "TED", "multiUN", "Europarl", "NewsCommentary11", "UN", "EUBookShop", "ParaCrawl", "OpenSubtitles2018", "DGT", ] _CORPORA_FILEPATHS = {corpus: os.path.join("spanish-corpora", "raw", f"{corpus}.txt") for corpus in _CORPORA} _VERSION = "1.1.0" _COMBINED = "combined" class LargeSpanishCorpusConfig(datasets.BuilderConfig): def __init__(self, corpora=None, **kwargs): super(LargeSpanishCorpusConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs) self.corpora = corpora @property def filepaths(self): return [_CORPORA_FILEPATHS[corpus] for corpus in self.corpora] class LargeSpanishCorpus(datasets.GeneratorBasedBuilder): """The Large Spanish Corpus.""" BUILDER_CONFIGS = [ LargeSpanishCorpusConfig(name=corpus, corpora=[corpus], description=f"Spanish examples in corpus {corpus}.") for corpus in _CORPORA ] + [ LargeSpanishCorpusConfig( name=_COMBINED, corpora=_CORPORA, description="Complete Spanish dataset with all corpora." ) ] BUILDER_CONFIG_CLASS = LargeSpanishCorpusConfig DEFAULT_CONFIG_NAME = _COMBINED def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "text": datasets.Value("string"), } ), supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): data_dir = dl_manager.download_and_extract(_URL) return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir})] def _generate_examples(self, data_dir): _id = 0 for filepath in self.config.filepaths: filepath = os.path.join(data_dir, filepath) with open(filepath, mode="r", encoding="utf-8") as f: for line in f: yield _id, {"text": line.strip()}, _id += 1