Instructions to use SaProtHub/Model-DMS_DLG4_RAT-35M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SaProtHub/Model-DMS_DLG4_RAT-35M with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("westlake-repl/SaProt_35M_AF2") model = PeftModel.from_pretrained(base_model, "SaProtHub/Model-DMS_DLG4_RAT-35M") - Notebooks
- Google Colab
- Kaggle
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README.md
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The dataset is from [Deep generative models of genetic variation capture the effects of mutations](https://www.nature.com/articles/s41592-018-0138-4).
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And can also be found on [SaprotHub dataset](https://huggingface.co/datasets/SaProtHub/DMS_DLG4_RAT).
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Label means fitness score of each mutant amino acid sequence.
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### Model input type
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Amino acid sequence
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The dataset is from [Deep generative models of genetic variation capture the effects of mutations](https://www.nature.com/articles/s41592-018-0138-4).
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And can also be found on [SaprotHub dataset](https://huggingface.co/datasets/SaProtHub/DMS_DLG4_RAT).
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Label means fitness score of each mutant amino acid sequence.
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Ranging from negative infinity to positive infinity. If the effect larger than 0 represents high fitness, smaller than 0 represents low fitness.
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### Model input type
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Amino acid sequence
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