Update README.md

README.md

@@ -19,7 +19,7 @@ The **T5 Large for Medical Text Summarization** is a specialized variant of the
 
 The T5 Large model, known as "t5-large," is pre-trained on a broad range of medical literature, enabling it to capture intricate medical terminology, extract crucial information, and produce meaningful summaries. The fine-tuning process for this model is meticulous, with attention to hyperparameter settings, including batch size and learning rate, to ensure optimal performance in the field of medical text summarization.
 
-During the fine-tuning process, a batch size of 4 is chosen for efficiency, and a learning rate of 1e-5 is selected to strike a balance between convergence speed and model optimization. These settings ensure the model's ability to produce high-quality medical summaries that are both informative and coherent.
+During the fine-tuning process, a batch size of 8 is chosen for efficiency, and a learning rate of 2e-5 is selected to strike a balance between convergence speed and model optimization. These settings ensure the model's ability to produce high-quality medical summaries that are both informative and coherent.
 
 The fine-tuning dataset consists of diverse medical documents, clinical studies, and healthcare research, along with human-generated summaries. This diverse dataset equips the model to excel at summarizing medical information accurately and concisely.