The Transformer model is a neural network architecture that uses self-attention to understand relationships between elements in sequential data like words in a sentence. Unlike recurrent neural networks (RNNs) that process data sequentially, the Transformer can process all words in parallel. It has an encoder to read the input and a decoder to generate the output. Positional encoding accounts for the order of words. The Transformer has achieved state-of-the-art results in machine translation and other language tasks, with less training time and greater parallelization than previous models.

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