dev

app.py

@@ -91,18 +91,18 @@ def fine_tune_model(model, dataset_name, hub_id, api_key, num_epochs, batch_size
             # Assuming 'text' is the input and 'target' is the expected output
             model_inputs = tokenizer(
                 examples['text'], 
-                max_length=max_length,  # Set to None for dynamic padding
-                truncation=True,
-                padding='max_length',
+                #max_length=max_length,  # Set to None for dynamic padding
+                #truncation=True,
+                #padding='max_length',
                 return_tensors='pt', 
             )
         
             # Setup the decoder input IDs (shifted right)
             labels = tokenizer(
                 examples['target'], 
-                max_length=max_length,  # Set to None for dynamic padding
-                truncation=True,
-                padding='max_length',
+                #max_length=max_length,  # Set to None for dynamic padding
+                #truncation=True,
+                #padding='max_length',
                 #text_target=examples['target'],
                 return_tensors='pt', 
             )