---
dataset_info:
  features:
  - name: input_text
    dtype: string
  - name: question_id
    dtype: int64
  - name: answer
    dtype: string
  - name: answer_type
    dtype: int64
  - name: image
    dtype: image
  - name: pdf_filepath
    dtype: string
  - name: question_page_number
    dtype: string
  splits:
  - name: test
    num_bytes: 1461659890.23
    num_examples: 1155
  download_size: 1190766680
  dataset_size: 1461659890.23
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
license: cc-by-sa-4.0
language:
- ja
source_datasets:
  - shunk031/JDocQA
---

# JDocQA_SingleImage Dataset

## Dataset Summary
`JDocQA_SingleImage`は、`shunk031/JDocQA`のtestサブセットを基に作成されたデータセットで、PDFファイルを200dpiの画像に変換し、画像が取得できない設問と複数画像が必要な設問を除外しています。元のデータセットが37GBと大きすぎるため、サイズを削減しつつ実用性を保つことを目的としています。

- 元データ: `shunk031/JDocQA` (test split, 1,176 instances)
- 変換後: PDFを画像に置き換え、画像が取得できてかつ単一画像入力の設問のみを含む
- 言語: 日本語 (BCP-47 ja-JP)

## Data Fields
- `input_text`: 質問テキスト
- `question_id`: ユニークなID
- `answer`: 回答
- `answer_type`: 回答タイプ（0: Yes/No, 1: Factoid, 2: Numerical, 3: Open-ended）
- `image`: 200dpiのPNG画像（バイト形式）
- `pdf_filepath`: 元のPDFファイルパス（デバッグ用）
- `question_page_number`: 質問に関連するページ番号（デバッグ用）

## Usage
```python
from datasets import load_dataset
dataset = load_dataset("your_username/JDocQA_Image", split="test")
```

## Additional Information
### Dataset Curators
[More Information Needed]
<!-- List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here. -->
### Licensing Information
From [JDocQA's README.md](https://github.com/mizuumi/JDocQA/blob/main/dataset/README.md):
> JDocQA dataset annotations are distributed under CC BY-SA 4.0. We are delighted to see many derivations from JDocQA! When you create any derivations, e.g., datasets, papers, etc, from JDocQA, please cite our paper accordingly. If your derivations are web-based projects, please cite our paper and include the link to [this github page](https://github.com/mizuumi/JDocQA?tab=readme-ov-file#cite).
### Citation Information
```bibtex
@inproceedings{onami2024jdocqa,
  title={JDocQA: Japanese Document Question Answering Dataset for Generative Language Models},
  author={Onami, Eri and Kurita, Shuhei and Miyanishi, Taiki and Watanabe, Taro},
  booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  pages={9503--9514},
  year={2024}
}
```
### Contributions
Thanks to [@mizuumi](https://github.com/mizuumi)  and [@shunk031](https://huggingface.co/shunk031) for creating this dataset.