  This paper introduces transformer-based language models to the literature
measuring corporate culture from text documents. We compile a unique data set
of employee reviews that were labeled by human evaluators with respect to the
information the reviews reveal about the firms' corporate culture. Using this
data set, we fine-tune state-of-the-art transformer-based language models to
perform the same classification task. In out-of-sample predictions, our
language models classify 17 to 30 percentage points more of employee reviews in
line with human evaluators than traditional approaches of text classification.
We make our models publicly available.
