import json import os import datasets class ERRNewsConfig(datasets.BuilderConfig): def __init__(self, data_url, features, **kwargs): super().__init__(version=datasets.Version("1.0.0"), **kwargs) self.features = features self.data_url = data_url class ERRNews(datasets.GeneratorBasedBuilder): features = ["transcript", "summary", "id"] data_url = "https://cs.taltech.ee/staff/heharm/AMIsum/" BUILDER_CONFIGS = [ ERRNewsConfig( name="full", features=features, data_url=data_url ) ] DEFAULT_CONFIG_NAME = "full" def _info(self): features = datasets.Features( { "transcript": datasets.Value("string"), "summary": datasets.Value("string"), "id": datasets.Value("string"), }) description = """\ AMI Summarization Dataset (AMIsum) is a meeting summarization dataset, consisting of meeting transcripts \ and abstract summaries from the AMI Corpus: https://groups.inf.ed.ac.uk/ami/corpus/. """ return datasets.DatasetInfo( features=features, description=description, supervised_keys=None, version=self.config.version, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" train = "train.json" test = "test.json" val = "val.json" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "file_path": dl_manager.download(self.config.data_url + train), }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "file_path": dl_manager.download(self.config.data_url + val), }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "file_path": dl_manager.download(self.config.data_url + test), }, ), ] def create_dict(self, data): res = dict() for key in self.config.features: res[key] = data[key] return res def _generate_examples(self, file_path): with open(file_path) as f: data = json.load(f) for idx, transcript in enumerate(data["transcript"]): id_ = data["id"][idx] yield id_, { "transcript": transcript, "summary": data["summary"][idx], "id": id_, }