Training the tokenizer selects byte, character, word, or subword pieces from a corpus according to an algorithm. Vocabulary composition affects sequence length, language coverage, and compatibility with the model's embedding and output layers.
A tokenizer vocabulary is the fixed set of token units that a tokenizer maps to integer identifiers.
Training the tokenizer selects byte, character, word, or subword pieces from a corpus according to an algorithm. Vocabulary composition affects sequence length, language coverage, and compatibility with the model's embedding and output layers.