import datasets as ds import pandas as pd _CITATION = """\ @article{yanaka-mineshima-2022-compositional, title = "Compositional Evaluation on {J}apanese Textual Entailment and Similarity", author = "Yanaka, Hitomi and Mineshima, Koji", journal = "Transactions of the Association for Computational Linguistics", volume = "10", year = "2022", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/2022.tacl-1.73", doi = "10.1162/tacl_a_00518", pages = "1266--1284", } """ _DESCRIPTION = """\ Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset. JSICK is the Japanese NLI and STS dataset by manually translating the English dataset SICK (Marelli et al., 2014) into Japanese. We hope that our dataset will be useful in research for realizing more advanced models that are capable of appropriately performing multilingual compositional inference. (from official website) """ _HOMEPAGE = "https://github.com/verypluming/JSICK" _LICENSE = "CC BY-SA 4.0" _URLS = { "base": "https://raw.githubusercontent.com/verypluming/JSICK/main/jsick/jsick.tsv", "stress": "https://raw.githubusercontent.com/verypluming/JSICK/main/jsick-stress/jsick-stress-all-annotations.tsv", } class JSICKDataset(ds.GeneratorBasedBuilder): VERSION = ds.Version("1.0.0") DEFAULT_CONFIG_NAME = "base" BUILDER_CONFIGS = [ ds.BuilderConfig( name="base", version=VERSION, description="hoge", ), ds.BuilderConfig( name="original", version=VERSION, description="hoge", ), ds.BuilderConfig( name="stress", version=VERSION, description="fuga", ), ds.BuilderConfig( name="stress-original", version=VERSION, description="fuga", ), ] def _info(self) -> ds.DatasetInfo: labels = ds.ClassLabel(names=["entailment", "neutral", "contradiction"]) if self.config.name == "base": features = ds.Features( { "id": ds.Value("int32"), "premise": ds.Value("string"), "hypothesis": ds.Value("string"), "label": labels, "score": ds.Value("float32"), "sentence_A_En": ds.Value("string"), "sentence_B_En": ds.Value("string"), "entailment_label_En": labels, "relatedness_score_En": ds.Value("float32"), "corr_entailment_labelAB_En": ds.Value("string"), "corr_entailment_labelBA_En": ds.Value("string"), "image_ID": ds.Value("string"), "original_caption": ds.Value("string"), "semtag_short": ds.Value("string"), "semtag_long": ds.Value("string"), } ) elif self.config.name == "original": features = ds.Features( { "pair_ID": ds.Value("int32"), "sentence_A_Ja": ds.Value("string"), "sentence_B_Ja": ds.Value("string"), "entailment_label_Ja": labels, "relatedness_score_Ja": ds.Value("float32"), "sentence_A_En": ds.Value("string"), "sentence_B_En": ds.Value("string"), "entailment_label_En": labels, "relatedness_score_En": ds.Value("float32"), "corr_entailment_labelAB_En": ds.Value("string"), "corr_entailment_labelBA_En": ds.Value("string"), "image_ID": ds.Value("string"), "original_caption": ds.Value("string"), "semtag_short": ds.Value("string"), "semtag_long": ds.Value("string"), } ) elif self.config.name == "stress": features = ds.Features( { "id": ds.Value("string"), "premise": ds.Value("string"), "hypothesis": ds.Value("string"), "label": labels, "score": ds.Value("float32"), "sentence_A_Ja_origin": ds.Value("string"), "entailment_label_origin": labels, "relatedness_score_Ja_origin": ds.Value("float32"), "rephrase_type": ds.Value("string"), "case_particles": ds.Value("string"), } ) elif self.config.name == "stress_original": features = ds.Features( { "pair_ID": ds.Value("string"), "sentence_A_Ja": ds.Value("string"), "sentence_B_Ja": ds.Value("string"), "entailment_label_Ja": labels, "relatedness_score_Ja": ds.Value("float32"), "sentence_A_Ja_origin": ds.Value("string"), "entailment_label_origin": labels, "relatedness_score_Ja_origin": ds.Value("float32"), "rephrase_type": ds.Value("string"), "case_particles": ds.Value("string"), } ) return ds.DatasetInfo( description=_DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, license=_LICENSE, features=features, ) def _split_generators(self, dl_manager: ds.DownloadManager): data_path = dl_manager.download_and_extract(_URLS[self.config.name]) df: pd.DataFrame = pd.read_table(data_path, sep="\t", header=0) if self.config.name in ["stress", "stress_original"]: df = df[ [ "pair_ID", "sentence_A_Ja", "sentence_B_Ja", "entailment_label_Ja", "relatedness_score_Ja", "sentence_A_Ja_origin", "entailment_label_origin", "relatedness_score_Ja_origin", "rephrase_type", "case_particles", ] ] if self.config.name in ["base", "stress"]: df = df.rename( columns={ "pair_ID": "id", "sentence_A_Ja": "premise", "sentence_B_Ja": "hypothesis", "entailment_label_Ja": "label", "relatedness_score_Ja": "score", } ) if self.config.name in ["base", "original"]: return [ ds.SplitGenerator( name=ds.Split.TRAIN, gen_kwargs={"df": df[df["data"] == "train"].drop("data", axis=1)}, ), ds.SplitGenerator( name=ds.Split.TEST, gen_kwargs={"df": df[df["data"] == "test"].drop("data", axis=1)}, ), ] elif self.config.name in ["stress", "stress_original"]: return [ ds.SplitGenerator( name=ds.Split.TEST, gen_kwargs={"df": df}, ), ] def _generate_examples(self, df: pd.DataFrame): for i, row in enumerate(df.to_dict("records")): yield i, row