Tell me more about transformer architechtures
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AI systems today leverage transformer architectures that process input through multiple attention layers, enabling contextual understanding at scale. The key components include:
Self-Attention Mechanism — Allows the model to weigh the importance of different parts of the input relative to each other, creating rich contextual representations.
Positional Encoding — Since transformers don’t process data sequentially, positional encodings inject order information so the model understands sequence.