---
dataset_info:
- config_name: CC-OCR-JA-Refined
  features:
  - name: original_id
    dtype: int64
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: ocr
    dtype: string
  - name: image_name
    dtype: string
  splits:
  - name: test
    num_bytes: 71840923
    num_examples: 145
  download_size: 71822359
  dataset_size: 71840923
- config_name: CVQA-JA-Refined
  features:
  - name: image
    dtype: image
  - name: original_id
    dtype: string
  - name: question
    dtype: string
  - name: options
    list: string
  - name: answer
    dtype: int64
  - name: Category
    dtype: string
  - name: Image Type
    dtype: string
  - name: Image Source
    dtype: string
  - name: License
    dtype: string
  splits:
  - name: test
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    num_examples: 200
  download_size: 62713215
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- config_name: Heron-Bench-Refined
  features:
  - name: original_id
    dtype: int64
  - name: image
    dtype: image
  - name: image_category
    dtype: string
  - name: context
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  splits:
  - name: test
    num_bytes: 38067838
    num_examples: 88
  download_size: 38046258
  dataset_size: 38067838
- config_name: JA-Multi-Image-VQA-Refined
  features:
  - name: original_id
    dtype: int64
  - name: images
    list: image
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: image_urls
    list: string
  - name: page_urls
    list: string
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  - name: test
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    num_examples: 53
  download_size: 99466318
  dataset_size: 99461072
- config_name: JA-VLM-Bench-Refined
  features:
  - name: original_id
    dtype: int64
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: image_url
    dtype: string
  - name: page_url
    dtype: string
  splits:
  - name: test
    num_bytes: 6925318
    num_examples: 49
  download_size: 6917035
  dataset_size: 6925318
- config_name: JDocQA-Refined
  features:
  - name: original_id
    dtype: int64
  - name: question
    dtype: string
  - name: answer
    dtype: string
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  - name: test
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    num_examples: 861
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- config_name: JGraphQA-Refined
  features:
  - name: original_id
    dtype: int64
  - name: type
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: test
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    num_examples: 196
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configs:
- config_name: CC-OCR-JA-Refined
  data_files:
  - split: test
    path: CC-OCR-JA-Refined/test-*
- config_name: CVQA-JA-Refined
  data_files:
  - split: test
    path: CVQA-JA-Refined/test-*
- config_name: Heron-Bench-Refined
  data_files:
  - split: test
    path: Heron-Bench-Refined/test-*
- config_name: JA-Multi-Image-VQA-Refined
  data_files:
  - split: test
    path: JA-Multi-Image-VQA-Refined/test-*
- config_name: JA-VLM-Bench-Refined
  data_files:
  - split: test
    path: JA-VLM-Bench-Refined/test-*
- config_name: JDocQA-Refined
  data_files:
  - split: test
    path: JDocQA-Refined/test-*
- config_name: JGraphQA-Refined
  data_files:
  - split: test
    path: JGraphQA-Refined/test-*
  default: true
license: other
language:
- ja
size_categories:
- 1K<n<10K
---



## Overview

JAMMEval is a curated benchmark collection for evaluating Vision-Language Models (VLMs) on Japanese Visual Question Answering (VQA) tasks.
It is constructed by refining seven existing Japanese VQA evaluation datasets through two rounds of human annotation, with the goal of improving evaluation reliability and quality.

## Included Datasets

JAMMEval consists of the following seven refined datasets:

- CC-OCR-JA-Refined
- CVQA-JA-Refined
- Heron-Bench-Refined
- JA-Multi-Image-VQA-Refined
- JA-VLM-Bench-Refined
- JDocQA-Refined
- JGraphQA-Refined

Each dataset is derived from its original version (without the `-Refined` suffix) through a systematic refinement process.

## Refinement Process

The refinement focuses on addressing key issues found in the original datasets:

- Ambiguity in questions or answers
- Incorrect annotated answers
- Questions solvable without visual input (i.e., not requiring the image)

Through manual inspection and correction, JAMMEval improves the reliability of VLM evaluation, ensuring that tasks genuinely require multimodal understanding.

## Dataset Usage

```python
from datasets import load_dataset

ds = load_dataset("secret13/JAMMEval", "Heron-Bench-Refined", split="test")

print(ds)
```

Example output:
```
Dataset({
    features: ['original_id', 'image', 'question', 'answer', 'ocr', 'image_name'],
    num_rows: 145
})
```


## License
Each dataset is derived from its original source dataset and is subject to the license terms of the original dataset.
  - [CC-OCR](https://arxiv.org/abs/2412.02210)
    - MIT
  - [CVQA](https://arxiv.org/abs/2406.05967):
    - > Note that each question has its own license. All data here is free to use for research purposes, but not every entry is permissible for commercial use.
  - [Heron-Bench](https://arxiv.org/abs/2404.07824)
    - > We have collected images that are either in the public domain or licensed under Creative Commons Attribution 1.0 (CC BY 1.0) or Creative Commons Attribution 2.0 (CC BY 2.0). Please refer to the LICENSE.md file for details on the licenses.
  - [JA-Multi-Image-VQA](https://huggingface.co/datasets/SakanaAI/JA-Multi-Image-VQA)
    -  The images in this dataset are sourced from Unsplash and are free to use under the Unsplash License. They cannot be sold without significant modification and cannot be used to replicate similar or competing services. All other parts of this dataset, excluding the images, are licensed under the Apache 2.0 License.
  - [JA-VLM-Bench](https://huggingface.co/datasets/SakanaAI/JA-VLM-Bench-In-the-Wild)
    - > The images in this dataset are sourced from Unsplash and are free to use under the Unsplash License. They cannot be sold without significant modification and cannot be used to replicate similar or competing services.
  - [JDocQA](https://arxiv.org/abs/2403.19454)
    - > 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.
  - [JGraphQA](https://huggingface.co/datasets/r-g2-2024/JGraphQA)
    - License information is not clearly specified. Users should verify the original source before use.

⚠️ Since JAMMEval is a collection of datasets with different licenses, 
users must check the license of each individual dataset and each data entry (if applicable) before use. In particular, some datasets (e.g., CVQA) may include data that is restricted to non-commercial use.

**Note on JDocQA-Refined Images**

The images included in JDocQA-Refined must be used in compliance with Japanese copyright law:

> "Use is permitted only within the scope defined by Article 30-4 of the Japanese Copyright Act."

Users are responsible for ensuring that their use of these images complies with applicable regulations.