Update custom_model_package/custom_model.py

custom_model_package/custom_model.py

@@ -10,27 +10,25 @@ class CustomConfig(PretrainedConfig):
         self.num_emotion_labels = num_emotion_labels
 
 class CustomModel(XLMRobertaForSequenceClassification):
-    config_class = CustomConfig
-    
-    def __init__(self, config):
+    def __init__(self, config, num_emotion_labels):
         super(CustomModel, self).__init__(config)
-        self.num_emotion_labels = config.num_emotion_labels
+        self.num_emotion_labels = num_emotion_labels
         self.dropout_emotion = nn.Dropout(config.hidden_dropout_prob)
         self.emotion_classifier = nn.Sequential(
             nn.Linear(config.hidden_size, 512),
             nn.Mish(),
             nn.Dropout(0.3),
-            nn.Linear(512, self.num_emotion_labels)
+            nn.Linear(512, num_emotion_labels)
         )
         self._init_weights(self.emotion_classifier[0])
         self._init_weights(self.emotion_classifier[3])
-
+        
     def _init_weights(self, module):
         if isinstance(module, nn.Linear):
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
             if module.bias is not None:
                 module.bias.data.zero_()
-
+                
     def forward(self, input_ids=None, attention_mask=None, sentiment=None, labels=None):
         outputs = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
         sequence_output = outputs[0]
@@ -39,16 +37,13 @@ class CustomModel(XLMRobertaForSequenceClassification):
         cls_hidden_states = sequence_output[:, 0, :]
         cls_hidden_states = self.dropout_emotion(cls_hidden_states)
         emotion_logits = self.emotion_classifier(cls_hidden_states)
-        
-        # Sentiment logits from the original classifier
-        sentiment_logits = self.classifier(cls_hidden_states)
-        
-        # Concatenate the sentiment and emotion logits
-        logits = torch.cat([sentiment_logits, emotion_logits], dim=-1)
-        
+        with torch.no_grad():
+            cls_token_state = sequence_output[:, 0, :].unsqueeze(1)
+            sentiment_logits = self.classifier(cls_token_state).squeeze(1)
         if labels is not None:
             class_weights = torch.tensor([1.0] * self.num_emotion_labels).to(labels.device)
             loss_fct = nn.BCEWithLogitsLoss(pos_weight=class_weights)
             loss = loss_fct(emotion_logits, labels)
-            return {"loss": loss, "logits": logits}
-        return {"logits": logits}
+            return {"loss": loss, "emotion_logits": emotion_logits, "sentiment_logits": sentiment_logits}
+        return {"emotion_logits": emotion_logits, "sentiment_logits": sentiment_logits}
+