Notes

Sure, I'd be happy to explain how to prepare input text and pass it through your transformer model. Let's break it down step by step:

1. **Tokenization:** Input text needs to be tokenized into individual words or subwords, similar to how your dataset was tokenized during the data loading process. This helps the model understand the text as a sequence of discrete tokens.

2. **Adding Special Tokens:** You need to add the start token `[START]` and end token `[END]` to the input text to indicate the beginning and end of the sequence.

3. **Converting to Indices:** Convert the tokenized input text to a sequence of indices using your vocabulary mapping. Each token is replaced by its corresponding index in the vocabulary.

4. **Padding and Reshaping:** The input sequence needs to have a fixed length to be fed into the model. If the input is shorter than the desired length, pad it with padding tokens. If it's longer, truncate or handle it appropriately. Then reshape the sequence to have a batch dimension.

5. **Passing through the Model:** Once the input text is prepared as a tensor with appropriate dimensions, you can pass it through the model.

Here's a general code outline that demonstrates these steps:

```python
import torch

# Sample input text
input_text = "What is the weather today?"

# Tokenization and adding special tokens
input_tokens = nltk.word_tokenize(input_text.lower())
input_tokens = [START_TOKEN] + input_tokens + [END_TOKEN]

# Converting tokens to indices
input_indices = [combined_to_index[token] for token in input_tokens]

# Padding and reshaping
max_input_length = max_sequence_length # Choose an appropriate length
padded_input = input_indices + [combined_to_index[PADDING_TOKEN]] * (max_input_length - len(input_indices))
input_tensor = torch.tensor(padded_input).unsqueeze(0) # Add a batch dimension

# Move the tensor to the appropriate device (GPU if available)
input_tensor = input_tensor.to(device)

# Pass through the model
with torch.no_grad():
model.eval() # Set the model to evaluation mode
output = model(input_tensor, input_tensor) # Pass input_tensor as both src and tgt
predicted_indices = output.argmax(dim=-1) # Get the indices with the highest probability

# Convert predicted indices back to tokens
predicted_tokens = [index_to_combined[idx.item()] for idx in predicted_indices[0]]

# Remove special tokens and padding tokens
predicted_tokens = [token for token in predicted_tokens if token not in [START_TOKEN, END_TOKEN, PADDING_TOKEN]]

# Convert predicted tokens to a readable sentence
predicted_sentence = " ".join(predicted_tokens)
print("Predicted sentence:", predicted_sentence)
```

Please replace `nltk.word_tokenize` with your actual tokenization method. Also, ensure that the `max_input_length` matches the maximum sequence length used during training.

This outline illustrates the process of preparing input text and generating predictions using your transformer model. Make sure to adapt it to fit your code structure and requirements.