complexity threshold

return complexity / (len(query) + num_dependencies + 1) def resize_window(query, complexity): # Resize context window based on complexity base_window_size = 512 if complexity > 0.7: window_size = int(base_window_siz…

- Add additional checks to ensure the query length does not exceed the window size. ### Example Adjusted Logic ```python def resize_window(query, complexity): # Resize context window based on complexity base_window_size = 768 …

# Evaluate model on test queries precision = 0 for query in test_queries: # Calculate complexity complexity = calculate_complexity(query) # Apply threshold if complexity > 0.5: …

[Turn 8176] User: Sounds good! I'll extend the `test_queries` and `expected_outcomes` lists to include 2,000 queries and their expected outcomes. I'll make sure to cover a wide range of complexities and scenarios to get a thorough evaluatio…

correct_count = 0 for query, expected in zip(test_queries, expected_outcomes): # Calculate complexity complexity = calculate_complexity(query) # Apply threshold and resize window resized_quer…

dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False) # Process inputs in batches all_resized_inputs = [] for batch in dataloader: batch_inputs = batch[0] resized_batch = process_inputs(batch_inputs) all_resize…

complexity_scoring_module = ComplexityScoringModule().to(device) resizing_module = ResizingModule().to(device) # Define a function to process inputs def process_inputs(inputs, complexity_threshold=0.7): inputs = inputs.to(device) w…

1. **Check Model Outputs**: - Ensure that the outputs of the `ComplexityScoringModule` are within the expected range (0 to 1). - Verify that the resizing logic is applied correctly based on the complexity threshold. 2. **Monitor Sta…

all_resized_queries.append(resized_batch) # Concatenate all resized queries resized_queries = torch.cat(all_resized_queries, dim=0) # Print the shape of the resized queries to verify print(resized_queries.shape) ``` ### Explanation …