simplify model loading

tasks/text.py

@@ -59,30 +59,26 @@ async def evaluate_text(request: TextEvaluationRequest):
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         
         # Model and tokenizer paths
-        path_model = 'Tonic/climate-guard-toxic-agent'
-        path_tokenizer = "Tonic/climate-guard-toxic-agent"
+        model_name = "Tonic/climate-guard-toxic-agent"
         
-        # Load tokenizer
-        tokenizer = AutoTokenizer.from_pretrained(path_tokenizer)
-        
-        # Load model
+        # Load tokenizer and model
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
         model = AutoModelForSequenceClassification.from_pretrained(
-            path_model,
-            trust_remote_code=True,
-            num_labels=8,
-            problem_type="single_label_classification",
-            ignore_mismatched_sizes=True
+            model_name,
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+            trust_remote_code=True
         ).to(device)
         
-        # Convert to half precision and eval mode
-        model = model.half()
+        # Set model to evaluation mode
         model.eval()
         
         # Preprocess function
         def preprocess_function(examples):
             return tokenizer(
                 examples["quote"],
+                padding=False,
                 truncation=True,
+                max_length=512,
                 return_tensors=None
             )
         
@@ -110,11 +106,10 @@ async def evaluate_text(request: TextEvaluationRequest):
         with torch.no_grad():
             for batch in test_loader:
                 # Move batch to device
-                input_ids = batch['input_ids'].to(device)
-                attention_mask = batch['attention_mask'].to(device)
+                batch = {k: v.to(device) for k, v in batch.items()}
                 
                 # Get model outputs
-                outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+                outputs = model(**batch)
                 preds = torch.argmax(outputs.logits, dim=-1)
                 
                 # Add batch predictions to list