Update README.md

README.md

@@ -41,7 +41,7 @@ class BLIPNet(torch.nn.Module):
         return outputs, logits  
         
 model = BLIPNet()
-model.load_state_dict(torch.load("best_model_wts_path"), strict=False)
+model.load_state_dict(torch.load("BLILP_Generation_Classification.bin"), strict=False)
 
 You need to input the sample in the same way as shown in the example provided at: https://huggingface.co/uf-aice-lab/BLIP-Math
 Then you can get the generated text and classification score simultaneously.