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README.md

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----
-language:
-- ja
-language_creators:
-- other
-multilinguality:
-- monolingual
-pretty_name: JaNLI
-task_categories:
-- text-classification
-task_ids:
-- natural-language-inference
-license: cc-by-sa-4.0
----
-
-# Dataset Card for JaNLI
-
-## Table of Contents
-- [Dataset Card for JaNLI](#dataset-card-for-janli)
-  - [Table of Contents](#table-of-contents)
-  - [Dataset Description](#dataset-description)
-    - [Dataset Summary](#dataset-summary)
-    - [Languages](#languages)
-  - [Dataset Structure](#dataset-structure)
-    - [Data Instances](#data-instances)
-      - [base](#base)
-      - [original](#original)
-    - [Data Fields](#data-fields)
-      - [base](#base-1)
-      - [original](#original-1)
-    - [Data Splits](#data-splits)
-    - [Annotations](#annotations)
-  - [Additional Information](#additional-information)
-    - [Licensing Information](#licensing-information)
-    - [Citation Information](#citation-information)
-    - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** https://github.com/verypluming/JaNLI
-- **Repository:** https://github.com/verypluming/JaNLI
-- **Paper:** https://aclanthology.org/2021.blackboxnlp-1.26/
-
-### Dataset Summary
-
-The JaNLI (Japanese Adversarial NLI) dataset, inspired by the English HANS dataset, is designed to necessitate an understanding of Japanese linguistic phenomena and to illuminate the vulnerabilities of models.
-
-### Languages
-
-The language data in JaNLI is in Japanese (BCP-47 [ja-JP](https://www.rfc-editor.org/info/bcp47)).
-
-
-
-## Dataset Structure
-
-
-### Data Instances
-When loading a specific configuration, users has to append a version dependent suffix:
-
-```python
-import datasets as ds
-
-dataset: ds.DatasetDict = ds.load_dataset("hpprc/janli")
-print(dataset)
-# DatasetDict({
-#     train: Dataset({
-#         features: ['id', 'premise', 'hypothesis', 'label', 'heuristics', 'number_of_NPs', 'semtag'],
-#         num_rows: 13680
-#     })
-#     test: Dataset({
-#         features: ['id', 'premise', 'hypothesis', 'label', 'heuristics', 'number_of_NPs', 'semtag'],
-#         num_rows: 720
-#     })
-# })
-
-dataset: ds.DatasetDict = ds.load_dataset("hpprc/janli", name="original")
-print(dataset)
-# DatasetDict({
-#     train: Dataset({
-#         features: ['id', 'sentence_A_Ja', 'sentence_B_Ja', 'entailment_label_Ja', 'heuristics', 'number_of_NPs', 'semtag'],
-#         num_rows: 13680
-#     })
-#     test: Dataset({
-#         features: ['id', 'sentence_A_Ja', 'sentence_B_Ja', 'entailment_label_Ja', 'heuristics', 'number_of_NPs', 'semtag'],
-#         num_rows: 720
-#     })
-# })
-```
-
-
-#### base
-
-An example of looks as follows:
-
-```json
-{
-  'id': 12,
-  'premise': '若者がフットボール選手を見ている',
-  'hypothesis': 'フットボール選手を若者が見ている',
-  'label': 0,
-  'heuristics': 'overlap-full',
-  'number_of_NPs': 2,
-  'semtag': 'scrambling'
-}
-```
-
-#### original
-
-An example of looks as follows:
-
-```json
-{
-  'id': 12,
-  'sentence_A_Ja': '若者がフットボール選手を見ている',
-  'sentence_B_Ja': 'フットボール選手を若者が見ている',
-  'entailment_label_Ja': 0,
-  'heuristics': 'overlap-full',
-  'number_of_NPs': 2,
-  'semtag': 'scrambling'
-}
-```
-
-### Data Fields
-
-#### base
-
-A version adopting the column names of a typical NLI dataset.
-
-- `id`: The number of the sentence pair.
-- `premise`: The premise (sentence_A_Ja).
-- `hypothesis`: The hypothesis (sentence_B_Ja).
-- `label`: The correct label for this sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction (entailment_label_Ja).
-- `heuristics`: The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.
-- `number_of_NPs`: The number of noun phrase in a sentence.
-- `semtag`: The linguistic phenomena tag.
-
-#### original
-
-The original version retaining the unaltered column names.
-
-- `id`: The number of the sentence pair.
-- `sentence_A_Ja`: The premise.
-- `sentence_B_Ja`: The hypothesis.
-- `entailment_label_Ja`: The correct label for this sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction
-- `heuristics`: The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.
-- `number_of_NPs`: The number of noun phrase in a sentence.
-- `semtag`: The linguistic phenomena tag.
-
-
-### Data Splits
-
-| name     |  train | validation | test |
-| -------- | -----: | ---------: | ---: |
-| base     | 13,680 |            |  720 |
-| original | 13,680 |            |  720 |
-
-
-
-### Annotations
-
-The annotation process for this Japanese NLI dataset involves tagging each pair (P, H) of a premise and hypothesis with a label for structural pattern and linguistic phenomenon.
-The structural relationship between premise and hypothesis sentences is classified into five patterns, with each pattern associated with a type of heuristic that can lead to incorrect predictions of the entailment relation.
-Additionally, 11 categories of Japanese linguistic phenomena and constructions are focused on for generating the five patterns of adversarial inferences.
-
-For each linguistic phenomenon, a template for the premise sentence P is fixed, and multiple templates for hypothesis sentences H are created.
-In total, 144 templates for (P, H) pairs are produced.
-Each pair of premise and hypothesis sentences is tagged with an entailment label (entailment or non-entailment), a structural pattern, and a linguistic phenomenon label.
-
-The JaNLI dataset is generated by instantiating each template 100 times, resulting in a total of 14,400 examples.
-The same number of entailment and non-entailment examples are generated for each phenomenon.
-The structural patterns are annotated with the templates for each linguistic phenomenon, and the ratio of entailment and non-entailment examples is not necessarily 1:1 for each pattern.
-The dataset uses a total of 158 words (nouns and verbs), which occur more than 20 times in the JSICK and JSNLI datasets.
-
-
-## Additional Information
-
-- [verypluming/JaNLI](https://github.com/verypluming/JaNLI)
-- [Hitomi Yanaka, Koji Mineshima, Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference, Proceedings of the 2021 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP2021), 2021.](https://aclanthology.org/2021.blackboxnlp-1.26/)
-
-### Licensing Information
-
-CC BY-SA 4.0
-
-### Citation Information
-
-```bibtex
-@InProceedings{yanaka-EtAl:2021:blackbox,
-  author    = {Yanaka, Hitomi and Mineshima, Koji},
-  title     = {Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference},
-  booktitle = {Proceedings of the 2021 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP2021)},
-  url       = {https://aclanthology.org/2021.blackboxnlp-1.26/},
-  year      = {2021},
-}
-```
-
-### Contributions
-
-Thanks to [Hitomi Yanaka](https://hitomiyanaka.mystrikingly.com/) and Koji Mineshima for creating this dataset.

