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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors.
#
# 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.
"""Capes: Parallel corpus of theses and dissertation abstracts in Portuguese and English from CAPES"""
from __future__ import absolute_import, division, print_function
import os
import datasets
_CITATION = """\
@inproceedings{soares2018parallel,
title={A Parallel Corpus of Theses and Dissertations Abstracts},
author={Soares, Felipe and Yamashita, Gabrielli Harumi and Anzanello, Michel Jose},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={345--352},
year={2018},
organization={Springer}
}
"""
_DESCRIPTION = """\
A parallel corpus of theses and dissertations abstracts in English and Portuguese were collected from the \
CAPES website (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) - Brazil. \
The corpus is sentence aligned for all language pairs. Approximately 240,000 documents were \
collected and aligned using the Hunalign algorithm.
"""
_HOMEPAGE = "https://sites.google.com/view/felipe-soares/datasets#h.p_kxOR6EhHm2a6"
_URL = "https://ndownloader.figstatic.com/files/14015837"
class Capes(datasets.GeneratorBasedBuilder):
"""Capes: Parallel corpus of theses and dissertation abstracts in Portuguese and English from CAPES"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="en-pt",
version=datasets.Version("1.0.0"),
description="Parallel corpus of theses and dissertation abstracts in Portuguese and English from CAPES",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{"translation": datasets.features.Translation(languages=tuple(self.config.name.split("-")))}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"source_file": os.path.join(data_dir, "en_pt.en"),
"target_file": os.path.join(data_dir, "en_pt.pt"),
},
),
]
def _generate_examples(self, source_file, target_file):
with open(source_file, encoding="utf-8") as f:
source_sentences = f.read().split("\n")
with open(target_file, encoding="utf-8") as f:
target_sentences = f.read().split("\n")
assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % (
len(source_sentences),
len(target_sentences),
source_file,
target_file,
)
source, target = tuple(self.config.name.split("-"))
for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
result = {"translation": {source: l1, target: l2}}
yield idx, result
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