SmartBERT

🔗 This repo is Pytorch implemention of SmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference.

Environment

💻 Recommand you to set up a Python virtual environment with the required dependencies as follows:

conda create -n SmartBert python=3.8
pip install -r requirements.txt

Datasets

📖 We conducted experiments using the GLUE dataset(SST-2, MRPC, RTE, QNLI, QQP, MNLI, CoLA). All datasets can be downloaded from here, or by running the following Python script.

python download_glue.py

Usage

📜 Command for training and evaluating model:

sh run.sh

Please note that if you need to switch datasets, you'll need to modify both the parameters --data_dir and --task_name.

Citation

Please cite our paper if you find the method useful:

@inproceedings{ijcai2023p563,
  title     = {SmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference},
  author    = {Hu, Boren and Zhu, Yun and Li, Jiacheng and Tang, Siliang},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {5067--5075},
  year      = {2023},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2023/563},
  url       = {https://doi.org/10.24963/ijcai.2023/563},
}