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Transformers are a type of deep learning model that was introduced in 2017 by Vaswani et al. in the paper "Attention Is All You Need." The Transformer model is primarily designed for sequence-to-sequence tasks, such as machine translation, text summarization, and text generation. Unlike traditional recurrent neural networks (RNNs), Transformers rely on self-attention mechanisms to process input sequences in parallel, making them more efficient and scalable.