BAIYUN SI / AI LEXICON
Transformer Model architecture based on attention mechanisms
In 2017, a neural network architecture centered on attention mechanisms was proposed.
Transformer was proposed by the paper Attention Is All You Need. The original paper studied sequence transformation tasks, using attention mechanisms to establish relationships between input and output information.
The attention mechanism allows the model to combine information from other locations in the sequence when processing the current position. The original Transformer consists of an encoder and a decoder, and subsequent research has developed different structures based on this.
A Transformer is an architecture, not a single product, nor is it the same as all AI. The capability of a real model also depends on factors such as training data, training objectives, scale, and engineering implementation.
Relationship with the Baiyun community
Understanding some underlying structure helps establish reasonable expectations about the tools discussed in the Baiyun community. You don't need to become a researcher to start practicing, but it's worth constantly asking: why is it effective, and where is it prone to error?
Further reading
Returning to the journey of light Join the Baiyun SI community