AutoDisProxyT-QNLI for Distilling Massive Neural Networks
AutoDisProxyT is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models.
This AutoDisProxyT checkpoint with 7 layers, 160 hidden size, 10 attention heads corresponds to 6.88 million parameters and 0.27G FLOPs.
The following table shows the results on GLUE dev set.
Models | #Params (M) | #FLOPs (G) | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | Avg |
---|---|---|---|---|---|---|---|---|---|---|
BERT | 109 | 11.2 | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 53.5 | 82.2 |
BERTSMALL | 66 | 5.66 | 81.8 | 89.8 | 90.6 | 67.9 | 91.2 | 84.9 | 53.5 | 80.0 |
TruncatedBERT | 66 | 5.66 | 81.2 | 87.9 | 90.4 | 65.5 | 90.8 | 82.7 | 41.4 | 77.1 |
DistilBERT | 66 | 5.66 | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 51.3 | 78.6 |
TinyBERT | 66 | 5.66 | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 42.8 | 79.9 |
MiniLM | 66 | 5.66 | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 49.2 | 81.0 |
AutoTinyBERT-KD-S1 | 30.0 | 1.69 | 82.3 | 89.7 | 89.9 | 71.1 | 91.4 | 88.5 | 47.3 | 80.0 |
DynaBERT | 37.7 | 1.81 | 82.3 | 88.5 | 90.4 | 63.2 | 92.0 | 81.4 | 76.4 | 43.7 |
NAS-BERT10 | 10.0 | 2.30 | 76.4 | 86.3 | 88.5 | 66.6 | 88.6 | 79.1 | 34.0 | 74.2 |
AutoTinyBERT-KD-S4 | 66 | 5.66 | 76.0 | 85.5 | 86.9 | 64.9 | 86.8 | 81.4 | 20.4 | 71.7 |
NAS-BERT5 | 66 | 5.66 | 74.4 | 84.9 | 85.8 | 66.6 | 87.3 | 79.6 | 19.8 | 71.2 |
AutoDisProxyT | 6.88 | 0.27 | 79.0 | 86.4 | 89.1 | 64.3 | 85.9 | 78.5 | 24.8 | 72.6 |
Tested with torch 1.6.0
If you use this checkpoint in your work, please cite:
@article{xu2022autodistil,
title={AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models},
author={Xu, Dongkuan and Mukherjee, Subhabrata and Liu, Xiaodong and Dey, Debadeepta and Wang, Wenhui and Zhang, Xiang and Awadallah, Ahmed Hassan and Gao, Jianfeng},
journal={arXiv preprint arXiv:2201.12507},
year={2022}
}
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