from pandas import read_csv from datasets import GeneratorBasedBuilder, Value, Version, BuilderConfig, Features, DatasetInfo, SplitGenerator, Split _DESCRIPTION = ''' This dataset contains transcribed sounds emitted by characters of the League of Legends game. The data can be useful for building text classification models, fine-tuning language generation models, speech synthesis and speech recognition models. The underlying web dump for the dataset construction has been last refreshed **20.10.2023** ''' _HOMEPAGE = 'https://huggingface.co/datasets/zeio/pale' _LICENSE = 'Apache License Version 2.0' _URLS = { 'full': 'https://huggingface.co/datasets/zeio/pale/resolve/main/pale.tsv', 'quotes': 'https://huggingface.co/datasets/zeio/pale/resolve/main/quotes.tsv', 'annotated': 'https://huggingface.co/datasets/zeio/pale/resolve/main/annotated.tsv' } class Pale(GeneratorBasedBuilder): VERSION = Version('27.10.2023') BUILDER_CONFIGS = [ BuilderConfig(name = 'quotes', version = VERSION, description = 'Truncated version of the corpus, which does\'t contain sound effects'), BuilderConfig(name = 'annotated', version = VERSION, description = 'An extended version of the full configuration with a couple of additional columns with labels'), BuilderConfig(name = 'vanilla', version = VERSION, description = 'All data pulled from the website without significant modifications apart from the web page structure parsing') ] DEFAULT_CONFIG_NAME = 'quotes' def _info(self): if self.config.name == 'vanilla': features = Features({ 'header': Value('string'), 'subheader': Value('string'), 'text': Value('string'), 'source': Value('string'), 'champion': Value('string') }) elif self.config.name == 'annotated': features = Features({ 'header': Value('string'), 'subheader': Value('string'), 'text': Value('string'), 'source': Value('string'), 'champion': Value('string'), 'quote': Value('bool') }) elif self.config.name == 'quotes': features = Features({ 'header': Value('string'), 'subheader': Value('string'), 'text': Value('string'), 'champion': Value('string') }) return DatasetInfo( description=_DESCRIPTION, features = features, homepage=_HOMEPAGE, license=_LICENSE ) def _split_generators(self, dl_manager): name = self.config.name url = _URLS[name] return [ SplitGenerator( name = Split.TRAIN, gen_kwargs = { "path": dl_manager.download_and_extract(url) } ) ] def _generate_examples(self, path: str): for i, row in read_csv(path, sep = '\t').iterrows(): yield i, dict(row)