How to Simultaneously Use Sentence, Character, and Word Tokenization in AI Models

AI Discussions
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I have a question regarding the tokenization methods used in large language models like ChatGPT.
Specifically, I am interested in understanding :thinking: how to simultaneously use

  1. sentence tokenization,
  2. character tokenization,
  3. word tokenization
    to process a ‘single sentence’.
    For example, given the sentence:

“I’m really hungry. What should I have for lunch? I can’t think of anything. Maybe I’ll have ramen?”

What criteria are used to choose and combine sentence, character, and word tokenization methods?
How do tokenization methods like ‘Byte Pair Encoding (BPE)’ or WordPiece function in this process?
How does a model determine and optimize the use of these tokenization methods when processing specific text?
I would like to understand the detailed process of handling a sentence using these combined tokenization methods when developing an AI model.
Any references or advice on this topic would be greatly appreciated. thanks

import nltk
nltk.download(‘punkt’)
from nltk.tokenize import [word_tokenize]

text =
“I’m really hungry. What should I have for lunch? I can’t think of anything. Maybe I’ll have ramen?”
[word_tokens = word_tokenize(text)
print(word_tokens)] <-I said this point