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XtremeDistilTransformers for Distilling Massive Neural Networks

XtremeDistilTransformers 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 XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation.

We leverage task transfer combined with multi-task distillation techniques from the papers XtremeDistil: Multi-stage Distillation for Massive Multilingual Models and MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers with the following Github code.

This l6-h384 checkpoint with 6 layers, 384 hidden size, 12 attention heads corresponds to 22 million parameters with 5.3x speedup over BERT-base.

Other available checkpoints: xtremedistil-l6-h256-uncased and xtremedistil-l12-h384-uncased

The following table shows the results on GLUE dev set and SQuAD-v2.

Models #Params Speedup MNLI QNLI QQP RTE SST MRPC SQUAD2 Avg
BERT 109 1x 84.5 91.7 91.3 68.6 93.2 87.3 76.8 84.8
DistilBERT 66 2x 82.2 89.2 88.5 59.9 91.3 87.5 70.7 81.3
TinyBERT 66 2x 83.5 90.5 90.6 72.2 91.6 88.4 73.1 84.3
MiniLM 66 2x 84.0 91.0 91.0 71.5 92.0 88.4 76.4 84.9
MiniLM 22 5.3x 82.8 90.3 90.6 68.9 91.3 86.6 72.9 83.3
XtremeDistil-l6-h256 13 8.7x 83.9 89.5 90.6 80.1 91.2 90.0 74.1 85.6
XtremeDistil-l6-h384 22 5.3x 85.4 90.3 91.0 80.9 92.3 90.0 76.6 86.6
XtremeDistil-l12-h384 33 2.7x 87.2 91.9 91.3 85.6 93.1 90.4 80.2 88.5

Tested with tensorflow 2.3.1, transformers 4.1.1, torch 1.6.0

If you use this checkpoint in your work, please cite:

@misc{mukherjee2021xtremedistiltransformers,
      title={XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation}, 
      author={Subhabrata Mukherjee and Ahmed Hassan Awadallah and Jianfeng Gao},
      year={2021},
      eprint={2106.04563},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
MIT License Copyright (c) Microsoft Corporation. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE

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