# 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](https://github.com/huggingface/transformers/tree/main/examples/research_projects/bertology/run_bertology.py) while extract information and prune a model pre-trained on

GLUE.