The optimisation process employs the Adam optimiser with a custom learning rate scheduler that adjusts based on the model dimensions, as suggested in the transformer model's original implementation. A custom schedule function defines this learning rate schedule, dynamically modifying the learning rate throughout training. The loss is calculated using the Sparse Categorical Cross-Entropy loss function, with a mechanism to ignore padding tokens. This is achieved by applying a boolean mask that excludes padded elements from the loss computation, ensuring the model does not learn from these irrelevant input parts. Key metrics for evaluation include training loss, which is tracked using a Keras Mean metric, and training accuracy, which is measured using Sparse Categorical Accuracy. These metrics provide critical insights into the model's performance and guide adjustments during the training process.