Nuclear magnetic resonance (NMR) spectroscopy has become a reliable tool for structural investigations in such materials. As a spectroscopy technique, NMR is highly sensitive to the electron density about an atom and relies on local structure rather than any longrange order. NMR measurements are typically combined with powder XRD measurements and ab initio simulations to obtain refined crystal structures in a technique termed NMR Crystallography. These refinement procedures, however, often take an expensive iterative approach as many ab initio NMR calculations are repeated until the results converge. Despite advances in computational power and algorithmic efficiencies, the NMR calculation is still expensive and time consuming. Machine learning (ML) has increasingly been shown to be useful for providing high quality predictions for material properties but with orders of magnitude less computational demand. ML techniques have recently been applied to NMR, with most applications focused on organic molecules and a few focused on Si chemical shift prediction. While chemical shifts are useful as they correlate to the average electronic environment about an atom, shifts are only one piece of the spectrum. The line-shape observed in an NMR measurement is described by a tensor, of which the chemical shift is the isotropic part of the tensor. By ignoring the tensorial nature of the NMR measurement, a myriad of structural information is lost, which is an issue for many previous ML models as they are only capable of predicting scalar quantities.