import datasets import os from datasets import load_dataset CITATION = """ @inproceedings{reid2022m2d2, title={ {M2D2}: A Massively Multi-Domain Language Modeling Dataset }, author={ Machel Reid and Victor Zhong and Suchin Gururangan and Luke Zettlemoyer }, booktitle={ EMNLP }, year={ 2022 } } """ DESCRIPTION = """ M2D2 dataset from 'M2D2: A Massively Multi-Domain Language Modeling Dataset' """ FEATURES = datasets.Features({"text": datasets.Value("string")}) def _URLS(split): return f"https://hugginface.co/datasets/machelreid/m2d2/resolve/main/data/{split}.tar.gz" with open("split_names.txt", "r") as f: M2D2_SPLIT_NAMES = [i.strip() for i in f.readlines()] class M2D2Config(datasets.BuilderConfig): def __init__(self, features, citation, **kwargs): super().__init__(**kwargs) self.features = features self.citation = citation class M2D2(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ M2D2Config(name=name, features=FEATURES, citation=CITATION) for name in M2D2_SPLIT_NAMES ] def _info(self): return datasets.DatasetInfo( description=DESCRIPTION, citation=CITATION, features=FEATURES ) def _split_generators(self, dl_manager): urls = _URLS(self.config.name) data_dir = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, "train.txt"), }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": os.path.join(data_dir, "valid.txt"), }, ), datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, "test.txt"), }, ), ] def _generate_examples(self, filepath): with open(filepath, encoding="utf-8") as f: for key, row in enumerate(f): data = row.strip() yield key, data