---
license: apache-2.0
task_categories:
- question-answering
language:
- ja
size_categories:
- n<1K
---


# JGraphQA

## Introduction
We introduce JGraphQA, a multimodal benchmark designed to evaluate the chart understanding capabilities of Large Multimodal Models (LMMs) in Japanese.
To create JGraphQA, we first conducted a detailed analysis of the existing ChartQA benchmark. Then, focusing on Japanese investor relations (IR) materials, we collected a total of 100 images consisting of four types: pie charts, line charts, bar charts, and tables. For each image, we created two question-answer pairs.
All questions and answers were manually crafted and verified to ensure accurate and meaningful evaluation.

## Installation
These code snippets were created for evaluation using [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/main). Please make sure to install lmms-eval before using this benchmark.
```sh
conda create --prefix ./lmms-eval python=3.10 -y
conda activate ./lmms-eval
pip install --upgrade pip
git clone --branch v0.3.0 https://github.com/EvolvingLMMs-Lab/lmms-eval
cd lmms-eval
pip install -e .
```


- Access the URLs listed in the "citation_pdf_url" column of "source.csv" and download the corresponding PDF files.
Rename each downloaded file according to the file name specified in the "local_file_name" column of "source.csv".
(Alternatively, you may keep the original file names of the downloaded files and instead update the file names in the "local_file_name" column accordingly.)
Please place the downloaded PDF files in the ./pdf directory.
- Run "create_dataset_for_lmms-eval.ipynb" to generate "jgraphqa.parquet".
- Copy "jgraphqa.yaml", "utils.py", and the generated "jgraphqa.parquet" file into the [lmms_eval/tasks](https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/main/lmms_eval/tasks)/jgraphqa directory.
(You will need to create the jgraphqa directory if it does not already exist.)
- Please add the path to the jgraphqa.parquet file on line 3 of the jgraphqa.yaml file.

### Optional
- If you would like to evaluate [Llama-3.1-70B-Instruct-multimodal-JP-Graph-v0.1](https://huggingface.co/r-g2-2024/Llama-3.1-70B-Instruct-multimodal-JP-Graph-v0.1), after installing lmms-eval, first follow the instructions on the r-g2-2024/Llama-3.1-70B-Instruct-multimodal-JP-Graph-v0.1 page to install LLaVA and other necessary components. Then, please overwrite [lmms_eval/models/llava_onevision.py](https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/models/llava_onevision.py) with the attached "llava_onevision.py".
- If you encounter an error related to wandb, please run the following command: 
```sh
pip install wandb==0.18.5
```

## Usage
- Using the lmms-eval framework, please run the following command:
```bash
CUDA_VISIBLE_DEVICES=0,1 python -m lmms_eval \
    --model llava_onevision  \
    --model_args pretrained="r-g2-2024/Llama-3.1-70B-Instruct-multimodal-JP-Graph-v0.1",model_name=llava_llama_3,conv_template=llava_llama_3,device_map=auto \
    --tasks jgraphqa \
    --batch_size=1 \
    --log_samples \
    --log_samples_suffix llava-onevision \
    --output_path ./logs/ \
    --wandb_args=project=lmms-eval,job_type=eval,name=Llama-3.1-70B-Instruct-multimodal-JP-Graph-v0.1
```