import os import datasets import pandas as pd class semiHomoConfig(datasets.BuilderConfig): def __init__(self, features, data_url, **kwargs): super(semiHomoConfig, self).__init__(**kwargs) self.features = features self.data_url = data_url class semiHomo(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ semiHomoConfig( name="pairs", features={ "ltable_id": datasets.Value("string"), "rtable_id": datasets.Value("string"), "label": datasets.Value("string"), }, data_url="https://huggingface.co/datasets/matchbench/semi-Homo/resolve/main/", ), ] def _info(self): return datasets.DatasetInfo( features=datasets.Features(self.config.features) ) def _split_generators(self, dl_manager): if self.config.name == "pairs": return [ datasets.SplitGenerator( name=split, gen_kwargs={ "path_file": dl_manager.download_and_extract( os.path.join(self.config.data_url, f"{split}.csv")), "split": split, } ) for split in ["train", "valid", "test"] ] def _generate_examples(self, path_file, split): file = pd.read_csv(path_file) for i, row in file.iterrows(): if split in ['train', 'valid','test']: yield i, { "ltable_id": row["ltable_id"], "rtable_id": row["rtable_id"], "label": row["label"], }