Passed configuration parameters directly to ModernBertConfig constructor

tasks/text.py

@@ -60,42 +60,41 @@ async def evaluate_text(request: TextEvaluationRequest):
         
         # Model and tokenizer paths
         model_name = "Tonic/climate-guard-toxic-agent"
-        tokenizer_name = "answerdotai/ModernBERT-base"
+        tokenizer_name = "Tonic/climate-guard-toxic-agent"
 
-        # Define minimal configuration
-        config_dict = {
-            "_name_or_path": "answerdotai/ModernBERT-base",
-            "architectures": ["ModernBertForSequenceClassification"],
-            "model_type": "modernbert",
-            "vocab_size": 50368,
-            "hidden_size": 768,
-            "num_hidden_layers": 22,
-            "num_attention_heads": 12,
-            "intermediate_size": 1152,
-            "max_position_embeddings": 8192,
-            "position_embedding_type": "absolute",
-            "layer_norm_eps": 1e-5,
-            "hidden_activation": "gelu",
-            "classifier_activation": "gelu",
-            "classifier_pooling": "mean",
-            "num_labels": 8,
-            "pad_token_id": 50283,
-            "bos_token_id": 50281,
-            "eos_token_id": 50282,
-            "sep_token_id": 50282,
-            "cls_token_id": 50281,
-            "problem_type": "single_label_classification",
-            "id2label": {str(i): label for i, label in enumerate(LABEL_MAPPING.keys())},
-            "label2id": LABEL_MAPPING
-        }
+        # Create config
+        config = ModernBertConfig(
+            vocab_size=50368,
+            hidden_size=768,
+            num_hidden_layers=22,
+            num_attention_heads=12,
+            intermediate_size=1152,
+            max_position_embeddings=8192,
+            layer_norm_eps=1e-5,
+            position_embedding_type="absolute",
+            pad_token_id=50283,
+            bos_token_id=50281,
+            eos_token_id=50282,
+            sep_token_id=50282,
+            cls_token_id=50281,
+            hidden_activation="gelu",
+            classifier_activation="gelu",
+            classifier_pooling="mean",
+            num_labels=8,
+            id2label={str(i): label for i, label in enumerate(LABEL_MAPPING.keys())},
+            label2id=LABEL_MAPPING,
+            problem_type="single_label_classification",
+            architectures=["ModernBertForSequenceClassification"],
+            model_type="modernbert"
+        )
         
         # Load tokenizer
         tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
         
-        # Load model with minimal config
+        # Load model
         model = AutoModelForSequenceClassification.from_pretrained(
             model_name,
-            config_dict=config_dict,
+            config=config,
             trust_remote_code=True,
             ignore_mismatched_sizes=True,
             torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
@@ -149,6 +148,7 @@ async def evaluate_text(request: TextEvaluationRequest):
     except Exception as e:
         print(f"Error during model inference: {str(e)}")
         raise
+    
     #--------------------------------------------------------------------------------------------
     # MODEL INFERENCE ENDS HERE
     #--------------------------------------------------------------------------------------------