direct with auto config modification

tasks/text.py

@@ -53,47 +53,34 @@ async def evaluate_text(request: TextEvaluationRequest):
     # MODEL INFERENCE CODE
     #--------------------------------------------------------------------------------------------   
     
-    
     try:
         # Set device
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         
         # Model and tokenizer paths
         model_name = "Tonic/climate-guard-toxic-agent"
-        tokenizer_name = "answerdotai/ModernBERT-base"
-        
-        # Load base config
-        config = AutoConfig.from_pretrained(model_name)
-        
-        # Remove problematic bias configurations
-        config_dict = config.to_dict()
-        bias_keys = ['attention_bias', 'classifier_bias', 'decoder_bias', 'mlp_bias', 'norm_bias']
-        for key in bias_keys:
-            if key in config_dict:
-                del config_dict[key]
+        tokenizer_name = "Tonic/climate-guard-toxic-agent"
         
-        # Set essential configurations
-        config_dict.update({
-            "architectures": ["ModernBertForSequenceClassification"],
-            "model_type": "modernbert",
-            "num_labels": 8,
-            "problem_type": "single_label_classification",
-            "hidden_size": 768,
-            "num_attention_heads": 12,
-            "num_hidden_layers": 22,
-            "intermediate_size": 1152,
-            "max_position_embeddings": 8192,
-            "layer_norm_eps": 1e-05,
-            "classifier_dropout": 0.0
-        })
-        
-        # Create new config from cleaned dict
-        config = AutoConfig.from_dict(config_dict)
+        # Create config with essential parameters
+        config = AutoConfig.from_pretrained(
+            model_name,
+            num_labels=8,
+            problem_type="single_label_classification",
+            architectures=["ModernBertForSequenceClassification"],
+            model_type="modernbert",
+            hidden_size=768,
+            num_attention_heads=12,
+            num_hidden_layers=22,
+            intermediate_size=1152,
+            max_position_embeddings=8192,
+            layer_norm_eps=1e-05,
+            classifier_dropout=0.0
+        )
         
         # Load tokenizer
         tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
         
-        # Load model with cleaned config
+        # Load model with modified config
         model = AutoModelForSequenceClassification.from_pretrained(
             model_name,
             config=config,