BERT Pre-training › BERT Pre-training
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Pre-training is where BERT learns language. Starting from random weights, the model processes billions of words and emerges with representations that capture syntax and semantics, along with some world knowledge. This transformation happens through two complementary objectives: predicting masked tokens and recognizing whether sentences belong together.
Understanding BERT's pre-training reveals why it works so well. The training data, masking strategies, auxiliary objectives, and hyperparameters all interact to produce a model that transfers effectively to downstream tasks. This chapter walks through each component: how the data is prepared, how the objectives shape learning, what hyperparameters matter, and how long training takes to converge.