DynaBERT: Dynamic BERT with Adaptive Width and Depth

• DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and the subnetworks of it have competitive performances as other similar-sized compressed models. The training process of DynaBERT includes first training a width-adaptive BERT and then allowing both adaptive width and depth using knowledge distillation.

• This code is modified based on the repository developed by Hugging Face: Transformers v2.1.1, and is released in GitHub.

Reference

Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu. DynaBERT: Dynamic BERT with Adaptive Width and Depth.

@inproceedings{hou2020dynabert,
  title = {DynaBERT: Dynamic BERT with Adaptive Width and Depth},
  author = {Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu},  
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2020}
}