We find that the NMR tensor parameters cannot be easily learned via symmetry invariant processes and often contain algebraic structure that make the learning process more difficult, independent of the tensor convention. By imposing symmetry equivariance, our MatTEN model is able to outperform by 47% the symmetry invariant models, demonstrating that handling the tensorial nature of the target is the key to accurately modeling the system.