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README.md
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---
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license: mit
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---
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---
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license: mit
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---
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We provide both huggingface version and
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[esm version](https://github.com/facebookresearch/esm) of
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SaProt (see our github <https://github.com/SaProt/SaProt>). Users can choose either one to use.
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### Huggingface model
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The following code shows how to load the model.
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```
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from transformers import EsmTokenizer, EsmForMaskedLM
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model_path = "/your/path/to/SaProt_650M_AF2"
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tokenizer = EsmTokenizer.from_pretrained(model_path)
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model = EsmForMaskedLM.from_pretrained(model_path)
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#################### Example ####################
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device = "cuda"
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model.to(device)
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seq = "MdEvVpQpLrVyQdYaKv"
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tokens = tokenizer.tokenize(seq)
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print(tokens)
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inputs = tokenizer(seq, return_tensors="pt")
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inputs = {k: v.to(device) for k, v in inputs.items()}
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outputs = model(**inputs)
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print(outputs.logits.shape)
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"""
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['Md', 'Ev', 'Vp', 'Qp', 'Lr', 'Vy', 'Qd', 'Ya', 'Kv']
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torch.Size([1, 11, 446])
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"""
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```
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### esm model
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The esm version is also stored in the same folder, named `SaProt_650M_AF2.pt`. We provide a function to load the model.
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```
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from utils.esm_loader import load_esm_saprot
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model_path = "/your/path/to/SaProt_650M_AF2.pt"
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model, alphabet = load_esm_saprot(model_path)
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```
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