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