Logs

app.py

@@ -121,14 +121,20 @@ def evaluate_model(model, dataloader, device):
     all_preds = []
     all_labels = []
 
-    test = True
-    test2 = True
     # Disable gradient calculations
     with torch.no_grad():
         for batch in dataloader:
             input_ids = batch['input_ids'].to(device)
             attention_mask = batch['attention_mask'].to(device)
             labels = batch['labels'].to(device)
+            x = len(labels[0])
+            print(labels[0])
+            for l in labels:
+                if len(l) != x:
+                    print(len(l))
+                    print(l)
+                    break
+            
 
             # Forward pass to get logits
             outputs = model(input_ids, attention_mask=attention_mask)
@@ -136,17 +142,9 @@ def evaluate_model(model, dataloader, device):
 
             # Get predictions
             preds = torch.argmax(logits, dim=-1).cpu().numpy()
-            if test or test2:
-                if test:
-                    test2 = False 
-                test = False
-                print(len(preds))
-                print(len(labels))
-                print(preds)
-                print(labels)
             
             all_preds.extend(preds)
-            all_labels.extend(labels.cpu().numpy())
+            all_labels.extend(labels)
 
     # Calculate evaluation metrics
     print("evaluate_model sizes")