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"""Cleaned Indonesian split of the mC4 corpus.""" |
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import json |
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import glob |
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import gzip |
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import textwrap |
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import zstandard as zstd |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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file = sorted(glob.glob('/data/oscar_2021_21_raw/*.zst')) |
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_CITATION = """ |
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@article{JMLR:v21:20-074, |
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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 = {Journal of Machine Learning Research}, |
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year = {2020}, |
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volume = {21}, |
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number = {140}, |
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pages = {1-67}, |
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url = {http://jmlr.org/papers/v21/20-074.html} |
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} |
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""" |
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_DESCRIPTION = """\ |
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A thoroughly cleaned version of the Italian portion of the multilingual |
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colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI. |
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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, with further cleaning |
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detailed in the repository README file. |
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""" |
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_HOMEPAGE = "https://github.com/allenai/allennlp/discussions/5056" |
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_LICENSE = "Open Data Commons Attribution License (ODC-By) v1.0" |
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_BASE_URL = "https://huggingface.co/datasets/munggok/mc4-id/resolve/main/mc4-id-filter/c4-id{split_suffix}.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz" |
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_CONFIGS = { |
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"tiny": {"train": 100, "validation": 1}, |
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"small": {"train": 250, "validation": 2}, |
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"medium": {"train": 500, "validation": 4}, |
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"large": {"train": 750, "validation": 6}, |
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"full": {"train": 1016, "validation": 8} |
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} |
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class OscarConfig(datasets.BuilderConfig): |
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"""BuilderConfig for the Clean mC4 Italian.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Clean mC4 Italian. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(**kwargs) |
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class Oscar(datasets.GeneratorBasedBuilder): |
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"""mC4, a colossal, cleaned version of Common Crawl's web crawl corpus.""" |
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BUILDER_CONFIGS = [ |
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OscarConfig( |
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name="full", |
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version=datasets.Version("1.0.0"), |
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description=textwrap.dedent( |
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f"""\ |
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The full cleaned version of the Italian portion of the multilingual C4 corpus. |
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Estimated size of compressed files: 103GB |
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""" |
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) |
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) |
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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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"text": datasets.Value("string"), |
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"url": datasets.Value("string"), |
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"timestamp": datasets.Value("string"), |
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"meta": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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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_urls = {} |
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train_downloaded_files = file |
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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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] |
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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(f"Generating examples from {filepath}") |
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with zstd.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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meta = dict() |
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meta["warc_headers"] = example["warc_headers"] |
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meta["warc_headers"]["warc-identified-content-language"] = example[ |
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"warc_headers" |
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].get("warc-identified-content-language") |
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meta["identification"] = example["metadata"]["identification"] |
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meta["annotations"] = example["metadata"]["annotation"] |
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meta["line_identifications"] = example["metadata"][ |
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"sentence_identifications" |
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] |
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yield id_, {'text':example['content'],'url':example['warc_headers']['warc-target-uri'],'timestamp':example['warc_headers']['warc-date'],"meta": json.dumps(meta)} |
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id_ += 1 |
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