Update README.md

README.md

@@ -16,7 +16,7 @@ We introduce JGraphQA, a multimodal benchmark designed to evaluate the chart und
 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
+## Installation
 - 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.)
@@ -61,9 +61,9 @@ Please place the downloaded PDF files in the ./pdf directory.
 ```
 
 
-## 🤗Usage
+## Usage
 - Using the lmms-eval framework, please run the following command:
-```python
+```bash
 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 \