Datasets:
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# 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"
_URL = "http://nlp.ffzg.hr/resources/corpora/cawac/"
_URLS = "http://nlp.ffzg.hr/data/corpora/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=_URL,
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,
}
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