Notes

To get predictions from your trained Transformer model on your sample data, you can follow these steps:

1. Preprocess Input:
Convert your input text (questions) into token indices using your vocabulary mapping. Make sure to add the START_TOKEN at the beginning and the END_TOKEN at the end of the token sequence.

2. Prepare Input Tensor:
Convert the token indices into a tensor and move it to the appropriate device (CPU or GPU).

3. Generate Predictions:
Use the trained model to generate predictions. Pass the input tensor through the encoder and then through the decoder step by step, generating one token at a time. At each step, append the generated token to the input tensor and continue the generation process.

4. Decode Output:
Convert the generated token indices back to text using your vocabulary mapping.

Here's a simplified code example of how you can do this:

```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():
encoder_output = model.encoder(input_tensor)
decoder_input = torch.tensor([combined_to_index[START_TOKEN]]).to(device) # Start with START_TOKEN
generated_indices = []

for _ in range(max_sequence_length): # Adjust max_sequence_length if needed
decoder_output = model.decoder(decoder_input, encoder_output)
_, next_token = torch.max(decoder_output, dim=-1)
generated_indices.append(next_token.item())
decoder_input = next_token

# Decode generated indices to text
generated_tokens = [index_to_combined[idx] for idx in generated_indices]
generated_text = " ".join(generated_tokens[1:-1]) # Exclude START_TOKEN and END_TOKEN

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

Please note that this is a simplified example and might need adjustments based on your actual model and setup. Additionally, you might want to implement mechanisms like beam search for better text generation.