transformer
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A transformer model is a type of neural network that is used for natural language processing (NLP) tasks. It was first introduced in the paper "Attention Is All You Need" by Vaswani et al. (2017). Transformer models are based on the attention mechanism, which allows them to learn long-range dependencies in the input sequence. This makes them well-suited for tasks such as machine translation, text summarization, and question answering. Transformer models are typically composed of an encoder and a decoder. The encoder takes the input sequence as input and outputs a sequence of hidden states. The decoder then takes these hidden states as input and outputs the output sequence. The encoder and decoder are both made up of a stack of self-attention layers. Self-attention is a type of attention mechanism that allows each hidden state to attend to all of the other hidden states in the sequence. This allows the model to learn long-range dependencies in the input sequence. Transformer models have been shown to be very effective at a variety of NLP tasks. They have achieved state-of-the-art results on machine translation, text summarization, and question answering. Here is a more detailed description of the transformer model architecture: Input sequence: The input to the transformer model is a sequence of tokens, such as words or characters. Encoder: The encoder is a stack of self-attention layers. Each self-attention layer takes the hidden states from the previous layer as input and outputs a new set of hidden states. The self-attention layers allow the encoder to learn long-range dependencies in the input sequence. Decoder: The decoder is a stack of self-attention layers. Each self-attention layer takes the hidden states from the encoder and the previous output token as input and outputs a new output token. The self-attention layers allow the decoder to generate the output sequence while attending to the input sequence. Output sequence: The output of the transformer model is a sequence of tokens, such as words or characters. The output sequence is a translation, summary, or answer to the input sequence. |