base/janli-test.parquet

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base/janli-train.parquet

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janli.py

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-import datasets as ds
-import pandas as pd
-
-_CITATION = """\
-@InProceedings{yanaka-EtAl:2021:blackbox,
-    author    = {Yanaka, Hitomi and Mineshima, Koji},
-    title     = {Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference},
-    booktitle = {Proceedings of the 2021 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP2021)},
-    year      = {2021},
-}
-"""
-
-_DESCRIPTION = "The JaNLI (Japanese Adversarial NLI) dataset, inspired by the English HANS dataset, is designed to necessitate an understanding of Japanese linguistic phenomena and to illuminate the vulnerabilities of models."
-
-_HOMEPAGE = "https://github.com/verypluming/JaNLI"
-
-_LICENSE = "CC BY-SA 4.0"
-
-_DOWNLOAD_URL = "https://raw.githubusercontent.com/verypluming/JaNLI/main/janli.tsv"
-
-
-class JaNLIDataset(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.",
-        ),
-    ]
-
-    def _info(self) -> ds.DatasetInfo:
-        if self.config.name == "base":
-            features = ds.Features(
-                {
-                    "id": ds.Value("int64"),
-                    "premise": ds.Value("string"),
-                    "hypothesis": ds.Value("string"),
-                    "label": ds.ClassLabel(names=["entailment", "non-entailment"]),
-                    "heuristics": ds.Value("string"),
-                    "number_of_NPs": ds.Value("int32"),
-                    "semtag": ds.Value("string"),
-                }
-            )
-        elif self.config.name == "original":
-            features = ds.Features(
-                {
-                    "id": ds.Value("int64"),
-                    "sentence_A_Ja": ds.Value("string"),
-                    "sentence_B_Ja": ds.Value("string"),
-                    "entailment_label_Ja": ds.ClassLabel(names=["entailment", "non-entailment"]),
-                    "heuristics": ds.Value("string"),
-                    "number_of_NPs": ds.Value("int32"),
-                    "semtag": 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(_DOWNLOAD_URL)
-        df: pd.DataFrame = pd.read_table(data_path, header=0, sep="\t", index_col=0)
-        df["id"] = df.index
-
-        if self.config.name == "base":
-            df = df.rename(
-                columns={
-                    "sentence_A_Ja": "premise",
-                    "sentence_B_Ja": "hypothesis",
-                    "entailment_label_Ja": "label",
-                }
-            )
-
-        return [
-            ds.SplitGenerator(
-                name=ds.Split.TRAIN,
-                gen_kwargs={"df": df[df["split"] == "train"]},
-            ),
-            ds.SplitGenerator(
-                name=ds.Split.TEST,
-                gen_kwargs={"df": df[df["split"] == "test"]},
-            ),
-        ]
-
-    def _generate_examples(self, df: pd.DataFrame):
-        df = df.drop("split", axis=1)
-        for i, row in enumerate(df.to_dict("records")):
-            yield i, row

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pyproject.toml

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-[tool.poetry]
-name = "datasets-janli"
-version = "0.1.0"
-description = ""
-authors = ["hppRC <[email protected]>"]
-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"