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"""Machine translation EN-AZ dataset based on Google Translate and National Library of Azerbaijan.""" |
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import os |
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import datasets |
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_CITATION = """\ |
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@InProceedings{ |
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huggingface:dataset, |
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title={Machine translation EN-AZ dataset}, |
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author={Learning Machine LLC}, |
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year={2022} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Machine translation EN-AZ dataset based on Google Translate and National Library of Azerbaijan. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/learningmachineaz/translate_enaz_10m" |
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_LICENSE = "Apache" |
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_URL = "https://learningmachine.az/datasets/translate_enaz_10m.zip" |
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class TranslateEnaz10m(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"translation": datasets.Value("string"), |
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"source_text": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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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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data_dir = dl_manager.download_and_extract(_URL) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"file_path": os.path.join(data_dir, "dataset_enaz_10m.tsv") |
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} |
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) |
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] |
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def _generate_examples(self, file_path): |
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with open(file_path, "r", encoding="utf-8") as f: |
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for id_, row in enumerate(f): |
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row = row.split("\t") |
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yield id_, { |
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"translation": row[0].strip(), |
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"source_text": row[1].strip(), |
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} |
