Four transformer models were trained on D and D c and with and without ranking loss. Every model was trained for 30 epochs, 3 utilizing 8 A100 GPUs, with each training cycle lasting for 30 days. All models were trained following the same strategy and using the same hyperparameters as , including a batch size of 128, Adam optimizer and the original learning rate schedule with 4,000 warmup steps. Due to the computational time required to train a model, we could not optimize λ (see Section 2.1). However, while not necessarily optimal, the value we chose for λ, i.e. λ = 10 already highlights (see Section 3) the benefits from using the ranking loss when assessing the overall quality of the models.