In light of the success of transferring language models into NLP tasks, we ask whether the full BERT model is always the best and does it exist a simple but effective method to find the winning ticket in state-of-the-art deep neural networks without complex calculations. We construct a series of BERT-based models with different size and compare their predictions on 8 binary classification tasks. The results show there truly exist smaller sub-networks performing better than the full model. Then we present a further study and propose a simple method to shrink BERT appropriately before fine-tuning. Some extended experiments indicate that our method could save time and storage overhead extraordinarily with little even no accuracy loss.
@article{arxiv.2111.10951,
title = {Can depth-adaptive BERT perform better on binary classification tasks},
author = {Jing Fan and Xin Zhang and Sheng Zhang and Yan Pan and Lixiang Guo},
journal= {arXiv preprint arXiv:2111.10951},
year = {2022}
}