NLG using AR models

In the previous section, you have learned how it is possible to train an AR model on your own corpus. As a result, you have trained the GPT-2 version of your own. But the missing answer to the question How can I use it? remains. To answer that, let's proceed as follows:

1. Let's start generating sentences from the model you have just trained, as follows:

   def generate(start, model): 
       input_token_ids = tokenizer_gpt.encode(start, return_tensors='tf') 
       output = model.generate( 
           input_token_ids, 
           max_length = 500, 
           num_beams = 5, 
           temperature = 0.7, 
           no_repeat_ngram_size=2, 
           num_return_sequences=1 
     &...