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2021.acl-long.400.pdf
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🔗 Source: aclanthology.org
📊 Size: 1.4 MB
👤 Author: Yichun Yin ; Cheng Chen ; Lifeng Shang ; Xin Jiang ; Xiao Chen ; Qun Liu
⬇️ Downloads: 85

Summary

AutoTinyBERT is a method for automatic hyper-parameter optimization for efficient pre-trained language models. It uses one-shot Neural Architecture Search (NAS) to search for optimal hyper-parameters for specific latency constraints, outperforming existing methods such as NAS-BERT, DistilBERT, TinyBERT, and MobileBERT. The method involves training a SuperPLM, which acts as a proxy for all potential sub-architectures, and then using a search algorithm to find the best hyper-parameters. Experiments show that AutoTinyBERT consistently outperforms baselines under different latency constraints, and a fast development method is proposed that can develop an AutoTinyBERT for a specific constraint with about 50% of the training time of a conventional PLM.

Description

AutoTinyBERT is a method for automatic hyper-parameter optimization for efficient pre-trained language models.

Technical Information

  • File Format: PDF
  • File Size: 1.4 MB
  • Pages: 12
  • Language: EN
  • Author: Yichun Yin ; Cheng Chen ; Lifeng Shang ; Xin Jiang ; Xiao Chen ; Qun Liu
  • Total Downloads: 85
  • Last Updated: 15 hours ago

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