| import datasets |
| import os |
| from datasets import load_dataset |
| import gzip |
| import json |
|
|
| logger = datasets.logging.get_logger(__name__) |
|
|
|
|
| CITATION = """ |
| """ |
|
|
| DESCRIPTION = """ |
| The Open License Corpus |
| """ |
|
|
| OLC_SUBSET_NAMES = [ |
| "ccby_law", |
| "ccby_s2orc", |
| "ccby_stackexchange", |
| "ccby_stackoverflow", |
| "ccby_wikinews", |
| "ccby_wikipedia", |
| "pd_arxiv_abstracts", |
| "pd_books", |
| "pd_law", |
| "pd_news", |
| "pd_s2orc", |
| "sw_amps_math", |
| "sw_dm_math", |
| "sw_github", |
| "sw_hackernews", |
| "sw_ubuntu_irc" |
| ] |
|
|
| URL = "https://huggingface.co/datasets/kernelmachine/open-license-corpus/" |
|
|
|
|
|
|
| N_SHARDS_PER_SPLIT = { |
| "ccby_s2orc": {"train": 5000}, |
| "ccby_law": {"train": 50}, |
| "ccby_stackexchange": {"train": 1500}, |
| "ccby_stackoverflow": {"train": 750}, |
| "ccby_wikinews": {"train": 42}, |
| "ccby_wikipedia": {"train": 3000}, |
| "pd_arxiv_abstracts": {"train": 1}, |
| "pd_books": {"train": 150}, |
| "pd_law": {"train": 2000}, |
| "pd_news": {"train": 10}, |
| "pd_s2orc": {"train": 30}, |
| "sw_amps_math": {"train": 2}, |
| "sw_dm_math": {"train": 239}, |
| "sw_github": {"train": 2500}, |
| "sw_hackernews": {"train": 16}, |
| "sw_ubuntu_irc": {"train": 27} |
| } |
|
|
| |
| DATA_URL = 'https://huggingface.co/datasets/kernelmachine/open-license-corpus/resolve/main/data/{name}/{split}-{index:05d}-of-{n_shards:05d}.jsonl.gz' |
|
|
| class OpenLicenseCorpusConfig(datasets.BuilderConfig): |
| def __init__(self, features, citation, **kwargs): |
| super().__init__(**kwargs) |
|
|
|
|
| class OpenLicenseCorpus(datasets.GeneratorBasedBuilder): |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig(name=name) |
| for name in OLC_SUBSET_NAMES |
| ] |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=DESCRIPTION, |
| features=datasets.Features( |
| { |
| "text": datasets.Value("string"), |
| } |
| ), |
| supervised_keys=None, |
| homepage=URL, |
| citation=CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| data_urls = {} |
| for split in ["train"]: |
| n_shards = N_SHARDS_PER_SPLIT[self.config.name][split] - 1 |
| data_urls[split] = [ |
| DATA_URL.format(name=self.config.name, split=split, index=index, n_shards=n_shards) |
| for index in range(n_shards) |
| ] |
|
|
| train_downloaded_files = dl_manager.download(data_urls["train"]) |
| |
| return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files})] |
|
|
| def _generate_examples(self, filepaths): |
| """This function returns the examples in the raw (text) form by iterating on all the files.""" |
| id_ = 0 |
| for filepath in filepaths: |
| logger.info("generating examples from = %s", filepath) |
| with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f: |
| for line in f: |
| if line: |
| example = json.loads(line) |
| yield id_, example |
| id_ += 1 |
|
|
