Instructions to use sihuapeng/PPPSL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sihuapeng/PPPSL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sihuapeng/PPPSL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sihuapeng/PPPSL") model = AutoModelForSequenceClassification.from_pretrained("sihuapeng/PPPSL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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pipeline_tag: text-classification
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# Model descriptions
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**PPPSL-ESM2**(PPPSL, Prediction of prokaryotic protein 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 prokaryotic protein subcellular localization dataset. It achieves the following results:
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Train Loss: 0.0015
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Train Accuracy: 0.9893
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Validation Loss: 0.0184
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pipeline_tag: text-classification
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# Model descriptions
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**PPPSL-ESM2**(PPPSL, Prediction of prokaryotic protein 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 prokaryotic protein subcellular localization dataset. It achieves the following results:
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Train Loss: 0.0015
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Train Accuracy: 0.9893
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Validation Loss: 0.0184
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