A Robustly Optimized BERT Pretraining Approach

Author(s): Edward Ma

Originally published on Towards AI.

What is BERT?

Top highlight

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.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI