Here, we describe the mathematics behind the transformer-based, deep learning and model agnostic methods investigated in this paper. The methods utilize some common parameters and variables including the sequence length S, embedding dimension E, number of attention heads h, and head-specific embedding dimension d = E/h, number of layers L. Furthermore, we define x as the tokenized input sequence, Ames prediction model f (x) = y, y ∈ R 2 . XAI attributions are given as ϕ ∈ R S×E , where E can equal S depending on the XAI method. Because the dimensions of the embedding of ϕ can be different based on method, we average over the embedding space to make ϕ i = 1 n n j=0 ϕ i,j , which means that ϕ i , i ∈ (1, ..., S) is consistent between methods.