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--- |
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base_model: westlake-repl/SaProt_650M_AF2 |
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library_name: peft |
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--- |
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# Base model: [westlake-repl/SaProt_650M_AF2](https://huggingface.co/westlake-repl/SaProt_650M_AF2) |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is used to predict signal peptides on each site of amino acid sequences. |
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### Task type |
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Residue level clssification |
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### Dataset description |
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The dataset is from [SignalP 6.0 predicts all five types of signal |
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peptides using protein language models](https://www.nature.com/articles/s41587-021-01156-3). |
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This dataset contains 7 classes: |
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S (0): Sec/SPI signal peptide | T (1): Tat/SPI or Tat/SPII signal peptide | L (2): Sec/SPII signal peptide | |
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P (3): Sec/SPIII signal peptide | I (4): cytoplasm | M (5): transmembrane | O (6): extracellular |
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### Model input type |
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Amino acid sequence |
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### Performance |
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test_acc: 0.96 |
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### LoRA config |
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lora_dropout: 0.0 |
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lora_alpha: 16 |
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target_modules: ["query", "key", "value", "intermediate.dense", "output.dense"] |
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modules_to_save: ["classifier"] |
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### Training config |
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class: AdamW |
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betas: (0.9, 0.98) |
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weight_decay: 0.01 |
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learning rate: 1e-4 |
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epoch: 10 |
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batch size: 100 |
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precision: 16-mixed |
