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Google, NYU & Maryland U’s Token-Dropping Approach Reduces BERT Pretraining Time by 25%
March 29, 2022, 2:20 p.m. | Synced
Synced syncedreview.com
In the new paper Token Dropping for Efficient BERT Pretraining, a research team from Google, New York University, and the University of Maryland proposes a simple but effective “token dropping” technique that significantly reduces the pretraining cost of transformer models such as BERT without hurting performance on downstream fine-tuning tasks.
The post Google, NYU & Maryland U’s Token-Dropping Approach Reduces BERT Pretraining Time by 25% first appeared on Synced.
ai artificial intelligence bert google machine learning machine learning & data science ml natural language processing nature language tech nyu pretrained language model research technology time transformers
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