What is tokenization? Mapping text into model inputs
Understand segmentation and token IDs, subwords versus linguistic words, and checks for matching vocabularies and special markers when changing models.
Understand the concept
Tokenization converts text into token sequences and numerical input representations. Common methods use subwords or bytes rather than necessarily complete linguistic words. Tokenizers depend on the vocabulary and rules used during training. IDs cannot be freely reused across models, and successful segmentation does not establish factual understanding.
Subwords balance vocabulary size and unseen text
Whole-word vocabularies can grow large and struggle with unseen words. Subword methods decompose uncommon words into smaller known units. Segmentation depends on the algorithm and learned vocabulary, so tokenizers may split the same word differently.
Hugging Face explains methods including BPE and Unigram, with byte-level approaches offering different coverage. Calling a model does not require implementing them, but tokens are not inherently semantic units. Chinese text without spaces does not imply one token per character.
Check tokenizers and special markers when switching models
An ID can represent different content in another vocabulary, so match model and tokenizer. Chat models may rely on role boundaries and end markers. Replacing weights while keeping an old template can cause repetition or termination problems even without an immediate error.
Load corresponding configuration through supported methods and inspect encoding and decoding of a short input. Record cleaning that changes spaces or symbols to avoid silently altering source text. This is a checking method, not a complete compatibility test.
Count and truncate the actual input representation
Character length does not precisely determine model length. Recount after adding roles and materials. Truncation must preserve factual qualifications, and tables should not retain amounts while losing the headers that identify them.
Languages and code formats affect token efficiency, but efficiency is not capability. Compare counts and task outcomes for equivalent content. Tokenization represents input; facts still need original evidence, regardless of neat segmentation.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.