The scale and diversity of this dataset makes it particularly suited for use in training deeplearning models to solve word problems.
there is a significant amount of unwanted noise in the dataset, including problems with incorrect solutions, problems that are unsolvable without brute-force enumeration of solutions, and rationales that contain few or none of the steps required to solve the corresponding problem.