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  # Model descriptions
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  In biology, "targeting peptides" typically refer to "targeting signal peptides" or "targeting sequences," also known as "signal peptides" or "signal sequences." These are short amino acid sequences located at the N-terminal or C-terminal of a protein that direct the protein to specific locations within the cell, such as the mitochondria, chloroplasts, plastids, endoplasmic reticulum, and more. Targeting peptides play a crucial signaling role during protein synthesis, ensuring that the protein is correctly localized to its intended cellular destination.
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- **TarPepSubLoc-ESM2** (TarPepSubLoc, Targeting Peptide Subcellular Localization) is a protein language model fine-tuned from [**ESM2**](https://github.com/facebookresearch/esm) pretrained model [facebook/esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) on a trageting peptides subcelluar localization dataset with five classes. It achieves the following results:
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  Train Loss: 0.0012
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  Train Accuracy: 0.9972
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  Validation Loss: 0.0319
 
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  # Model descriptions
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  In biology, "targeting peptides" typically refer to "targeting signal peptides" or "targeting sequences," also known as "signal peptides" or "signal sequences." These are short amino acid sequences located at the N-terminal or C-terminal of a protein that direct the protein to specific locations within the cell, such as the mitochondria, chloroplasts, plastids, endoplasmic reticulum, and more. Targeting peptides play a crucial signaling role during protein synthesis, ensuring that the protein is correctly localized to its intended cellular destination.
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+ **TarPepSubLoc-ESM2** (TarPepSubLoc, Targeting Peptide Subcellular Localization) is a protein language model fine-tuned from [**ESM2**](https://github.com/facebookresearch/esm) pretrained model [***facebook/esm2_t36_3B_UR50D***](https://huggingface.co/facebook/esm2_t36_3B_UR50D) on a trageting peptides subcelluar localization dataset with five classes. It achieves the following results:
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  Train Loss: 0.0012
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  Train Accuracy: 0.9972
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  Validation Loss: 0.0319