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
- Xet hash:
- 830ad8fecff2e41817766d302228fe8236c9837bad6b52ba42222c0326ed226f
- Size of remote file:
- 4.86 GB
- SHA256:
- 2a243e24b1efba662a6f3a95961ef3d24fcfa9ed06a3dbb46e8e72f885f64620
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