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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = """\ |
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Every prompt dataset. |
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Every Prompt is a data-driven approach to mining instructions from the web. |
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It contains over a million FAQs and HowTos from around the world in a structured format. |
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It also has basic pre-processing to calculate the length of the useful text and identify the language of that text with the help of GCLD3 |
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""" |
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_URLS = [ |
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"every_prompt.jsonlines", |
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] |
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class EveryPromptDataset(datasets.GeneratorBasedBuilder): |
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"""Every Prompt Dataset""" |
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VERSION = datasets.Version("1.0.0") |
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DEFAULT_CONFIG_NAME = "default" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="default", version=VERSION, description=""), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"language": datasets.Value("string"), |
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"language_is_reliable": datasets.Value("bool"), |
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"text_length": datasets.Value("int32"), |
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"data_length": datasets.Value("int32"), |
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"text_to_data_ratio": datasets.Value("float32"), |
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"url": datasets.Value("string"), |
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"schema_type": datasets.Value("string"), |
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"payload": datasets.Value("string"), |
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} |
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), |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download(_URLS) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files} |
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) |
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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.""" |
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logger.info("generating examples from = %s", filepaths) |
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key = 0 |
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for path in filepaths: |
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with open(path, encoding="utf-8") as f: |
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for instruction_str in f: |
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instruction = json.loads(instruction_str) |
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yield key, instruction |
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key += 1 |
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