A multi-head self-attention layer is a crucial component of transformer models that allows the model to focus on different parts of the input sequence simultaneously by applying multiple attention mechanisms in parallel. This design enhances the model's ability to capture diverse relationships and dependencies within the data, improving its overall performance in tasks like translation, summarization, and more.
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