Datasets:

soarescmsa
/

capes

	# 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
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	# distributed under the License is distributed on an "AS IS" BASIS,
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	"""Capes: Parallel corpus of theses and dissertation abstracts in Portuguese and English from CAPES"""


	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