metadata

dataset_info:
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: title
        dtype: string
      - name: context
        dtype: string
      - name: question
        dtype: string
      - name: answers
        struct:
          - name: answer_start
            sequence: int64
          - name: text
            sequence: string
      - name: is_impossible
        dtype: bool
    splits:
      - name: train
        num_bytes: 43238824
        num_examples: 62859
      - name: validation
        num_bytes: 3233443
        num_examples: 4442
    download_size: 9405217
    dataset_size: 46472267
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
license: cc-by-sa-4.0
task_categories:
  - question-answering
language:
  - ja

評価スコアの再現性確保と SB Intuitions 修正版の公開用クローン

ソース: yahoojapan/JGLUE on GitHub

JSQuAD

JSQuAD is a Japanese version of SQuAD (Rajpurkar+, 2016), one of the datasets of reading comprehension. Each instance in the dataset consists of a question regarding a given context (Wikipedia article) and its answer. JSQuAD is based on SQuAD 1.1 (there are no unanswerable questions). We used the Japanese Wikipedia dump as of 20211101.

Licensing Information

Creative Commons Attribution Share Alike 4.0 International

Citation Information

@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"
}

Subsets

default

datasets/jsquad-v1.1 on GitHub

id (str): id of a question
title (str): title of a Wikipedia article
context (str): a concatenation of the title and paragraph
question(str): question
answers(dict{answer_start(int), text(str)}): a set of answers
- start position (character index)
- answer text
is_impossible(bool): all the values are false