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Transformer architectures have fundamentally changed the landscape of natural language processing. The self-attention mechanism allows the model to weigh the importance of different parts of the input...
Unlike recurrent neural networks, transformers process all positions simultaneously, enabling significantly faster training on modern hardware accelerators.
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