AI transformers rely on tokens, which are the smallest units of language, to construct meaning in natural language processing. Tokens can be compared to atoms that compose larger structures like sentences. Each token represents critical components of language, while embeddings give these tokens a universal identity, allowing them to form connections and context. By combining tokens and embeddings, transformers achieve impressive results in translation and text generation, showcasing their near-human linguistic capabilities.
Transformers utilize tokens, which are the fundamental units of language, to construct meaning in natural language processing tasks. Tokens serve as the basic building blocks, similar to atoms in molecules.
Embeddings are essential for transforming tokens into meaningful representations. They provide a universal descriptor for tokens, allowing transformers to leverage their mathematical relationships to generate contextually aware language outputs.
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