Introduction to Transformer model application

Transformers is a model that uses a self-attention mechanism, which adopts an encoder-decoder architecture to achieve results. Some common Transformer architecture-based models include BERT and RoBERTa.

The Transformer architecture is specially designed to handle sequence-to-sequence problems in natural language processing tasks. Compared with traditional RNN, LSTM and other architectures, the main advantage of Transformer lies in its unique self-attention mechanism. This mechanism enables Transformer to accurately capture long-range dependencies and correlations between tokens in input sentences and greatly reduces computing time. Through the self-attention mechanism, Transformer can adaptively weight each position in the input sequence to better capture contextual information at different positions. This mechanism makes the Transformer more effective in handling long-distance dependencies, resulting in excellent performance in many natural language processing tasks.

This architecture is based on encoder-decoder and consists of multiple layers of encoders and decoders. Each encoder contains multiple sub-layers, including a multi-head self-attention layer and a positional fully connected feed-forward neural network. Likewise, each decoder also has the same two sub-layers, with the addition of a third sub-layer called the encoder-decoder attention layer, which is applied to the output of the encoder stack.

There is a normalization layer after each sub-layer, and there are residual connections around each feedforward neural network. This residual connection provides a free path for gradient and data flow, helping to avoid vanishing gradient problems when training deep neural networks.

The encoder's attention vector is passed to the feedforward neural network, which converts it into a vector representation and passes it to the next attention layer. The decoder’s task is to transform the encoder’s attention vector into output data. During the training phase, the decoder can use the attention vectors and expected results generated by the encoder.

The decoder uses the same tokenization, word embedding and attention mechanisms to process the expected results and generate attention vectors. This attention vector then interacts with the attention layer in the encoder module to establish the association between the input and output values. The decoder attention vector is processed by the feedforward layer and then mapped into a large vector of the target data size.

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