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"""RedPajama: An Open-Source, Clean-Room 1.2 Trillion Token Dataset.""" |
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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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RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. |
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""" |
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_URL_LISTS = { |
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"arxiv": "urls/arxiv.txt", |
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"book": "urls/book.txt", |
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"c4": "urls/c4.txt", |
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"common_crawl": "urls/common_crawl.txt", |
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"github": "urls/github.txt", |
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"stackexchange": "urls/stackexchange.txt", |
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"wikipedia": "urls/wikipedia.txt", |
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} |
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class RedPajama1TConfig(datasets.BuilderConfig): |
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"""BuilderConfig for RedPajama sample.""" |
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def __init__(self, *args, subsets, **kwargs): |
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"""BuilderConfig for RedPajama. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(RedPajama1TConfig, self).__init__(**kwargs) |
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self.subsets = subsets |
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class RedPajama1T(datasets.GeneratorBasedBuilder): |
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"""RedPajama: Reproducing the LLaMA training dataset of over 1.2 trillion tokens. Version 1.0.0.""" |
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BUILDER_CONFIGS = [ |
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RedPajama1TConfig( |
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subsets = list(_URL_LISTS.keys()), |
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name="plain_text", |
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version=datasets.Version("1.0.0", ""), |
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description="Plain text", |
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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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"meta": datasets.Value("string"), |
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"red_pajama_subset": datasets.Value("string"), |
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} |
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), |
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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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url_lists = dl_manager.download_and_extract({ |
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subset: _URL_LISTS[subset] for subset in self.config.subsets |
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}) |
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urls = {} |
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for subset, url_list in url_lists.items(): |
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with open(url_list, encoding="utf-8") as f: |
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urls[subset] = [line.strip() for line in f][:1] |
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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, |
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gen_kwargs = { |
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"files": { |
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subset: downloaded_files[subset] |
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for subset in self.config.subsets |
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} |
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} |
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) |
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] |
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def _generate_examples(self, files): |
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"""This function returns the examples in the raw (text) form.""" |
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key = 0 |
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for subset in files: |
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if subset == "common_crawl": |
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import zstandard as zstd |
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for path in files[subset]: |
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with zstd.open(open(path, "rb"), "rt", encoding="utf-8") as f: |
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for i, row in enumerate(f): |
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data = json.loads(row) |
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text = data["text"] |
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del data["text"] |
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yield key, { |
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"text": text, |
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"meta": json.dumps(data), |
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"red_pajama_subset": subset, |
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} |
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key += 1 |
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else: |
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for path in files[subset]: |
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with open(path, encoding="utf-8") as f: |
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for i, row in enumerate(f): |
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data = json.loads(row) |
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yield key, { |
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"text": data["text"], |
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"meta": data["meta"], |
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"red_pajama_subset": subset, |
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} |
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key += 1 |
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