Papers with speed-performance tradeoff

1 papers
FastBERT: a Self-distilling BERT with Adaptive Inference Time (2020.acl-main)

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Challenge: Pre-trained language models like BERT have proven to be highly performant, but are often computationally expensive in many practical scenarios.
Approach: They propose a speed-tunable FastBERT with adaptive inference time that can be flexibly adjusted under varying demands.
Outcome: The proposed model achieves promising results in English and Chinese datasets.

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