  The Myers-Briggs Type Indicator (MBTI) is a popular personality metric that
uses four dichotomies as indicators of personality traits. This paper examines
the use of pre-trained language models to predict MBTI personality types based
on scraped labeled texts. The proposed model reaches an accuracy of $0.47$ for
correctly predicting all 4 types and $0.86$ for correctly predicting at least 2
types. Furthermore, we investigate the possible uses of a fine-tuned BERT model
for personality-specific language generation. This is a task essential for both
modern psychology and for intelligent empathetic systems.
