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"""RO-STS-Parallel: a Parallel English-Romanian Dataset by translating the Semantic Textual Similarity dataset""" |
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from __future__ import absolute_import, division, print_function |
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import datasets |
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_CITATION = """\ |
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Article under review |
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""" |
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_DESCRIPTION = """\ |
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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. |
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""" |
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_HOMEPAGE = "https://github.com/dumitrescustefan/RO-STS" |
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_LICENSE = "CC BY-SA 4.0 License" |
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_URL = "https://github.com/dumitrescustefan/RO-STS/tree/master/dataset/ro-en" |
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_DATA_URL = "https://raw.githubusercontent.com/dumitrescustefan/RO-STS/master/dataset/ro-en/RO-STS.{}.{}" |
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class ROSTSParallelConfig(datasets.BuilderConfig): |
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"""BuilderConfig for RO-STS-Parallel dataset""" |
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def __init__(self, language_pair=(None, None), **kwargs): |
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super(ROSTSParallelConfig, self).__init__(**kwargs) |
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self.language_pair = language_pair |
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class RoStsParallel(datasets.GeneratorBasedBuilder): |
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"""RO-STS-Parallel dataset""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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ROSTSParallelConfig( |
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name="ro_sts_parallel", version=VERSION, description="RO-STS Parallel dataset", language_pair=("ro", "en") |
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) |
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] |
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def _info(self): |
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source, target = self.config.language_pair |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{"translation": datasets.features.Translation(languages=self.config.language_pair)} |
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), |
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supervised_keys=(source, target), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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source, target = self.config.language_pair |
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files = {} |
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for split in ("train", "dev", "test"): |
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if split == "train": |
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dl_dir_src = dl_manager.download_and_extract(_DATA_URL.format("train", source)) |
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dl_dir_tar = dl_manager.download_and_extract(_DATA_URL.format("train", target)) |
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if split == "dev": |
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dl_dir_src = dl_manager.download_and_extract(_DATA_URL.format("dev", source)) |
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dl_dir_tar = dl_manager.download_and_extract(_DATA_URL.format("dev", target)) |
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if split == "test": |
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dl_dir_src = dl_manager.download_and_extract(_DATA_URL.format("test", source)) |
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dl_dir_tar = dl_manager.download_and_extract(_DATA_URL.format("test", target)) |
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files[split] = {"source_file": dl_dir_src, "target_file": dl_dir_tar} |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=files["train"]), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=files["dev"]), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=files["test"]), |
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] |
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def _generate_examples(self, source_file, target_file): |
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"""This function returns the examples in the raw (text) form.""" |
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with open(source_file, encoding="utf-8") as f: |
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source_sentences = f.read().split("\n") |
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with open(target_file, encoding="utf-8") as f: |
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target_sentences = f.read().split("\n") |
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source, target = self.config.language_pair |
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for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)): |
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result = {"translation": {source: l1, target: l2}} |
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yield idx, result |
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