  Transformers, currently the state-of-the-art in natural language
understanding (NLU) tasks, are prone to generate uncalibrated predictions or
extreme probabilities, making the process of taking different decisions based
on their output relatively difficult. In this paper we propose to build several
inductive Venn--ABERS predictors (IVAP), which are guaranteed to be well
calibrated under minimal assumptions, based on a selection of pre-trained
transformers. We test their performance over a set of diverse NLU tasks and
show that they are capable of producing well-calibrated probabilistic
predictions that are uniformly spread over the [0,1] interval -- all while
retaining the original model's predictive accuracy.
