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ProtBert model

Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids.

Model description

ProtBert is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences.

One important difference between our Bert model and the original Bert version is the way of dealing with sequences as separate documents. This means the Next sentence prediction is not used, as each sequence is treated as a complete document. The masking follows the original Bert training with randomly masks 15% of the amino acids in the input.

At the end, the feature extracted from this model revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences.

Intended uses & limitations

The model could be used for protein feature extraction or to be fine-tuned on downstream tasks. We have noticed in some tasks you could gain more accuracy by fine-tuning the model rather than using it as a feature extractor.

How to use

You can use this model directly with a pipeline for masked language modeling:

>>> from transformers import BertForMaskedLM, BertTokenizer, pipeline
>>> tokenizer = BertTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False )
>>> model = BertForMaskedLM.from_pretrained("Rostlab/prot_bert")
>>> unmasker = pipe

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