readme: ## 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](https://github.com/huggingface/transformers/tree/v2.1.1), and is released in [GitHub](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/DynaBERT).
  
  ### Reference
  Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu.
  [DynaBERT: Dynamic BERT with Adaptive Width and Depth](https://arxiv.org/abs/2004.04037).
  ```
  @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}
  }
  ```
  