To improve the generalizability of our model and reduce the risk of overfitting, we leverage several techniques to further augment this dataset during training, in addition to the traditional regularization techniques of dropout and weight decay typically employed in deep learning. First, we chose a random number of peaks between 5 and 50, randomly sampled from the entire spectrum with probability proportional to their intensity. Further, the m/z value of each peak was slightly perturbed by a random amount of uniform noise with magnitude 0.02, thus making the model resilient to measurement noise. Each peak was paired with the corresponding neutral loss, and both were encoded with a sinusoidal position encoding to a dimensionality of 512. The frequencies for the sinusoidal encoding included the atomic masses of H, C, N, O, Cl, S, P, K, F, and Br, and 246 additional frequencies between 3 -5 (0.0041) and 3 (2,187) evenly distributed in base three log-space.