This project is about MATE-KD long paper: MATE-KD: Masked Adversarial Text, a Companion to Knowledge Distillation, which is accepted by ACL 2021. In this project, we are interested in KD for model compression and study the use of adversarial training to improve student accuracy using just the logits of the teacher as in standard KD. The paper can be found at https://arxiv.org/abs/2105.05912v1.
- Does MATE-KD
- Python version >= 3.6
- PyTorch version >= 1.5.0
- HuggingFace version == 3.1.0
- To set up this code and develop locally:
-
Set the conda environment
conda env create -f environment.yml pip install transformers==3.1.0 -
Restore the pretrained model in following directories (can be downloaded from Huggingface e.g., https://huggingface.co/distilbert-base-uncased): -- ./bert_models/distilbert-base -- ./bert_models/distilroberta-base -- ./bert_models/uncased_L-6_H-768_A-12 -- ./bert_models/uncased_L-4_H-256_A-4
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Restore the finetuned teacher model in following directories: -- ./ckpts/teachers_roberta_large -- ./ckpts/teachers_bert_base
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Download the GLUE benchmark in following directory: -- ./glue_data
bash adversarial_train_mnli.sh # for BERT-base model
bash run_adversarial_train_mnli_roberta.sh # for roberta-large model
This project's license is under the Apache 2.0 license.
Please cite as:
@inproceedings{rashid-etal-2021-mate,
title = "{MATE}-{KD}: Masked Adversarial {TE}xt, a Companion to Knowledge Distillation",
author = "Rashid, Ahmad and
Lioutas, Vasileios and
Rezagholizadeh, Mehdi",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.86",
doi = "10.18653/v1/2021.acl-long.86",
pages = "1062--1071",
abstract = "The advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks has scaled with size, compression techniques such as knowledge distillation have been key in making them practical. We present MATE-KD, a novel text-based adversarial training algorithm which improves the performance of knowledge distillation. MATE-KD first trains a masked language model-based generator to perturb text by maximizing the divergence between teacher and student logits. Then using knowledge distillation a student is trained on both the original and the perturbed training samples. We evaluate our algorithm, using BERT-based models, on the GLUE benchmark and demonstrate that MATE-KD outperforms competitive adversarial learning and data augmentation baselines. On the GLUE test set our 6 layer RoBERTa based model outperforms BERT-large.",
}