Notes

I apologize for any confusion. It seems there was a misunderstanding. In the code I provided earlier, I assumed that you had implemented your Transformer model with separate encoder and decoder modules. However, the standard Transformer architecture consists of an encoder and a decoder.

If you have implemented your model using the standard Transformer architecture, you can generate predictions as follows:

```python
# Assuming you have trained your model and loaded the checkpoint
# Define your START_TOKEN and END_TOKEN

# Input text
input_text = "What is the weather today?"

# Preprocess input
input_tokens = [START_TOKEN] + nltk.word_tokenize(input_text.lower()) + [END_TOKEN]
input_indices = [combined_to_index[token] for token in input_tokens]

# Convert to tensor and move to device
input_tensor = torch.tensor(input_indices).unsqueeze(0).to(device) # Add batch dimension

# Generate predictions
with torch.no_grad():
output = model(input_tensor) # Pass input through the model

# Get the predicted token indices
predicted_indices = torch.argmax(output, dim=-1).squeeze().cpu().numpy()

# Convert predicted indices to text
predicted_tokens = [index_to_combined[idx] for idx in predicted_indices]
predicted_text = " ".join(predicted_tokens[1:-1]) # Exclude START_TOKEN and END_TOKEN

print("Generated Answer:", predicted_text)
```

This code assumes that your trained model accepts a tensor of token indices as input and produces token predictions as output. Please make sure that your model is set up this way for the code to work correctly. If you have implemented a more complex decoder mechanism (such as autoregressive decoding), you would need to modify the generation process accordingly.