Knowledge distillation for BERT model

Run command below to install the environment

conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
pip install -r requirements.txt

where L_CE is the CrossEntropy loss, DS is the usual Distillation loss, and PT is the proposed loss. Please see our paper below for more details.

Modify the HOME_DATA_FOLDER in envs.py and put all data under it (by default it is ./data), RTE data is uploaded for your convenience.

• The folder name under HOME_DATA_FOLDER should be
  • data_raw: store the raw datas of all tasks. So put downloaded raw data under here
    • MRPC
    • RTE
    • ... (other tasks)
  • data_feat: store the tokenized data under this folder (optional)
    • MRPC
    • RTE
    • ...
• models
  • pretrained: put downloaded pretrained model (bert-base-uncased) under this folder

Run NLI_KD_training.py to start training, you can set DEBUG = True to run some pre-defined arguments

• set argv = get_predefine_argv('glue', 'RTE', 'finetune_teacher') or argv = get_predefine_argv('glue', 'RTE', 'finetune_student') to start the normal fine-tuning
• run run_glue_benchmark.py to get teacher's prediction for KD or PKD.
  • set output_all_layers = True for patient teacher
  • set output_all_layers = False for normal teacher
• set argv = get_predefine_argv('glue', 'RTE', 'kd') to start the vanilla KD
• set argv = get_predefine_argv('glue', 'RTE', 'kd.cls') to start the vanilla KD

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

If you find this code useful for your research, please consider citing:

@article{sun2019patient,
title={Patient Knowledge Distillation for BERT Model Compression},
author={Sun, Siqi and Cheng, Yu and Gan, Zhe and Liu, Jingjing},
journal={arXiv preprint arXiv:1908.09355},
year={2019}
}

Paper is available at here.