number six joint training on label data and unlabeled data outperforms the pipeline that first pre-trains with unlabeled data and then fine-tunes on labeled data so this is in contrast to like what people have done before in the self-supervised learning and so on where it's always kind of pre-training then fine-tuning or in the in the transfer learning setting seven using a large ratio between unlabeled batch size and label batch size enables models to train longer on non-labeled data to it to achieve a higher accuracy okay