512

outputs.append(self.model(chunk)) return outputs # Example usage: segmenter = ContextWindowSegmentation('bert-base-uncased', 512) input_text = 'This is a sample input text that needs to be segmented and processed.' out…

chunk = inputs['input_ids'][0][i:i+self.max_tokens] chunks.append(chunk) # Process each chunk outputs = [] for chunk in chunks: # Process chunk using model outputs.app…

# High complexity, resize to larger window resized_window = resize_window(query, 2048) elif complexity < 0.2: # Low complexity, resize to smaller window resized_window = resize_window(query, 256) else…

# Test the class resizer = ContextWindowResizer(max_window_size=512) input_ids = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) attention_mask = torch.tensor([[1, 1, 1, 0, 0], [1, 1, 1, 1, 0]]) resized_window = resizer(input_ids, attenti…

Given this breakdown, 12 hours seems to be a reasonable estimate to complete 65% of the resizing code. Here's a more detailed plan: ### Detailed Plan 1. **Query Complexity Analysis (2 hours)** - Analyze the distribution of query comple…

data = data.to(device) optimizer.zero_grad() outputs = model(data) loss = nn.MSELoss()(outputs, data) loss.backward() optimizer.step() # Generate synthetic data num_queries = 3500 batch_size …