THIS PROTEIN VEC ADAPTATION WAS MODIFIED FROM https://github.com/tymor22/protein-vec
All credit for the original work goes to Tymor Hamamsy and the following authors of this paper https://www.biorxiv.org/content/10.1101/2023.11.26.568742v1
We have added a Huggingface compatible wrapper for the model in protvec.py
Please consider liking the model page and starring the github repo if you are going to use it :)
https://huggingface.co/lhallee/ProteinVec
https://github.com/lhallee/ProteinVecHuggingface
Clone and install
git clone https://github.com/lhallee/ProteinVecHuggingface.git
pip install torch pytorch_lightning transformers
To use from hugggingface
from transformers import T5Tokenizer
from protvec import ProteinVec, ProteinVecConfig
tokenizer = T5Tokenizer.from_pretrained('lhallee/ProteinVec')
model = ProteinVec.from_pretrained('lhallee/ProteinVec', config=ProteinVecConfig())
Embed a single sequence with embed
model.to_eval()
model = model.cuda() # remove if cpu inference
embedding = model.embed('SEQWENCE').detach().cpu() # torch.tensor(1, 512)
Use a particular AspectVec by setting the inference_mask
model.aspect_to_keys_dict # dictionary showing the aspects
### The model is set to ALL by default to use full ProteinVec
model.inference_mask = model.get_mask('EC') # for Enzyme Comission AspectVec
embedding = model.embed(...)
To half precision weights
model.to_half()
Copyright (c) 2023, Tymor Hamamsy
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