For 1 H-NMR and 13 C-NMR data, we used the QM9NMR dataset, which has gas-and solvent-phase chemical shifts computed at the mPW1PW91/6-311+G(2d,p) for all QM9 molecules at geometry optimized conformations computed at the B3LYP/6-31G(2df,p) level. We used the hydrogen-and carbon-site embeddings as the input representation for training on the prediction of gas-phase 1 H-NMR and 13 C-NMR. The model was trained using an 8000 molecule randomized dataset from QM9NMR, achieving a RMSE of 2.69 ppm for carbon NMR and 0.20 ppm for proton NMR on 2000 molecule separate test set, which is comparable to full-fledged training on significantly larger datasets. For instance, the highly accurate kernel regression model that was applied at the inception of the QM9NMR dataset achieves a mean error of 1.9 ppm for carbon chemical shifts. Both carbon and proton NMR results are within the accuracy of density functional methods.