Instructions to use westlake-repl/SaProt_35M_AF2_seqOnly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use westlake-repl/SaProt_35M_AF2_seqOnly with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="westlake-repl/SaProt_35M_AF2_seqOnly")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("westlake-repl/SaProt_35M_AF2_seqOnly") model = AutoModelForMaskedLM.from_pretrained("westlake-repl/SaProt_35M_AF2_seqOnly") - Notebooks
- Google Colab
- Kaggle
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README.md
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license: mit
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This model is provided to compare with official ESM-2 35M model. It only receives residue sequence but shares the same vocabulary with normal SaProt,
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which means all structure tokens are marked as ``#`` during training.
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### Huggingface model
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The following code shows how to load the model.
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license: mit
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---
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This model is provided to compare with official ESM-2 35M model. It only receives residue sequence but shares the same vocabulary with normal SaProt,
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which means all structure(3Di) tokens are marked as ``#`` during training.
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### Huggingface model
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The following code shows how to load the model.
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