bert-large-cased

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion . The model is cased: it makes a difference between English and English . It pretrained with two objectives: Masked language modeling (MLM) and next sentence prediction (NSP) The model learns an inner representation of the English language that can then be used to extract features that can be used for downstream tasks like sequence classification, token classification or question answering . This model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)to make decisions, such as sequence classification . For tasks such as textgeneration you should look at,