
BERTology
There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT
(that some call "BERTology"). Some good examples of this field are:

BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick:
  https://arxiv.org/abs/1905.05950
Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D.
  Manning: https://arxiv.org/abs/1906.04341
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://arxiv.org/abs/2210.04633

In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to
help people access the inner representations, mainly adapted from the great work of Paul Michel
(https://arxiv.org/abs/1905.10650):

accessing all the hidden-states of BERT/GPT/GPT-2,
accessing all the attention weights for each head of BERT/GPT/GPT-2,
retrieving heads output values and gradients to be able to compute head importance score and prune head as explained
  in https://arxiv.org/abs/1905.10650.

To help you understand and use these features, we have added a specific example script: bertology.py while extract information and prune a model pre-trained on
GLUE.