The general model architecture for training SkipSpecies models is adapted from SkipAtom 30 and consists of an input layer of 336 neurons, a hidden embedding layer with d neurons and an output layer with 336 neurons with a softmax activation. The main modification compared to SkipAtom is that the input and output layers contain 336 neurons instead of 86 neurons to account for each unique species in the dataset. For the hidden dimension d, models with the following dimensions are chosen: 30, 86, 100, 200, 300, 400. The input to this model is the one-hot representation of the target species and the "fake" target variable is the one-hot representation of a species that co-occurs with the target. The loss function for this model is the cross-entropy loss between the one-hot representation of the target species and the probabilities produced by the softmax activation on the output layer of the model. The distributed representations are obtained from the embedding layer.