To the best of our knowledge, no previous model has been proposed to predict full shift tensors, thus benchmarking will take place in two steps. The current state-of-the-art model for Si scalar NMR parameter prediction was introduced by Chaker et al., who use linear ridge regression (LRR) over the Smooth Overlap of Atomic Positions (SOAP) features to predict the Si isotropic chemical shift. We reimplemented this approach using the SOAP features generated by DScribe 59 and the LRR in scikit-learn. During the invariant-target benchmark an LRR-SOAP model will be trained to predict the three eigenvalues of the chemical shift tensor and all models will be compared on their predictions of the eigenvalues.