Tokenization

A tokenizer may split text into words and punctuation, or map it through a learned subword vocabulary into integer IDs. Schemes such as byte-pair encoding, WordPiece and unigram tokenization differ in vocabulary construction and segmentation. Normalization, special tokens and postprocessing are part of the full mapping. A pretrained model expects the vocabulary and conventions it learned with, so replacing its tokenizer changes the meaning of its input IDs. Offset mappings connect tokens to character positions when supported. Tokenization is narrower than preprocessing: it determines units and their representation, while cleaning, redaction or sentence selection changes the text supplied to that mapping.

Load the tokenizer matched to a checkpoint and pin its configuration. Inspect examples with punctuation, identifiers, accented characters, code and the languages used in the application. Check encoding and decoding behavior, special-token placement, padding, truncation and offset alignment. If training a new vocabulary, use permitted representative text and keep evaluation corpora out of fitting. Measure token expansion and unknown-unit handling rather than only vocabulary size. The deliverable is a stable text-to-input contract, including documented behavior at boundaries and tests showing that annotations, context limits and decoded outputs remain consistent.

Tokens are computational units and do not always correspond to words, syllables or meaningful concepts. Language-dependent expansion can affect context coverage and cost. Normalization or byte decoding can complicate span alignment. Changing special tokens without updating model handling can produce invalid inputs. A round-trip decode may not reproduce every original formatting detail. Keep tokenizer identity distinct from model architecture, and verify the actual encoded examples rather than assuming whitespace splitting or a familiar tokenizer name establishes compatibility.