  Transformers are the dominant architecture in NLP, but their training and
fine-tuning is still very challenging. In this paper, we present the design and
implementation of a visual analytic framework for assisting researchers in such
process, by providing them with valuable insights about the model's intrinsic
properties and behaviours. Our framework offers an intuitive overview that
allows the user to explore different facets of the model (e.g., hidden states,
attention) through interactive visualization, and allows a suite of built-in
algorithms that compute the importance of model components and different parts
of the input sequence. Case studies and feedback from a user focus group
indicate that the framework is useful, and suggest several improvements.
