| 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. |