Does tokenization remove the need for embeddings?
No. Tokenization supplies discrete units and IDs, while embeddings provide learned numerical vectors. These steps serve different purposes in a typical neural language-model pipeline.
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More FAQs in What is tokenization, and why is it necessary for LLMs?
Yes. Models can be designed around characters or bytes, but those are still computational input units. Many widely used text models instead choose subword vocabularies.
No. Space-based splitting misses punctuation, languages without spaces, and subword structure. Tokenizers use model-specific rules that can divide or combine text in less obvious ways.
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