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 = """\

	"""

	_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 ["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 == "stress":
	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",
	]
	]
	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