The Transformer architecture is an elegant sequence-to-sequence modeling approach introduced by Google researchers in 2017. It uses self-attention mechanisms to process variable-length input sequences and output sequences.
The Transformer architecture is an elegant sequence-to-sequence modeling approach introduced by Google researchers in 2017. It uses self-attention mechanisms to process variable-length input sequences and output sequences.