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• W3035030897 abstract "Pre-trained language models like BERT have proven to be highly performant. However, they are often computationally expensive in many practical scenarios, for such heavy models can hardly be readily implemented with limited resources. To improve their efficiency with an assured model performance, we propose a novel speed-tunable FastBERT with adaptive inference time. The speed at inference can be flexibly adjusted under varying demands, while redundant calculation of samples is avoided. Moreover, this model adopts a unique self-distillation mechanism at fine-tuning, further enabling a greater computational efficacy with minimal loss in performance. Our model achieves promising results in twelve English and Chinese datasets. It is able to speed up by a wide range from 1 to 12 times than BERT if given different speedup thresholds to make a speed-performance tradeoff." @default.
• W3035030897 created "2020-06-19" @default.
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• W3035030897 date "2020-01-01" @default.
• W3035030897 modified "2023-10-04" @default.
• W3035030897 title "FastBERT: a Self-distilling BERT with Adaptive Inference Time" @default.
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• W3035030897 doi "https://doi.org/10.18653/v1/2020.acl-main.537" @default.
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