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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"""
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."""
archive = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"source_file": "en_pt.en",
"target_file": "en_pt.pt",
"src_files": dl_manager.iter_archive(archive),
"tgt_files": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, source_file, target_file, src_files, tgt_files):
source, target = tuple(self.config.name.split("-"))
for src_path, src_f in src_files:
if src_path == source_file:
for tgt_path, tgt_f in tgt_files:
if tgt_path == target_file:
for idx, (l1, l2) in enumerate(zip(src_f, tgt_f)):
l1 = l1.decode("utf-8").strip()
l2 = l2.decode("utf-8").strip()
if l1 and l2:
result = {"translation": {source: l1, target: l2}}
yield idx, result
break
break
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