metadata

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) 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
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]