Let's Build Our Own LLM (Part 1): Tokenization and Data Prep
How LLMs turn text into numbers: BPE tokenization explained step by step, why your choice of tokenizer shapes model quality, and building a data pipeline.
The text source describes the process of tokenization, a critical step in natural language processing that breaks down text into smaller units called tokens, which the model treats as indivisible units. This process is often overlooked, yet it significantly impacts a model's performance on tasks like counting letters or reversing strings.
The article explains that a token can be a whole word, a piece of a word, a punctuation mark, or even a single character. For example, the word "strawberry" might be tokenized as "str," "aw," and "berry." The choice of tokenizer and the resulting tokenization can greatly affect a model's ability to understand and process language, as demonstrated by the example of "myocardial," which could be tokenized as four separate pieces if the model was trained on general web text, or as a single token if it was trained on medical text.
The article also introduces Byte Pair Encoding (BPE), an algorithm used to create a vocabulary of tokens by iteratively merging the most frequent pairs of adjacent symbols in a training corpus.
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