Tokenization in LLMs

Question: What is tokenization in Large Language Models (LLMs), and why is it important?

Answer: Tokenization is the process of splitting text into smaller units called tokens, which can be words, subwords, or even individual characters. It is crucial for LLMs because:

It enables the model to process text efficiently.
Subword tokenization helps handle out-of-vocabulary (OOV) words by breaking them into known parts.
Smaller tokens allow the model to generalize better across different languages and contexts.

        
        from transformers import AutoTokenizer
      
        # Load a pre-trained tokenizer
      
        tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
      
        # Tokenize a sentence
      
        tokens = tokenizer.tokenize("Tokenization is important.")
      
        # Convert tokens to input IDs
      
        input_ids = tokenizer.convert_tokens_to_ids(tokens)