Tokenization

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Tokenization is usually the first step in natural language processing. It takes raw text and breaks it into smaller pieces called tokens, which a model can understand and process. These tokens can be full words, subwords, or even single characters. For example, a tokenizer might split a sentence by spaces and punctuation, or it might break a rare word into smaller parts so the model can still represent it.

Modern transformer models use learned tokenization methods that try to keep the vocabulary small while still covering as many word forms as possible. Good tokenization affects many parts of the NLP pipeline: how long sequences become, how well the model handles new or misspelled words, and how much meaning the model can capture. Since every later step depends on the tokens produced at this stage, tokenization is considered a core design choice rather than a minor preprocessing task.

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