Fine-tuning an application for one of these tasks proceeds just as with pretraining using the NSP objective. During fine-tuning, pairs of labeled sentences from the supervised training data are presented to the model. As with sequence classification, the output vector associated with the prepended [CLS] token represents the model's view of the input pair. And as with NSP training, the two inputs are separated by the a [SEP] token. To perform classification, the [CLS] vector is multiplied by a set of learning classification weights and passed through a softmax to generate label predictions, which are then used to update the weights.