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How translation happens in Transformer architecture

 Encoder:

  • Tokenize the input French phrase using the trained tokenizer.

  • Add tokens to the input on the encoder side.

  • Pass the tokens through the embedding layer.

  • Feed tokens into the multi-headed attention layers.

  • Outputs of the multi-headed attention layers are processed by a feed-forward network.

  • The encoder output represents the structure and meaning of the input sequence.

Decoder:

  • Insert the encoder output into the middle of the decoder to influence self-attention mechanisms.

  • Add a start-of-sequence token to the decoder input.

  • The decoder uses contextual understanding from the encoder to predict the next token.

  • The output of the decoder's self-attention layers is processed by the decoder feed-forward network.

  • Pass the output through a final softmax output layer to get the first token.

  • Continue the loop, passing the output token back to the input to predict the next token.

  • Repeat until the model predicts an end-of-sequence token.

  • The final sequence of tokens can be detokenized into words to obtain the output translation.
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