Update README.md

README.md

@@ -6,4 +6,34 @@ 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.
+
+## 🔔News
+- 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.
+- 
+
+## 🤗Usage
+```python
+CUDA_VISIBLE_DEVICES=0,1 python -m lmms_eval \
+    --model llava_onevision  \
+    --model_args pretrained="akirakinoshita/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
+```
+
+