bert-base-multilingual-uncased

BERT multilingual base model (uncased) pretrained on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective . This model is uncased: it does not make a difference between English and English . The model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)to make decisions, such as sequence classification, token classification or question answering . For tasks such as textgeneration you should look at model like GPT2. For example, the BertT model can be used to train a standard classifier using the features produced by the BERT model as inputs . For instance, the model is used to,