Datasets:
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License:
File size: 4,784 Bytes
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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="stress",
version=VERSION,
description="fuga",
),
]
def _info(self) -> ds.DatasetInfo:
labels = ds.ClassLabel(names=["entailment", "neutral", "contradiction"])
if self.config.name == "base":
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(
{
"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 == "base":
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
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