Train a final model with the full data
The last step for production models is typically to train with the entire set (train + validation) after tuning the hyperparameters using the validation set(s). The difference is typically not too drastic as the validation set should only be a fraction of the dataset but more data is always helpful especially for DL-based models.
I'm not familiar with the library you are using but perhaps you could just set the validation data to 0 and ignore the evaluation it prints.