revert to autoconfig

tasks/text.py

@@ -3,7 +3,7 @@ from datetime import datetime
 from datasets import load_dataset
 from sklearn.metrics import accuracy_score
 import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
 from torch.utils.data import DataLoader
 from transformers import DataCollatorWithPadding
 
@@ -53,6 +53,7 @@ async def evaluate_text(request: TextEvaluationRequest):
     # MODEL INFERENCE CODE
     #--------------------------------------------------------------------------------------------   
     
+    
     try:
         # Set device
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -64,20 +65,22 @@ async def evaluate_text(request: TextEvaluationRequest):
         # Initialize tokenizer
         tokenizer = AutoTokenizer.from_pretrained(path_tokenizer)
         
-        # Initialize model with specific configuration
+        # Load and modify config
+        config = AutoConfig.from_pretrained(path_model)
+        config.norm_bias = False
+        config.classifier_bias = False
+        config.attention_bias = False
+        config.mlp_bias = False
+        
+        # Initialize model with modified config
         model = AutoModelForSequenceClassification.from_pretrained(
             path_model,
+            config=config,
             trust_remote_code=True,
             num_labels=8,
             problem_type="single_label_classification",
             ignore_mismatched_sizes=True,
-            torch_dtype=torch.float16,
-            config_overrides={
-                "norm_bias": None,  # Remove bias parameter
-                "classifier_bias": None,
-                "attention_bias": None,
-                "mlp_bias": None
-            }
+            torch_dtype=torch.float16
         ).to(device)
         
         # Set model to evaluation mode
@@ -133,6 +136,7 @@ async def evaluate_text(request: TextEvaluationRequest):
         print(f"Error during model inference: {str(e)}")
         raise
 
+
     #--------------------------------------------------------------------------------------------
     # MODEL INFERENCE ENDS HERE
     #--------------------------------------------------------------------------------------------