Update README.md

README.md

@@ -19,9 +19,9 @@ PubMedCLIP was trained on the [Radiology Objects in COntext (ROCO)](https://gith
 The ROCO dataset includes diverse imaging modalities (such as X-Ray, MRI, ultrasound, fluoroscopy, etc.) from various human body regions (such as head, spine, chest, abdomen, etc.) 
 captured from open-access [PubMed](https://pubmed.ncbi.nlm.nih.gov/) articles.<br> 
 
-The authors of PubMedCLIP have released three different pre-trained models at 
-this [link](https://1drv.ms/u/s!ApXgPqe9kykTgwD4Np3-f7ODAot8?e=zLVlJ2) which use ResNet-50, ResNet-50x4 and ViT32 as image encoders. 
-This repository includes only the ViT32 variant of the PubMedCLIP model.<br> 
+PubMedCLIP was trained for 50 epochs with a batch size of 64 using the Adam optimizer with a learning rate of 10−5. 
+The authors have released three different pre-trained models at this [link](https://1drv.ms/u/s!ApXgPqe9kykTgwD4Np3-f7ODAot8?e=zLVlJ2) 
+which use ResNet-50, ResNet-50x4 and ViT32 as image encoders. This repository includes only the ViT32 variant of the PubMedCLIP model.<br> 
 
 - **Repository:** [PubMedCLIP Official GitHub Repository](https://github.com/sarahESL/PubMedCLIP)
 - **Paper:** [Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?](https://arxiv.org/abs/2112.13906)