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"""mC4 dataset based on Common Crawl.""" |
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import gzip |
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import json |
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import warnings |
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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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A colossal, cleaned version of Common Crawl's web crawl corpus. |
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Based on Common Crawl dataset: "https://commoncrawl.org". |
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This is the processed version of Google's mC4 dataset by AllenAI. |
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""" |
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_CITATION = """ |
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@article{2019t5, |
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author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, |
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title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, |
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journal = {arXiv e-prints}, |
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year = {2019}, |
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archivePrefix = {arXiv}, |
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eprint = {1910.10683}, |
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} |
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""" |
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_URL = "https://github.com/allenai/allennlp/discussions/5056" |
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_DATA_URL = "https://huggingface.co/datasets/allenai/c4/resolve/main/multilingual/c4-{language}.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz" |
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_LANGUAGES = ["fr", "en"] |
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_N_SHARDS_PER_SPLIT = { |
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"en": {"train": 11264, "validation": 128}, |
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"fr": {"train": 2048, "validation": 16}, |
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} |
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_N_SHARDS_PER_SPLIT_CUSTOMISED = { |
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"en": {"train": 2, "validation": 10}, |
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"fr": {"train": 2, "validation": 10}, |
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} |
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class Mc4Config(datasets.BuilderConfig): |
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"""BuilderConfig for mC4.""" |
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def __init__(self, *args, languages, **kwargs): |
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"""BuilderConfig for mC4. |
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Args: |
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languages (:obj:`List[str]`): list of languages to load |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__( |
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*args, |
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name="+".join(languages), |
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**kwargs, |
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) |
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self.languages = languages |
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class Mc4(datasets.GeneratorBasedBuilder): |
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"""mC4, a colossal, cleaned version of Common Crawl's web crawl corpus.""" |
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BUILDER_CONFIGS = [Mc4Config(languages=[lang]) for lang in _LANGUAGES] |
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BUILDER_CONFIG_CLASS = Mc4Config |
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def _info(self): |
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warnings.warn( |
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"Dataset 'mc4' is deprecated and will be deleted. Use 'allenai/c4' instead.", |
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FutureWarning, |
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) |
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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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"text": datasets.Value("string"), |
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"timestamp": datasets.Value("string"), |
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"url": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage=_URL, |
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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_urls = {} |
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for split in ["train", "validation"]: |
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data_urls[split] = [ |
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_DATA_URL.format( |
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language=lang, |
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split_suffix="-validation" if split == "validation" else "", |
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index=index, |
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n_shards=_N_SHARDS_PER_SPLIT[lang][split], |
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) |
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for lang in self.config.languages |
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for index in range(_N_SHARDS_PER_SPLIT_CUSTOMISED[lang][split]) |
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] |
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train_downloaded_files = dl_manager.download(data_urls["train"]) |
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validation_downloaded_files = dl_manager.download(data_urls["validation"]) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_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 by iterating on all the files.""" |
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id_ = 0 |
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for filepath in filepaths: |
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logger.info("generating examples from = %s", filepath) |
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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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if line: |
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example = json.loads(line) |
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yield id_, example |
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id_ += 1 |
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