Building a Python interface to interact with the model

In this section, we will first save the model and then build an interface to interact with our trained model.

Let’s first save the model if we choose to.

Saving the model

The following code will save the model’s files:

# Specify a directory to save your model and tokenizer
save_directory = "/content/model"
# If your model is wrapped in DataParallel, access the original model using .module and then save
if isinstance(model, torch.nn.DataParallel):
    model.module.save_pretrained(save_directory)
else:
    model.save_pretrained(save_directory)
# Save the tokenizer
tokenizer.save_pretrained(save_directory)

The saved /content/model directory contains:

• tokenizer_config.json: Configuration details specific to the tokenizer.
• special_tokens_map.json: Mappings for any special tokens.
• vocab.txt: The vocabulary of tokens that the tokenizer can recognize.
• added_tokens...