Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Languages:
Japanese
Size:
10K - 100K
License:
Commit
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Parent(s):
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Update parquet files
Browse files- README.md +0 -198
- base/janli-test.parquet +3 -0
- base/janli-train.parquet +3 -0
- janli.py +0 -101
- original/janli-test.parquet +3 -0
- original/janli-train.parquet +3 -0
- poetry.lock +0 -0
- pyproject.toml +0 -23
README.md
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---
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language:
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- ja
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language_creators:
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- other
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multilinguality:
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- monolingual
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pretty_name: JaNLI
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task_categories:
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- text-classification
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task_ids:
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- natural-language-inference
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license: cc-by-sa-4.0
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---
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# Dataset Card for JaNLI
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## Table of Contents
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- [Dataset Card for JaNLI](#dataset-card-for-janli)
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [base](#base)
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- [original](#original)
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- [Data Fields](#data-fields)
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- [base](#base-1)
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- [original](#original-1)
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- [Data Splits](#data-splits)
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- [Annotations](#annotations)
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- [Additional Information](#additional-information)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** https://github.com/verypluming/JaNLI
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- **Repository:** https://github.com/verypluming/JaNLI
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- **Paper:** https://aclanthology.org/2021.blackboxnlp-1.26/
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### Dataset Summary
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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.
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### Languages
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The language data in JaNLI is in Japanese (BCP-47 [ja-JP](https://www.rfc-editor.org/info/bcp47)).
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## Dataset Structure
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### Data Instances
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When loading a specific configuration, users has to append a version dependent suffix:
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```python
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import datasets as ds
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dataset: ds.DatasetDict = ds.load_dataset("hpprc/janli")
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print(dataset)
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# DatasetDict({
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# train: Dataset({
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# features: ['id', 'premise', 'hypothesis', 'label', 'heuristics', 'number_of_NPs', 'semtag'],
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# num_rows: 13680
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# })
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# test: Dataset({
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# features: ['id', 'premise', 'hypothesis', 'label', 'heuristics', 'number_of_NPs', 'semtag'],
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# num_rows: 720
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# })
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# })
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dataset: ds.DatasetDict = ds.load_dataset("hpprc/janli", name="original")
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print(dataset)
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# DatasetDict({
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# train: Dataset({
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# features: ['id', 'sentence_A_Ja', 'sentence_B_Ja', 'entailment_label_Ja', 'heuristics', 'number_of_NPs', 'semtag'],
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# num_rows: 13680
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# })
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# test: Dataset({
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# features: ['id', 'sentence_A_Ja', 'sentence_B_Ja', 'entailment_label_Ja', 'heuristics', 'number_of_NPs', 'semtag'],
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# num_rows: 720
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# })
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# })
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```
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#### base
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An example of looks as follows:
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```json
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{
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'id': 12,
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'premise': '若者がフットボール選手を見ている',
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'hypothesis': 'フットボール選手を若者が見ている',
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'label': 0,
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'heuristics': 'overlap-full',
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'number_of_NPs': 2,
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'semtag': 'scrambling'
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}
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```
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#### original
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An example of looks as follows:
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```json
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{
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'id': 12,
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'sentence_A_Ja': '若者がフットボール選手を見ている',
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'sentence_B_Ja': 'フットボール選手を若者が見ている',
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'entailment_label_Ja': 0,
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'heuristics': 'overlap-full',
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'number_of_NPs': 2,
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'semtag': 'scrambling'
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}
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```
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### Data Fields
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#### base
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A version adopting the column names of a typical NLI dataset.
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- `id`: The number of the sentence pair.
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- `premise`: The premise (sentence_A_Ja).
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- `hypothesis`: The hypothesis (sentence_B_Ja).
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- `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).
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- `heuristics`: The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.
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- `number_of_NPs`: The number of noun phrase in a sentence.
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- `semtag`: The linguistic phenomena tag.
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#### original
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The original version retaining the unaltered column names.
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- `id`: The number of the sentence pair.
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- `sentence_A_Ja`: The premise.
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- `sentence_B_Ja`: The hypothesis.
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- `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
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- `heuristics`: The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.
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- `number_of_NPs`: The number of noun phrase in a sentence.
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- `semtag`: The linguistic phenomena tag.
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### Data Splits
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| name | train | validation | test |
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| -------- | -----: | ---------: | ---: |
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| base | 13,680 | | 720 |
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| original | 13,680 | | 720 |
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### Annotations
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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.
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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.
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Additionally, 11 categories of Japanese linguistic phenomena and constructions are focused on for generating the five patterns of adversarial inferences.
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For each linguistic phenomenon, a template for the premise sentence P is fixed, and multiple templates for hypothesis sentences H are created.
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In total, 144 templates for (P, H) pairs are produced.
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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.
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The JaNLI dataset is generated by instantiating each template 100 times, resulting in a total of 14,400 examples.
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The same number of entailment and non-entailment examples are generated for each phenomenon.
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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.
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The dataset uses a total of 158 words (nouns and verbs), which occur more than 20 times in the JSICK and JSNLI datasets.
