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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README.md
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- biology
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pipeline_tag: text-classification
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## Example
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```
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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- biology
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pipeline_tag: text-classification
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---
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## Model descriptions
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This model is a fine-tuned version of [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 on the evaluation set:
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Train Loss: 0.0015
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Train Accuracy: 0.9893
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Validation Loss: 0.0155
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Validation Accuracy: 0.9702
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Epoch: 20
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## Example
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```
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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