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Status: Unchecked ×Keyword: TinyBERT ×Clear all

3 claims from 1 paper

1. Computer Science › Topic Modeling

   TinyBERT: Distilling BERT for Natural Language Understanding

   Jiao, Yin, Shang et al. · 2020

   The paper proposes a Transformer distillation method and a two-stage training framework to shrink BERT into TinyBERT, keeping most of its accuracy while cutting size and inference time.

   Unchecked3 claims
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   1. UncheckedA 4-layer TinyBERT reaches over 96.8% of BERT-base's GLUE performance while being 7.5 times smaller and 9.4 times faster at inference.“TinyBERT with 4 layers is empirically effective and achieves more than 96.8% the performance of its teacher BERTBASE on GLUE benchmark, while being 7.5x smaller and 9.4x faster on inference.”
   2. UncheckedA 4-layer TinyBERT is reported to beat 4-layer state-of-the-art BERT distillation baselines while using about 28% of their parameters and 31% of their inference time.“TinyBERT with 4 layers is also significantly better than 4-layer state-of-the-art baselines on BERT distillation, with only about 28% parameters and about 31% inference time of them.”
   3. UncheckedThe paper states that a 6-layer TinyBERT performs on par with its teacher model, BERT-base.“Moreover, TinyBERT with 6 layers performs on-par with its teacher BERTBASE.”