Update README.md

README.md

@@ -1,3 +1,43 @@
 ---
 license: mit
 ---
+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_35M_AF2"
+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_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)
+```