The answer
A basic token lookup vector does not. Later contextual layers incorporate the available sequence, so the same initial vector can contribute to different representations in different sentences.
Why are text inputs converted into embeddings before entering a neural network?
Does an embedding vector already contain all surrounding context?
More FAQs in Why are text inputs converted into embeddings before entering a neural network?
Usually it is part of the model itself. Tokenization is a separate preprocessing stage, while the learned embedding lookup participates in the neural network’s computation.
One-hot vectors identify vocabulary entries but are sparse and do not directly encode learned relationships. Dense embeddings provide a practical representation for subsequent neural computations.
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