The Transformer architecture is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. It is primarily used in natural language processing (NLP) tasks such as machine translation, text generation, and sentiment analysis, but has also found applications in computer vision and other fields. Its parallelizable structure and ability to capture long-range dependencies make it highly effective.
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