ro_sts_parallel.py

# coding=utf-8
# Copyright 2021 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.
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#
#     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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"""RO-STS-Parallel: a Parallel English-Romanian Dataset by translating the Semantic Textual Similarity dataset"""


import datasets


_CITATION = """\
@inproceedings{dumitrescu2021liro,
  title={Liro: Benchmark and leaderboard for romanian language tasks},
  author={Dumitrescu, Stefan Daniel and Rebeja, Petru and Lorincz, Beata and Gaman, Mihaela and Avram, Andrei and Ilie, Mihai and Pruteanu, Andrei and Stan, Adriana and Rosia, Lorena and Iacobescu, Cristina and others},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
  year={2021}
}
"""

_DESCRIPTION = """\
The RO-STS-Parallel (a Parallel Romanian English dataset - translation of the Semantic Textual Similarity) contains 17256 sentences in Romanian and English. It is a high-quality translation of the English STS benchmark dataset into Romanian.
"""

_HOMEPAGE = "https://github.com/dumitrescustefan/RO-STS"

_LICENSE = "CC BY-SA 4.0 License"

# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://github.com/dumitrescustefan/RO-STS/tree/master/dataset/ro-en"
# _TRAINING_FILE_RO = "RO-STS.train.ro"
# _TRAINING_FILE_EN = "RO-STS.train.en"
# _TEST_FILE_RO = "RO-STS.test.ro"
# _TEST_FILE_EN = "RO-STS.test.en"
# _DEV_FILE_RO = "RO-STS.dev.ro"
# _DEV_FILE_EN = "RO-STS.dev.en"
_DATA_URL = "https://raw.githubusercontent.com/dumitrescustefan/RO-STS/master/dataset/ro-en/RO-STS.{}.{}"


class ROSTSParallelConfig(datasets.BuilderConfig):
    """BuilderConfig for RO-STS-Parallel dataset"""

    def __init__(self, language_pair=(None, None), **kwargs):

        # description = ("RO-STS Parallel dataset, translation from %s to %s") % (language_pair[0], language_pair[1])
        super(ROSTSParallelConfig, self).__init__(**kwargs)
        self.language_pair = language_pair


class RoStsParallel(datasets.GeneratorBasedBuilder):
    """RO-STS-Parallel dataset"""

    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        ROSTSParallelConfig(
            name="ro_sts_parallel", version=VERSION, description="RO-STS Parallel dataset", language_pair=("ro", "en")
        )
    ]

    def _info(self):

        source, target = self.config.language_pair
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {"translation": datasets.features.Translation(languages=self.config.language_pair)}
            ),
            supervised_keys=(source, target),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        source, target = self.config.language_pair

        files = {}
        for split in ("train", "dev", "test"):
            if split == "train":
                dl_dir_src = dl_manager.download_and_extract(_DATA_URL.format("train", source))
                dl_dir_tar = dl_manager.download_and_extract(_DATA_URL.format("train", target))
            if split == "dev":
                dl_dir_src = dl_manager.download_and_extract(_DATA_URL.format("dev", source))
                dl_dir_tar = dl_manager.download_and_extract(_DATA_URL.format("dev", target))
            if split == "test":
                dl_dir_src = dl_manager.download_and_extract(_DATA_URL.format("test", source))
                dl_dir_tar = dl_manager.download_and_extract(_DATA_URL.format("test", target))

            files[split] = {"source_file": dl_dir_src, "target_file": dl_dir_tar}

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=files["train"]),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=files["dev"]),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=files["test"]),
        ]

    def _generate_examples(self, source_file, target_file):
        """This function returns the examples in the raw (text) form."""
        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")

        source, target = self.config.language_pair
        for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
            result = {"translation": {source: l1, target: l2}}
            yield idx, result