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In Transformer architectures, token embeddings map tokens into a high-dimensional vector space where closeness in the space means closeness in semantic meaning. These vectors are dense, trained such that words sharing semantic traits cluster together, allowing the model to measure similarity through dot products (cosine similarity). This spatial arrangement is structured, preserving relationships as directional offsets in the vector space. This property allows for vector arithmetic; in the class
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