initializing tokenizer earlier

model.py

@@ -77,22 +77,20 @@ class BertClassifier(LabelStudioMLBase):
         logger.info(f"✓ Training threshold: {self.start_training_threshold}")
         logger.info("============================")
 
-    def initialize(self):
-        """
-        Initialize model when server starts instead of when first prediction is requested.
-        """
-        logger.info("=== INITIALIZING MODEL ===")
-        
-        # Initialize model and move to device
+        # Initialize tokenizer and model architecture (but not weights yet)
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        logger.info(f"Using device: {self.device}")
-        
-        # Initialize model
+        self.tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
         self._model = AutoModelForSequenceClassification.from_pretrained(
             'bert-base-uncased',
             num_labels=len(self.categories)
         )
-        self.tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+        self._model.to(self.device)
+
+    def initialize(self):
+        """
+        Initialize model when server starts instead of when first prediction is requested.
+        """
+        logger.info("=== INITIALIZING MODEL ===")
         
         # Load saved model if exists
         model_path = os.path.join(self.model_dir, 'model_state.pt')
@@ -103,7 +101,6 @@ class BertClassifier(LabelStudioMLBase):
             except Exception as e:
                 logger.error(f"Failed to load model: {str(e)}")
         
-        self._model.to(self.device)
         logger.info("✓ Model ready")
         return self