"""TODO(discofuse): Add a description here.""" import csv import os import datasets _URL_ = "https://storage.googleapis.com/gresearch/discofuse/" _CITATION = """\ @InProceedings{GevaEtAl2019, title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion}, author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan}, booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics}, note = {arXiv preprint arXiv:1902.10526}, year = {2019} } """ # TODO(discofuse): _DESCRIPTION = """\ DISCOFUSE is a large scale dataset for discourse-based sentence fusion. """ class DiscofuseConfig(datasets.BuilderConfig): """BuilderConfig for Discofuse""" def __init__(self, data_url, balanced=False, **kwargs): """ Args: balanced: to specify if we want to load the balanced file or the full file **kwargs: keyword arguments forwarded to super. """ super(DiscofuseConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) self.balanced = balanced self.data_url = data_url class Discofuse(datasets.GeneratorBasedBuilder): """TODO(discofuse): Short description of my dataset.""" # TODO(discofuse): Set up version. VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ DiscofuseConfig( name="discofuse-sport", description="sentence fusion", data_url=_URL_ + "discofuse_v1_sports.zip" ), DiscofuseConfig( name="discofuse-wikipedia", description="sentence fusion", data_url=_URL_ + "discofuse_v1_wikipedia.zip" ), ] def _info(self): # TODO(discofuse): Specifies the datasets.DatasetInfo object return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # datasets.features.FeatureConnectors features=datasets.Features( { "connective_string": datasets.Value("string"), "discourse_type": datasets.Value("string"), "coherent_second_sentence": datasets.Value("string"), "has_coref_type_pronoun": datasets.Value("float32"), "incoherent_first_sentence": datasets.Value("string"), "incoherent_second_sentence": datasets.Value("string"), "has_coref_type_nominal": datasets.Value("float32"), "coherent_first_sentence": datasets.Value("string"), # These are the features of your dataset like images, labels ... } ), # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage="https://github.com/google-research-datasets/discofuse", citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # TODO(discofuse): Downloads the data and defines the splits # dl_manager is a datasets.download.DownloadManager that can be used to # download and extract URLs if self.config.name == "discofuse-sport": dl_dir = dl_manager.download_and_extract(self.config.data_url) data_dir = os.path.join(dl_dir, "discofuse_v1/sports") if self.config.balanced: return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "train_balanced.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "test_balanced.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "dev_balanced.tsv")}, ), ] else: return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "dev.tsv")}, ), ] else: if self.config.name == "discofuse-wikipedia": dl_dir = dl_manager.download_and_extract(self.config.data_url) data_dir = os.path.join(dl_dir, "discofuse_v1/wikipedia") if self.config.balanced: return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "train_balanced.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "test_balanced.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "dev_balanced.tsv")}, ), ] else: return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "dev.tsv")}, ), ] def _generate_examples(self, filepath): """Yields examples.""" # TODO(discofuse): Yields (key, example) tuples from the dataset with open(filepath, encoding="utf-8") as f: data = csv.DictReader(f, delimiter="\t") for id_, row in enumerate(data): co_first_sent = row["coherent_first_sentence"] co_second_sent = row["coherent_second_sentence"] connect_str = row["connective_string"] discourse_type = row["discourse_type"] has_coref_pronoun = row["has_coref_type_pronoun"] has_coref_nominal = row["has_coref_type_nominal"] inco_first_sent = row["incoherent_first_sentence"] inco_second_sent = row["incoherent_second_sentence"] yield id_, { "connective_string": connect_str, "discourse_type": discourse_type, "coherent_second_sentence": co_second_sent, "has_coref_type_pronoun": has_coref_pronoun, "incoherent_first_sentence": inco_first_sent, "incoherent_second_sentence": inco_second_sent, "has_coref_type_nominal": has_coref_nominal, "coherent_first_sentence": co_first_sent, }