This implementation of Transformer model architecture is based on the optimized implementation in Fairseq NLP toolkit.
The Transformer model uses standard NMT encoder-decoder architecture. This model unlike other NMT models, uses no recurrent connections and operates on fixed size context window. The encoder stack is made up of N identical layers. Each layer is composed of the following sublayers: 1. Self-attention layer 2. Feedforward network (which is 2 fully-connected layers) Like the encoder stack, the decoder stack is made up of N identical layers. Each layer is composed of the sublayers: 1. Self-attention layer 2. Multi-headed attention layer combining encoder outputs with results from the previous self-attention layer. 3. Feedforward network (2 fully-connected layers)
The encoder uses self-attention to compute a representation of the input sequence. The decoder generates the output sequence one token at a time, taking the encoder output and previous decoder-outputted tokens as inputs. The model also applies embeddings on the input and output tokens, and adds a constant positional encoding. The positional encoding adds information about the position of each token.
Figure 1. The architecture of a Transformer model.
The complete description of the Transformer architecture can be found in Attention Is All You Need paper.
The following datasets were used to train this model:
Performance numbers for this model are available in NGC.