xtremedistil-l12-h384-uncased

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-l6-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}
}
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