Transformer architecture is a neural network framework designed for processing sequential data, particularly in natural language processing tasks. It uses mechanisms called attention to weigh the importance of different words in a sequence, allowing for more effective context understanding. This structure significantly improves the performance of models in generating and interpreting text, making it foundational in various applications such as language modeling and response generation.
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