# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("westlake-repl/SaProt_35M_AF2")
model = AutoModelForMaskedLM.from_pretrained("westlake-repl/SaProt_35M_AF2")Quick Links
We provide two ways to use SaProt, including through huggingface class and through the same way as in esm github. Users can choose either one to use.
Huggingface model
The following code shows how to load the model.
from transformers import EsmTokenizer, EsmForMaskedLM
model_path = "/your/path/to/SaProt_35M_AF2"
tokenizer = EsmTokenizer.from_pretrained(model_path)
model = EsmForMaskedLM.from_pretrained(model_path)
#################### Example ####################
device = "cuda"
model.to(device)
seq = "M#EvVpQpL#VyQdYaKv" # Here "#" represents lower plDDT regions (plddt < 70)
tokens = tokenizer.tokenize(seq)
print(tokens)
inputs = tokenizer(seq, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model(**inputs)
print(outputs.logits.shape)
"""
['M#', 'Ev', 'Vp', 'Qp', 'L#', 'Vy', 'Qd', 'Ya', 'Kv']
torch.Size([1, 11, 446])
"""
esm model
The esm version is also stored in the same folder, named SaProt_35M_AF2.pt. We provide a function to load the model.
from utils.esm_loader import load_esm_saprot
model_path = "/your/path/to/SaProt_35M_AF2.pt"
model, alphabet = load_esm_saprot(model_path)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="westlake-repl/SaProt_35M_AF2")