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--- |
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license: apache-2.0 |
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task_categories: |
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- question-answering |
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language: |
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- ja |
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size_categories: |
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- n<1K |
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--- |
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# JGraphQA |
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## Introduction |
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We introduce JGraphQA, a multimodal benchmark designed to evaluate the chart understanding capabilities of Large Multimodal Models (LMMs) in Japanese. |
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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. |
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All questions and answers were manually crafted and verified to ensure accurate and meaningful evaluation. |
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## Installation |
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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. |
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```sh |
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conda create --prefix ./lmms-eval python=3.10 -y |
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conda activate ./lmms-eval |
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pip install --upgrade pip |
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git clone --branch v0.3.0 https://github.com/EvolvingLMMs-Lab/lmms-eval |
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cd lmms-eval |
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pip install -e . |
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``` |
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- Access the URLs listed in the "citation_pdf_url" column of "source.csv" and download the corresponding PDF files. |
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Rename each downloaded file according to the file name specified in the "local_file_name" column of "source.csv". |
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(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.) |
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Please place the downloaded PDF files in the ./pdf directory. |
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- Run "create_dataset_for_lmms-eval.ipynb" to generate "jgraphqa.parquet". |
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- 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. |
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(You will need to create the jgraphqa directory if it does not already exist.) |
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- Please add the path to the jgraphqa.parquet file on line 3 of the jgraphqa.yaml file. |
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### Optional |
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- 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". |
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- If you encounter an error related to wandb, please run the following command: |
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```sh |
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pip install wandb==0.18.5 |
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``` |
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## Usage |
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- Using the lmms-eval framework, please run the following command: |
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```bash |
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CUDA_VISIBLE_DEVICES=0,1 python -m lmms_eval \ |
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--model llava_onevision \ |
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--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 \ |
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--tasks jgraphqa \ |
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--batch_size=1 \ |
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--log_samples \ |
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--log_samples_suffix llava-onevision \ |
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--output_path ./logs/ \ |
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--wandb_args=project=lmms-eval,job_type=eval,name=Llama-3.1-70B-Instruct-multimodal-JP-Graph-v0.1 |
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``` |
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