Fill-Mask
Transformers
Safetensors
English
esmc
biology
esm
protein
protein-language-model
protein-embeddings
masked-language-modeling
transfer-learning
variant-effect-prediction
protein-engineering
Instructions to use biohub/ESMC-6B-step250k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-6B-step250k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="biohub/ESMC-6B-step250k")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("biohub/ESMC-6B-step250k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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The ESMC scaling-study checkpoints are being released to support reproducibility of the findings in our paper, please refer to [the paper](https://biohub.ai/papers/
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The ESMC scaling-study checkpoints are being released to support reproducibility of the findings in our paper, please refer to [the paper](https://biohub.ai/papers/esm_protein.pdf) and [github](https://github.com/Biohub/esm) for details. Please use the [ESMC](https://huggingface.co/biohub/ESMC-6B) 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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