Update README.md

README.md

@@ -23,7 +23,7 @@ model-index:
           type: summarization
           name: Summarization
         dataset:
-          name: 'SAMSum Corpus: Dataset for medical question summarization'
+          name: 'Dataset for medical question summarization'
           type: bigbio/meqsum
           split: valid
         metrics:
@@ -43,7 +43,7 @@ library_name: transformers
 ---
 
 ## MedQSum
-medqsum-bart-large-xsum-meqsum is the best fine-tuned model in the paper [Enhancing Large Language Models' Utility for Medical Question-Answering: A Patient Health Question Summarization Approach](), which introduces a solution to get the most out of LLMs, when answering health-related questions. We address the challenge of crafting accurate prompts by summarizing consumer health questions (CHQs) to generate clear and concise medical questions. Our approach involves fine-tuning Transformer-based models, including Flan-T5 in resource-constrained environments and three medical question summarization datasets.
+**`medqsum-bart-large-xsum-meqsum`** is the best fine-tuned model in the paper [Enhancing Large Language Models' Utility for Medical Question-Answering: A Patient Health Question Summarization Approach](), which introduces a solution to get the most out of LLMs, when answering health-related questions. We address the challenge of crafting accurate prompts by summarizing consumer health questions (CHQs) to generate clear and concise medical questions. Our approach involves fine-tuning Transformer-based models, including Flan-T5 in resource-constrained environments and three medical question summarization datasets.
 
 ## Hyperparameters
 ```json