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Model-Signal_Peptides_Prediction-650M
SaProtHub/Model-Signal_Peptides_Prediction-650M
Model-Signal_Peptides_Prediction-650M is a machine learning model from SaProtHub. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
This model is used to predict signal peptides on each site of amino acid sequences.
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At a glance
- Library
- peft
- Access
- Public
- Created
- Aug 1, 2024
- Updated
- Nov 18, 2024
- SHA
- b1cba190
Base models
Model card
From the Hugging Face model README
Base model: westlake-repl/SaProt_650M_AF2
Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->This model is used to predict signal peptides on each site of amino acid sequences.
Task type
Residue level clssification
Dataset description
The dataset is from SignalP 6.0 predicts all five types of signal peptides using protein language models. This dataset contains 7 classes:
S (0): Sec/SPI signal peptide | T (1): Tat/SPI or Tat/SPII signal peptide | L (2): Sec/SPII signal peptide | P (3): Sec/SPIII signal peptide | I (4): cytoplasm | M (5): transmembrane | O (6): extracellular
Model input type
Amino acid sequence
Performance
test_acc: 0.96
LoRA config
lora_dropout: 0.0
lora_alpha: 16
target_modules: ["query", "key", "value", "intermediate.dense", "output.dense"]
modules_to_save: ["classifier"]
Training config
class: AdamW
betas: (0.9, 0.98)
weight_decay: 0.01
learning rate: 1e-4
epoch: 10
batch size: 100
precision: 16-mixed
- Library
- peft
- Created
- Aug 1, 2024
- Updated
- Nov 18, 2024