:sparkles: Add jsick.py

jsick.py

@@ -0,0 +1,137 @@
+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

pyproject.toml

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+[tool.poetry]
+name = "datasets-jsick"
+version = "0.1.0"
+description = ""
+authors = ["hppRC <hpp.ricecake@gmail.com>"]
+readme = "README.md"
+packages = []
+
+[tool.poetry.dependencies]
+python = "^3.8.1"
+datasets = "^2.11.0"
+
+
+[tool.poetry.group.dev.dependencies]
+black = "^22.12.0"
+isort = "^5.11.4"
+flake8 = "^6.0.0"
+mypy = "^0.991"
+pytest = "^7.2.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"