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import collections |
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import gzip |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_FILES = ['10CGDFvfMddPmbkXiM4ntGZ75UyLAEX9N','1-uv2ALG5C7P02tAnOrRVoAyHsykWOTpg','10NUJo7Uf-Y1K1HS_Rj1wXPjEK6BsCnsR','1-xMQDKAssrgPs21plEI3wbn5sNmd5j_U','102iMMbfkInQwvIz8sLpz6AVgOhJFLcqo','10kZHAmyTpSWWVznqyWeSTYVJXq76cxmq','1-v2yDhV_f-eUSokNaNuy-iqLlXb2aQk-','108F-u0xgjGckwaprGidkP_PFUaiStEro','10taj31jPdDCBdla-2obITSn9LlllNC7D','111_rEBLm0V3yLZjBiAazUd25qDbwVE-O','10K1KGWjKeEOvvxjY5SHDBxOQNWWGCKmm','10YelrSn-sjGptw2ns5uZUEMSa58VNmF1'] |
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_BASE_DATA_URL = "https://drive.google.com/file/d/" |
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class BrwacCleanConfig(datasets.BuilderConfig): |
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"""BRWAC-clean corpus.""" |
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def __init__(self, **kwargs): |
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name = "brwac-clean" |
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description = "brwac-clean dataset" |
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super(BrwacCleanConfig, self).__init__(name=name, description=description, **kwargs) |
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self.base_data_url = _BASE_DATA_URL |
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class BrwacClean(datasets.GeneratorBasedBuilder): |
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"""BRWAC corpus.""" |
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BUILDER_CONFIGS = [ |
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BrwacCleanConfig( |
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version=datasets.Version("1.0.0"), |
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) |
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] |
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BUILDER_CONFIG_CLASS = BrwacCleanConfig |
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def _info(self): |
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return datasets.DatasetInfo( |
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features=datasets.Features({"id": datasets.Value("int64"), "text": datasets.Value("string")}), |
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supervised_keys=None, |
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) |
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def _split_generators(self, dl_manager): |
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data_urls = [self.config.base_data_url + data_filename for data_filename in _FILES] |
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downloaded_files = dl_manager.download(data_urls) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files}), |
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] |
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def _generate_examples(self, filepaths): |
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"""This function returns the examples in the raw (text) form by iterating on all the files.""" |
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id_ = 0 |
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for filepath in filepaths: |
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with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f: |
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for line in f: |
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feature = id_, {"id": id_, "text": line.replace("<END>", "\n").rstrip()} |
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yield feature |
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id_ += 1 |
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