Research in natural language processing (NLP) has provided strategies to leverage large amounts of unlabeled data to train generalizable language models for text generation and prediction . Masked language models are typically developed using two distinct stages, known as pre-training and fine-tuning. Pretraining is completely unsupervised (i.e., doesn't require any manual labeling) and, therefore, can be performed on very large data sets. In fine-tuning, the pretrained model is further trained for a specific task (e.g., document classification) using a labeled data set that is typically much smaller. Using this two-stage approach, language models have achieved state-of-the-art results for a range of NLP tasks for multiple application areas .