Update model.py

model.py

@@ -5,17 +5,20 @@ from transformers import AutoModel
 class EmotionClassifier(nn.Module):
     def __init__(self, model_name="microsoft/deberta-v3-base"):
         super().__init__()
-        self.backbone = AutoModel.from_pretrained(model_name)
-        hidden_size = self.backbone.config.hidden_size
-        # Must be named `out` because your trained weights use out.weight & out.bias
-        self.out = nn.Linear(hidden_size, 5)
+        # IMPORTANT: use the SAME NAME you used during training
+        self.transformer = AutoModel.from_pretrained(model_name)
+        
+        hidden = self.transformer.config.hidden_size
+        
+        # IMPORTANT: your saved checkpoint uses out.weight & out.bias
+        self.out = nn.Linear(hidden, 5)
 
     def forward(self, input_ids, attention_mask):
-        outputs = self.backbone(
+        outputs = self.transformer(
             input_ids=input_ids,
             attention_mask=attention_mask
         )
-        last_hidden_state = outputs.last_hidden_state
-        cls_token = last_hidden_state[:, 0, :]
-        logits = self.out(cls_token)
+        
+        cls_rep = outputs.last_hidden_state[:, 0, :]
+        logits = self.out(cls_rep)
         return logits