It allows the model to learn a bidirectional representation of the sentence. Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not.

Both cased and uncased models were created for our battery-related BERT models. The cased model keeps the same text in the original papers as input, including both the capitalized and lowercase words, while the uncased models only use the words in lowercase.

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BERT was originally implemented in the English language at two model sizes: (1) BERT_BASE: 12 encoders with 12 bidirectional self-attention heads totaling 110 million parameters, and (2) BERT_LARGE: 24 encoders with 16 bidirectional self-attention heads totaling 340 million parameters.