The transformer model architecture was based on the model described in the paper by but it differed in that learned positional encodings were introduced along with a standard Adam optimizer, and it did not have label smoothing (Supporting information Figure ) . The learning rate for the optimizer was 0.0005. The encoder took in an embedding layer as well as a positional embedding layer, both with a dimension of 256 and a dropout of 0.1. It contained 3 layers of multihead attention and position-wise feed-forward mechanisms. The multi-head attention layers had a dimension of 256 and 8 heads. The position-wise feed-forward mechanism had a dimension of 512 and a Rectified Linear Unit (ReLU) activation function. After each of these layers, a dropout of 0.1 was applied after which layer normalization was performed. The encoder generated as many context vectors as there were tokens. The decoder worked similarly to the encoder, but it had two multi-head attention mechanisms: one that used the target as input and another that used the encoder representations. The decoder also had a linear layer before the prediction.