In this evaluation, the projected embeddings were used to create the training and test sets and for creating the classifier's features, since this configuration was found to result in the best performance (Section 8.4).
the parameters (optimized using grid search) and the AUC are as follows: Logistic regression produced an AUC of 0.75.