 Training  After multiple test runs of finetuning the present model was further trained using the following parameters: - foundation-model: mT5-base - input_prefix: "summarize: " - num_train_epochs: 10 - learning_rate: 5e-5 - warmup_ratio: 0.3 - lr_scheduler_type: constant_with_warmup - per_device_train_batch_size: 3 - gradient_accumulation_steps: 2 - fp16: False  Every 5000 steps a checkpoint is stored and evaluated on the validation set. After the training, the checkpoint with the best cross-entropy loss on the validation set is saved as the final model.  