from datasets import Value, ClassLabel,Sequence import datasets _SNAP10K_CITATION = """\ """ _SNAP10K_DESCRIPTION = """\ GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems. """ class SNAP10KConfig(datasets.BuilderConfig): def __init__( self, text_features, label_column, data_url, data_dir, citation, url, label_classes=None, process_label=lambda x: x, **kwargs, ): super(SNAP10KConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) self.text_features = text_features self.label_column = label_column self.label_classes = label_classes self.data_url = data_url self.data_dir = data_dir self.citation = citation self.url = url self.process_label = process_label class SNAP10K(datasets.GeneratorBasedBuilder): domain_list = ['Automotive_5', 'Electronics_5', 'Industrial_and_Scientific_5', 'Kindle_Store_5', 'Cell_Phones_and_Accessories_5', 'Musical_Instruments_5', 'Office_Products_5', 'Patio_Lawn_and_Garden_5', 'Sports_and_Outdoors_5', 'Luxury_Beauty_5', 'Grocery_and_Gourmet_Food_5', 'Digital_Music_5', 'Tools_and_Home_Improvement_5', 'Pet_Supplies_5', 'Prime_Pantry_5', 'Toys_and_Games_5', 'Movies_and_TV_5', 'Home_and_Kitchen_5', 'Arts_Crafts_and_Sewing_5', 'Video_Games_5', 'CDs_and_Vinyl_5'] BUILDER_CONFIGS = [ SNAP10KConfig(name=domain_name, description= f'comments of {domain_name}.', text_features={'sentence':'sentence', 'domain':'domain'}, label_classes=['POS','NEG'], label_column='label', citation="", data_dir= "", data_url = r"https://huggingface.co/datasets/kuroneko3578/snap21/resolve/main/", url='https://github.com/ws719547997/LNB-DA') for domain_name in domain_list ] def _info(self): features = {'id':Value(dtype='int32', id=None), 'domain':Value(dtype='string', id=None), 'label':ClassLabel(num_classes=2, names=['POS', 'NEG'], names_file=None, id=None), 'rank':Value(dtype='string', id=None), 'sentence':Value(dtype='string', id=None)} return datasets.DatasetInfo( description=_SNAP10K_DESCRIPTION, features=datasets.Features(features), homepage=self.config.url, citation=self.config.citation + "\n" + _SNAP10K_CITATION, ) def _split_generators(self, dl_manager): test_file = rf'{self.config.data_url}test/{self.config.name}.txt' dev_file = rf'{self.config.data_url}dev/{self.config.name}.txt' train_file = rf'{self.config.data_url}train/{self.config.name}.txt' return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={ "data_file": dl_manager.download(test_file), "split": "test", },), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={ "data_file": dl_manager.download(dev_file), "split": "dev", },), datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ "data_file": dl_manager.download(train_file), "split": "train", },)] def _generate_examples(self, data_file, split): with open(data_file, 'r', encoding='utf-8') as f: for line in f: lin = line.strip() if not lin: continue lin_sp = lin.split('\t') if len(lin_sp) < 5: continue # id, {example} yield lin_sp[0], {'sentence':lin_sp[4],'domain':lin_sp[1], 'label':lin_sp[2], 'id':lin_sp[0], 'rank':lin_sp[3]}