How are the inputs tokenized when model deployment?

Amazon SageMaker
7

Yes, I know that is not possible to predict with inputs longer than 512 and this is in fact what complain me, as what I wanted to do, is to use my personal tokenizer on inference time.


long_sentence = "...." # longer than 512 tokens
sentiment_input= {
   {'inputs':long_sentence,
    'parameters': {'truncation':True}
   }
predictor.predict(sentiment_input)

Seems that this solution is pretty helpful. I didn’t know I could customize the input sentence with parameters. Where can I learn more about this customization? I mean, what other parameter I can customize and where is such documentation?

Thank you very much for your time.