Fill-Mask
Transformers
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esmc
biology
esm
protein
protein-language-model
protein-embeddings
masked-language-modeling
transfer-learning
variant-effect-prediction
protein-engineering
Instructions to use biohub/ESMC-600M-step1000k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-600M-step1000k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="biohub/ESMC-600M-step1000k")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("biohub/ESMC-600M-step1000k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
The ESMC scaling-study checkpoints are being released to support reproducibility of the findings in our paper, please refer to the paper and github for details. Please use the ESMC model for research work. ESMC is a state-of-the-art protein language model trained on billions of protein sequences that learns the rules of protein biology and provides representations for therapeutic protein engineering and basic biological insight.
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