Datasets:

hpprc
/

jsick

	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="A version adopting the column names of a typical NLI dataset.",
	),
	ds.BuilderConfig(
	name="original",
	version=VERSION,
	description="The original version retaining the unaltered column names.",
	),
	ds.BuilderConfig(
	name="stress",
	version=VERSION,
	description="The dataset to investigate whether models capture word order and case particles in Japanese.",
	),
	ds.BuilderConfig(
	name="stress-original",
	version=VERSION,
	description="The original version of JSICK-stress Test set retaining the unaltered column names.",
	),
	]

	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"),
	"premise_en": ds.Value("string"),
	"hypothesis_en": ds.Value("string"),
	"label_en": labels,
	"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):
	if self.config.name in ["base", "original"]:
	url = _URLS["base"]
	elif self.config.name in ["stress", "stress-original"]:
	url = _URLS["stress"]

	data_path = dl_manager.download_and_extract(url)
	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",
	"sentence_A_En": "premise_en",
	"sentence_B_En": "hypothesis_en",
	"entailment_label_En": "label_en",
	"relatedness_score_En": "score_en",
	}
	)

	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