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## Additional Information
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- [verypluming/JaNLI](https://github.com/verypluming/JaNLI)
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- [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/)
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### Licensing Information
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CC BY-SA 4.0
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### Citation Information
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```bibtex
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@InProceedings{yanaka-EtAl:2021:blackbox,
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author = {Yanaka, Hitomi and Mineshima, Koji},
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title = {Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference},
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booktitle = {Proceedings of the 2021 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP2021)},
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url = {https://aclanthology.org/2021.blackboxnlp-1.26/},
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year = {2021},
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}
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```
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### Contributions
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Thanks to [Hitomi Yanaka](https://hitomiyanaka.mystrikingly.com/) and Koji Mineshima for creating this dataset.
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base/janli-test.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:b89aea3cc598f35928d1264e86bd881f3506a56a356db945d6abcf93aa80179b
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size 30852
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base/janli-train.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:c2e9941d78f11f725ced9f44be1392e2a804ae1f89f2a7be8d1ed5f464b272d9
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size 474888
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janli.py
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import datasets as ds
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import pandas as pd
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_CITATION = """\
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@InProceedings{yanaka-EtAl:2021:blackbox,
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author = {Yanaka, Hitomi and Mineshima, Koji},
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title = {Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference},
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booktitle = {Proceedings of the 2021 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP2021)},
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year = {2021},
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}
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"""
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_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."
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_HOMEPAGE = "https://github.com/verypluming/JaNLI"
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_LICENSE = "CC BY-SA 4.0"
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_DOWNLOAD_URL = "https://raw.githubusercontent.com/verypluming/JaNLI/main/janli.tsv"
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class JaNLIDataset(ds.GeneratorBasedBuilder):
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VERSION = ds.Version("1.0.0")
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DEFAULT_CONFIG_NAME = "base"
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BUILDER_CONFIGS = [
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ds.BuilderConfig(
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name="base",
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version=VERSION,
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description="A version adopting the column names of a typical NLI dataset.",
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),
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ds.BuilderConfig(
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name="original",
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version=VERSION,
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description="The original version retaining the unaltered column names.",
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),
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]
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def _info(self) -> ds.DatasetInfo:
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if self.config.name == "base":
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features = ds.Features(
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{
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"id": ds.Value("int64"),
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"premise": ds.Value("string"),
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"hypothesis": ds.Value("string"),
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"label": ds.ClassLabel(names=["entailment", "non-entailment"]),
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"heuristics": ds.Value("string"),
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"number_of_NPs": ds.Value("int32"),
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"semtag": ds.Value("string"),
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}
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)
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elif self.config.name == "original":
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features = ds.Features(
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{
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"id": ds.Value("int64"),
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"sentence_A_Ja": ds.Value("string"),
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"sentence_B_Ja": ds.Value("string"),
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"entailment_label_Ja": ds.ClassLabel(names=["entailment", "non-entailment"]),
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"heuristics": ds.Value("string"),
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"number_of_NPs": ds.Value("int32"),
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"semtag": ds.Value("string"),
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}
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)
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return ds.DatasetInfo(
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description=_DESCRIPTION,
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citation=_CITATION,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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features=features,
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)
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def _split_generators(self, dl_manager: ds.DownloadManager):
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data_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
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df: pd.DataFrame = pd.read_table(data_path, header=0, sep="\t", index_col=0)
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df["id"] = df.index
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if self.config.name == "base":
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df = df.rename(
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columns={
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"sentence_A_Ja": "premise",
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"sentence_B_Ja": "hypothesis",
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"entailment_label_Ja": "label",
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}
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)
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return [
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ds.SplitGenerator(
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name=ds.Split.TRAIN,
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gen_kwargs={"df": df[df["split"] == "train"]},
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),
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ds.SplitGenerator(
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name=ds.Split.TEST,
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gen_kwargs={"df": df[df["split"] == "test"]},
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),
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]
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def _generate_examples(self, df: pd.DataFrame):
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df = df.drop("split", axis=1)
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for i, row in enumerate(df.to_dict("records")):
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yield i, row
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original/janli-test.parquet
ADDED
@@ -0,0 +1,3 @@
|
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|
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|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:27791e69bf03e8b1de805ee46a09ed2ba4f6e71a98db266c009d0aa7caa98c65
|
3 |
+
size 31008
|
original/janli-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
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|
|
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|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c89aad4ee1eed7fca58fdbb23e0b30749617aea53b60a442e3dea6ec5ae59ed4
|
3 |
+
size 475642
|
poetry.lock
DELETED
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See raw diff
|
|
pyproject.toml
DELETED
@@ -1,23 +0,0 @@
|
|
1 |
-
[tool.poetry]
|
2 |
-
name = "datasets-janli"
|
3 |
-
version = "0.1.0"
|
4 |
-
description = ""
|
5 |
-
authors = ["hppRC <hpp.ricecake@gmail.com>"]
|
6 |
-
readme = "README.md"
|
7 |
-
packages = []
|
8 |
-
|
9 |
-
[tool.poetry.dependencies]
|
10 |
-
python = "^3.8.1"
|
11 |
-
datasets = "^2.11.0"
|
12 |
-
|
13 |
-
|
14 |
-
[tool.poetry.group.dev.dependencies]
|
15 |
-
black = "^22.12.0"
|
16 |
-
isort = "^5.11.4"
|
17 |
-
flake8 = "^6.0.0"
|
18 |
-
mypy = "^0.991"
|
19 |
-
pytest = "^7.2.0"
|
20 |
-
|
21 |
-
[build-system]
|
22 |
-
requires = ["poetry-core"]
|
23 |
-
build-backend = "poetry.core.masonry.api"
|
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