Update modeling.py

modeling.py

@@ -11,27 +11,27 @@ from transformers import (
 
 
 # ============================================================
-# CONFIG CLASS (CRITICAL FIX HERE)
+# CONFIG CLASS (NO **kwargs in __init__)
 # ============================================================
 class EmoAxisConfig(PretrainedConfig):
     model_type = "emoaxis"
 
-    def __init__(self, num_labels=28, base_model_name="roberta-base", **kwargs):
-        super().__init__(**kwargs)
+    def __init__(self, num_labels=28, base_model_name="roberta-base"):
+        # Explicitly pass model_type to super() if removing **kwargs
+        super().__init__(model_type=self.model_type, num_labels=num_labels, base_model_name=base_model_name)
         self.num_labels = num_labels
         self.base_model_name = base_model_name
 
-    # 🚨 ULTIMATE FIX FOR THE TYPERROR 🚨
-    # We overload this method to manually filter out arguments that cause the clash.
+    # CRITICAL FIX: Overload from_pretrained (must retain **kwargs here to work)
+    # NOTE: You MUST retain **kwargs in the from_pretrained signature for it to function correctly
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
-        # Filter the arguments that AutoConfig passes internally, causing the 'multiple values' error
         kwargs.pop("return_unused_kwargs", None)
         kwargs.pop("config", None)
         
-        # Call the base PretrainedConfig's method directly to avoid conflicts with super()
+        # Call the base PretrainedConfig's method directly
         return PretrainedConfig.from_pretrained(
-            cls, # Pass the class itself (EmoAxisConfig) as the first argument
+            cls, 
             pretrained_model_name_or_path, 
             **kwargs
         )
@@ -46,7 +46,13 @@ class Encoder(nn.Module):
         self.encoder = base_encoder
 
     def forward(self, inputs):
-        outputs = self.encoder(**inputs, output_hidden_states=True)
+        # NOTE: If the base encoder takes specific non-standard arguments, 
+        # this will break without **kwargs. We assume only input_ids/attention_mask are passed.
+        outputs = self.encoder(
+            input_ids=inputs["input_ids"], 
+            attention_mask=inputs["attention_mask"], 
+            output_hidden_states=True
+        )
         last_hidden = outputs.hidden_states[-1]
 
         mask = inputs["attention_mask"].unsqueeze(-1).float()
@@ -74,17 +80,16 @@ class Classifier(nn.Module):
 
 
 # ============================================================
-# MAIN MODEL
+# MAIN MODEL (NO *args or **kwargs)
 # ============================================================
 class EmoAxis(PreTrainedModel):
     config_class = EmoAxisConfig
     
-    # FIX: Accept *args and **kwargs for resilience
-    def __init__(self, config, *args, **kwargs): 
-        super().__init__(config, *args, **kwargs)
+    # Removed *args and **kwargs from signature
+    def __init__(self, config): 
+        super().__init__(config)
         
-        # FIX: Load base encoder from its CONFIG to ensure fine-tuned weights are used 
-        # (prevents reinitialization of the base model weights)
+        # This line remains correct for loading saved weights
         base_encoder = AutoModel.from_config(AutoConfig.from_pretrained(config.base_model_name))
         
         self.encoder = Encoder(base_encoder)
@@ -93,11 +98,11 @@ class EmoAxis(PreTrainedModel):
             num_classes=config.num_labels
         )
 
-    def forward(self, input_ids=None, attention_mask=None, **kwargs):
+    # Removed **kwargs from signature
+    def forward(self, input_ids=None, attention_mask=None): 
         inputs = {
             "input_ids": input_ids,
             "attention_mask": attention_mask,
-            **kwargs
         }
         pooled = self.encoder(inputs)
         logits = self.classifier(pooled)