This is the one-directional model trained on 7 protein families:
- PF00002 - GPCRs
- PF00042 - Globins
- PF00125 - Core histones
- PF00127 - Copper binding proteins
- PF00257 - Dehydrins
- PF00262 - Calreticulins
- PF03668 - P-loop ATPase
Check out the github repo for more information.
Example usage:
from transformers import AutoModelForCausalLM
from tokenizers import Tokenizer
# optionally use local imports
# from models.progen.modeling_progen import ProGenForCausalLM
# from models.progen.configuration_progen import ProGenConfig
import torch
import torch.nn.functional as F
# load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("hugohrban/progen2-small-mix7", trust_remote_code=True)
tokenizer = Tokenizer.from_pretrained("hugohrban/progen2-small-mix7")
tokenizer.no_padding()
# prepare input
prompt = "<|pf03668|>1MEVVIVTGMSGAGK"
input_ids = torch.tensor(tokenizer.encode(prompt).ids).to(model.device)
# forward pass
logits = model(input_ids).logits
# print output probabilities
next_token_logits = logits[-1, :]
next_token_probs = F.softmax(next_token_logits, dim=-1)
for i in range(tokenizer.get_vocab_size(with_added_tokens=False)):
print(f"{tokenizer.id_to_token(i)}: {100 * next_token_probs[i].item():.2f} %")