metadata
library_name: transformers
tags:
- protein
license: bsd-3-clause
ProGen2-small finetuned on 7 protein families:
- PF00002 - GPCRs
- PF00042 - Globins
- PF00125 - Core histones
- PF00127 - Copper binding proteins
- PF00257 - Dehydrins
- PF00262 - Calreticulins
- PF03668 - P-loop ATPase
Bidirectional model trained on both N -> C and C -> N directions of protein sequences, specified by tokens "1" and "2" respectively.
See my github repo for more information.
Example usage:
from transformers import AutoModelForCausalLM
from tokenizers import Tokenizer
import torch
import torch.nn.functional as F
# load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("hugohrban/progen2-small-mix7-bidi", trust_remote_code=True)
tokenizer = Tokenizer.from_pretrained("hugohrban/progen2-small-mix7-bidi")
tokenizer.no_padding()
# prepare input
prompt = "<|pf00125|>2FDDDVSAVKSTGVSK"
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} %")