Update README.md

README.md

@@ -50,7 +50,7 @@ The fine-tuning dataset was meticulously sampled in part from the following data
 
 ## Benchmarks 📈
 
-Since there are still many different metrics that need to be tested, we chose a quick and simple metric first to guide the development of our model. Our metric is inspired by Lavy[4]. For the time being, we are using GPT-4 to evaluate the quality of answers on two datasets: OpenViVQA and ViTextVQA. Detailed results can be found at the provided [here](https://huggingface.co/datasets/5CD-AI/Vintern-1B-v2-Benchmark-gpt4o-score). The inputs are images, questions, labels, and predicted answers. The model will return a score from 0 to 10 for the corresponding answer quality. The results table is shown below.
+Since there are still many different metrics that need to be tested, **we chose a quick and simple metric first to guide the development of our model**. Our metric is inspired by Lavy[4]. For the time being, we are using GPT-4 to evaluate the quality of answers on two datasets: OpenViVQA and ViTextVQA. Detailed results can be found at the provided [here](https://huggingface.co/datasets/5CD-AI/Vintern-1B-v2-Benchmark-gpt4o-score). The inputs are images, questions, labels, and predicted answers. The model will return a score from 0 to 10 for the corresponding answer quality. The results table is shown below.
 
 <table border="1" cellspacing="0" cellpadding="5">
     <tr align="center">