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I was reading the BERT paper but I didn't find any tables concerning the performance of the masked language models during pretraining. Does anyone know the accuracy of BERT's masked language model?

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It is very hard to tell and researchers usually don't measure it because it's not a comparable number such as GLUE score. It depends on size of your vocabulary and also the masking strategy (for example newer BERT successors use span-masking). BERT is a subword language model (uses WordPiece tokenizer) so if you use large vocab then sentences are usually tokenized to "smaller" subword units that are easier to predict. I trained a BERT-like model for Czech language, which is morphologically more complex than English, and got top@1 test accuracy ~0.55, top@3 test accuracy ~0.68 after 1.5M steps with 30K vocab size.

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