Instructions to use westlake-repl/SaProt_650M_PDB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use westlake-repl/SaProt_650M_PDB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="westlake-repl/SaProt_650M_PDB")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("westlake-repl/SaProt_650M_PDB") model = AutoModelForMaskedLM.from_pretrained("westlake-repl/SaProt_650M_PDB") - Notebooks
- Google Colab
- Kaggle
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license: mit
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##### 🔴 <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>
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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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license: mit
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<!-- ##### 🔴 <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> -->
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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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