README.md

---
license: mit
---

<!-- ##### 🔴 <font color=red>Note: SaProt requires structural (SA token) input for optimal performance. AA-sequence-only mode works but must be finetuned - frozen embeddings work only for SA, not AA sequences! With structural input, SaProt surpasses ESM2 in most tasks.</font> -->

We provide two ways to use SaProt, including through huggingface class and 
through the same way as in [esm github](https://github.com/facebookresearch/esm). 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_650M_PDB"
tokenizer = EsmTokenizer.from_pretrained(model_path)
model = EsmForMaskedLM.from_pretrained(model_path)

#################### Example ####################
device = "cuda"
model.to(device)

seq = "MdEvVpQpLrVyQdYaKv"
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)

"""
['Md', 'Ev', 'Vp', 'Qp', 'Lr', 'Vy', 'Qd', 'Ya', 'Kv']
torch.Size([1, 11, 446])
"""
```

### esm model
The esm version is also stored in the same folder, named `SaProt_650M_AF2.pt`. We provide a function to load the model.
```
from utils.esm_loader import load_esm_saprot

model_path = "/your/path/to/SaProt_650M_PDB.pt"
model, alphabet = load_esm_saprot(model_path)
```