Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
Japanese
Size:
10K - 100K
License:
File size: 3,821 Bytes
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---
dataset_info:
- config_name: default
features:
- name: premise
dtype: large_string
- name: hypothesis
dtype: large_string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 3213257
num_examples: 20073
- name: test
num_bytes: 389445
num_examples: 2434
download_size: 1263287
dataset_size: 3602702
- config_name: v1.1
features:
- name: premise
dtype: large_string
- name: hypothesis
dtype: large_string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 3213257
num_examples: 20073
- name: test
num_bytes: 389445
num_examples: 2434
download_size: 1263287
dataset_size: 3602702
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- config_name: v1.1
data_files:
- split: train
path: v1.1/train-*
- split: test
path: v1.1/test-*
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- ja
tags:
- nli
- benchmark
- evaluation
pretty_name: JGLUE/JNLI
---
# JGLUE[JNLI]: Japanese General Language Understanding Evaluation
JNLI([yahoojapan/JGLUE](https://github.com/yahoojapan/JGLUE)) is a Japanese version of the NLI (Natural Language Inference) dataset.
NLI is a task to recognize the inference relation that a premise sentence has to a hypothesis sentence.
The inference relations are `entailment`, `contradiction`, and `neutral`.
## Dataset Details
### Dataset Description
- **Created by:** yahoojapan
- **Language(s) (NLP):** Japanese
- **License:** CC-BY-SA-4.0
### Dataset Sources [optional]
- **Repository:** [yahoojapan/JGLUE](https://github.com/yahoojapan/JGLUE)
- **Paper:** [More Information Needed]
## Citation
**BibTeX:**
```
@article{栗原 健太郎2023,
title={JGLUE: 日本語言語理解ベンチマーク},
author={栗原 健太郎 and 河原 大輔 and 柴田 知秀},
journal={自然言語処理},
volume={30},
number={1},
pages={63-87},
year={2023},
url = "https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_article/-char/ja",
doi={10.5715/jnlp.30.63}
}
@inproceedings{kurihara-etal-2022-jglue,
title = "{JGLUE}: {J}apanese General Language Understanding Evaluation",
author = "Kurihara, Kentaro and
Kawahara, Daisuke and
Shibata, Tomohide",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.317",
pages = "2957--2966",
abstract = "To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.",
}
@InProceedings{Kurihara_nlp2022,
author = "栗原健太郎 and 河原大輔 and 柴田知秀",
title = "JGLUE: 日本語言語理解ベンチマーク",
booktitle = "言語処理学会第28回年次大会",
year = "2022",
url = "https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf"
note= "in Japanese"
}
```
**APA:**
[More Information Needed]
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