BERT base model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.

Download the model by cloning the repository via git clone https://huggingface.co/OWG/bert-base-uncased .

You can use the same tokenizer for all of the various BERT models that hugging face provides. As BERT can only accept/take as input only 512 tokens at a time, we must specify the truncation parameter to True. The add special tokens parameter is just for BERT to add tokens like the start, end, [SEP], and [CLS] tokens.

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.