Passwords are common figures in everyone’s everyday life. One person has in average80 accounts for which they are supposed to use different passwords. Remembering allthese passwords is difficult and leads to people reusing, or reusing with slight modification,passwords on many accounts. Studies on memory show that information relating tosomething personal is more easily remembered. This is likely the reason as to why manypeople use passwords relating to either self, relatives, lovers, friends, or pets. Hackers will most often use either brute force or dictionary attacks to crack a password.These techniques can be quite time consuming so using machine learning could bea faster and easier approach. Segmenting someone’s previous passwords into meaningfulunits often reveals personal information about the creator and can thus be used as a basisfor password guessing. This report focuses on evaluating different sizes of the GPT-SW3model, which uses a transformer architecture, on password segmentation. The purposeis to find out if the GPT-SW3 model is suitable to use as a password segmenter and byextension if it can be used for password guessing. As training data, a list of passwords collected from a security breach on a platformcalled RockYou was used. The passwords were segmented by the author to provide themodel with a correct answer to learn from. The evaluation metric, Exact Match, checksif the model’s prediction is the same as that of the author. There were no positive resultswhen training GPT-SW3, most likely because of technical limitations. As the results arerather insufficient, future studies are required to prove or disprove the assumptions thisthesis is based on.