README.md

---
license: mit
---

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.