In the first part of this work we will assess the use of more traditional ML methods to attempt to learn NMR tensor parameters to demonstrate that a symmetry-invariant model is insufficient for NMR tensor parameter prediction. We show that all models trained herein fall short of predicting tensor elements, and instead a symmetry-equivariant model is needed to respect the symmetries of the tensor, as evidenced by a 47% (3.05 ppm vs. 6.44 ppm) decrease in mean absolute error when compared to invariant models. In the second part of this work we comprehensively assess the performance of our equivariant model. We show that it can learn the full NMR chemical shift tensor to a mean absolute error (MAE) of 1.05 ppm. When converted to the scalar isotropic chemical shift (which previous symmetryinvariant models can predict), our MatTEN model outperforms state-of-the-art model by a large margin, with MAEs of 2.82 ppm vs. 5.87 ppm.