Instructions to use ChatterjeeLab/FusOn-pLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChatterjeeLab/FusOn-pLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ChatterjeeLab/FusOn-pLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ChatterjeeLab/FusOn-pLM") model = AutoModelForMaskedLM.from_pretrained("ChatterjeeLab/FusOn-pLM") - Notebooks
- Google Colab
- Kaggle
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README.md
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print("Per-residue embeddings shape:", embeddings.shape)
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print("Per-residue embeddings shape:", embeddings.shape)
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```
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## Questions?
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Send an email to us at [pranam.chatterjee@duke.edu, sophia.vincoff@duke.edu](mailto:pranam.chatterjee@duke.edu,sophia.vincoff@duke.edu)!
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