One of the key advantages of transformer networks is the ability to take a model that was pretrained over vast quantities of text and fine-tune it for the task at hand. Intuitively, this strategy allows transformer networks to achieve higher performance on smaller datasets by relying on statistics acquired at scale in an unsupervised way (e.g., through the masked language model training objective). To this end, in this chapter, we will use the Hugging Face library, which has a rich repository of datasets and pretrained models, as well as helper methods and classes that make it easy to target downstream tasks. Using pretrained transformer encoders, we will implement the two tasks that served as use cases in the previous chapters: text classification and part-of-speech tagging.