capes.py

# coding=utf-8
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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