A Robustly Optimized BERT Pretraining Approach
Last Updated on July 25, 2023 by Editorial Team
Author(s): Edward Ma
Originally published on Towards AI.
What is BERT?
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BERT (Devlin et al., 2018) is a method of pre-training language representations, meaning that we train a general-purpose “language understanding” model on a large text corpus (like Wikipedia), and then use that model for downstream NLP tasks that we care about (like question answering). BERT outperforms previous methods because it is the first unsupervised, deeply bidirectional system for pre-training NLP.
Photo by Sara Bakhshi on Unsplash
Liu et al. studied the impact of many key hyper-parameters and training data size of BERT. They found that BERT was significantly undertrained, and can match or exceed the performance of every model published after… Read the full blog for free on Medium.
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