Update README.md

README.md

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 license: mit
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+This is the structure of the BLIPNet model. You can load the model with it, or you can create a bigger model for your task.
+
+        class BLIPNet(torch.nn.Module):
+            def __init__(self, ):
+                super().__init__()
+                #Generation Model
+                self.model = BlipForConditionalGeneration.from_pretrained(MODEL_NAME, cache_dir="model")
+                #Same with https://huggingface.co/uf-aice-lab/BLIP-Math
+                self.ebd_dim = ebd_dim= 443136
+
+                #Classification Model
+                fc_dim = 64  # You can choose a higher number for better performance, for example, 1024.
+                self.head = nn.Sequential(
+                    nn.Linear(self.ebd_dim, fc_dim),
+                    nn.ReLU(), 
+                )
+                self.score = nn.Linear(fc_dim, 5) #5 classes
+
+  
+            def forward(self, pixel_values, input_ids):
+                outputs = self.model(input_ids=input_ids, pixel_values=pixel_values, labels=input_ids)
+                image_text_embeds = self.model.vision_model(pixel_values, return_dict=True).last_hidden_state
+                image_text_embeds = self.head(image_embeds.view(-1, self.ebd_dim))
+
+                #A classification model is based on embeddings from a generative model to leverage BLIP's powerful image-text encoding capabilities.
+                logits = self.score(image_embeds)
+
+                #generated text, probabilities of classification
+                return outputs, logits  
+
+You need to input the sample in the same way as: 
+https://huggingface.co/uf-aice-lab/BLIP-Math
+Then you can get the text and score at the same time.