Regression A more challenging task than classification is to make a regression model, which would allow us to predict the value of a continuous property such as the Henry coefficient for the adsorption of a gas in a porous material. As we are using a pretrained language model, performing actual regression that predicts real numbers (∈ R) is impossible (without changes to the model architecture and training procedure). However, in most, if not all, practical applications, the accuracy for which we can make predictions is always limited. For example, for the Henry coefficient of a material, an accuracy of 1% (or a certain number of decimal points) is sufficient for most applications (see Supplementary Note 11 for discussion on this error source). Hence, we use molecules with Henry coefficients rounded to this accuracy as a training set and assume that the GPT-3 model can interpolate these numbers. Of course, one could also convert this into a classification problem by making tiny bins. For this more challenging regression task, we need more data for tuning the GPT-3 model, and we still get a performance that can approach the state-of-the-art, but as this approach requires much more data, the advantage, except for the ease of training, is less. We obtain a similar conclusion for other regression problems (see Supplementary Note 11) and imbalanced classification cases (Supplementary Note 7.8).