# 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. """caWaC: Catalan web corpus dataset.""" import datasets _CITATION = """\ @inproceedings{DBLP:conf/lrec/LjubesicT14, author = {Nikola Ljubesic and Antonio Toral}, editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asunci{\'{o}}n Moreno and Jan Odijk and Stelios Piperidis}, title = {caWaC - {A} web corpus of Catalan and its application to language modeling and machine translation}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation, {LREC} 2014, Reykjavik, Iceland, May 26-31, 2014}, pages = {1728--1732}, publisher = {European Language Resources Association {(ELRA)}}, year = {2014}, url = {http://www.lrec-conf.org/proceedings/lrec2014/summaries/841.html}, timestamp = {Mon, 19 Aug 2019 15:23:35 +0200}, biburl = {https://dblp.org/rec/conf/lrec/LjubesicT14.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _DESCRIPTION = """\ caWaC is a 780-million-token web corpus of Catalan built from the .cat top-level-domain in late 2013. """ _LICENSE = "CC BY-SA 3.0" _HOMEPAGE = "http://nlp.ffzg.hr/resources/corpora/cawac/" # Source: http://nlp.ffzg.hr/data/corpora/cawac.uniq.sortr.gz _URLS = "data/cawac.uniq.sortr.gz" class Cawac(datasets.GeneratorBasedBuilder): """caWaC: Catalan web corpus dataset.""" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "sentence": datasets.Value("string"), } ), supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): downloaded_file = dl_manager.download_and_extract(_URLS) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": downloaded_file, }, ), ] def _generate_examples(self, filepath): with open(filepath, encoding="utf8") as f: for id_, row in enumerate(f): yield id_, { "sentence": row, }