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import datasets |
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import os |
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from datasets import load_dataset |
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import gzip |
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import json |
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logger = datasets.logging.get_logger(__name__) |
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CITATION = """ |
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""" |
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DESCRIPTION = """ |
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The Open License Corpus |
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""" |
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OLC_SUBSET_NAMES = [ |
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"ccby_law", |
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"ccby_s2orc", |
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"ccby_stackexchange", |
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"ccby_stackoverflow", |
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"ccby_wikinews", |
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"ccby_wikipedia", |
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"pd_arxiv_abstracts", |
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"pd_books", |
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"pd_law", |
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"pd_news", |
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"pd_s2orc", |
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"sw_amps_math", |
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"sw_dm_math", |
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"sw_github", |
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"sw_hackernews", |
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"sw_ubuntu_irc" |
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] |
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URL = "https://huggingface.co/datasets/kernelmachine/open-license-corpus/" |
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N_SHARDS_PER_SPLIT = { |
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"ccby_s2orc": {"train": 5000}, |
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"ccby_law": {"train": 50}, |
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"ccby_stackexchange": {"train": 1500}, |
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"ccby_stackoverflow": {"train": 750}, |
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"ccby_wikinews": {"train": 42}, |
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"ccby_wikipedia": {"train": 3000}, |
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"pd_arxiv_abstracts": {"train": 1}, |
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"pd_books": {"train": 150}, |
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"pd_law": {"train": 2000}, |
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"pd_news": {"train": 10}, |
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"pd_s2orc": {"train": 30}, |
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"sw_amps_math": {"train": 2}, |
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"sw_dm_math": {"train": 239}, |
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"sw_github": {"train": 2500}, |
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"sw_hackernews": {"train": 16}, |
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"sw_ubuntu_irc": {"train": 27} |
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} |
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DATA_URL = 'https://huggingface.co/datasets/kernelmachine/open-license-corpus/resolve/main/data/{name}/{split}-{index:05d}-of-{n_shards:05d}.jsonl.gz' |
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class OpenLicenseCorpusConfig(datasets.BuilderConfig): |
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def __init__(self, features, citation, **kwargs): |
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super().__init__(**kwargs) |
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class OpenLicenseCorpus(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name=name) |
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for name in OLC_SUBSET_NAMES |
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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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} |
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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"]: |
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n_shards = N_SHARDS_PER_SPLIT[self.config.name][split] - 1 |
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data_urls[split] = [ |
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DATA_URL.format(name=self.config.name, split=split, index=index, n_shards=n_shards) |
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for index in range(n_shards) |
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] |
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train_downloaded_files = dl_manager.download(data_urls["train"]) |
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files})] |
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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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