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license: mit |
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--- |
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We provide two ways to use SaProt, including through huggingface class and |
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through the same way as in [esm github](https://github.com/facebookresearch/esm). 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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``` |
