readme: TinyBERT: Distilling BERT for Natural Language Understanding
  ======== 
  TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand.
  
  For more details about the techniques of TinyBERT, refer to our paper:
  [TinyBERT: Distilling BERT for Natural Language Understanding](https://arxiv.org/abs/1909.10351)
  
  
  Citation
  ========
  If you find TinyBERT useful in your research, please cite the following paper:
  ```
  @article{jiao2019tinybert,
    title={Tinybert: Distilling bert for natural language understanding},
    author={Jiao, Xiaoqi and Yin, Yichun and Shang, Lifeng and Jiang, Xin and Chen, Xiao and Li, Linlin and Wang, Fang and Liu, Qun},
    journal={arXiv preprint arXiv:1909.10351},
    year={2019}
  }
  ```
  