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:
- 3416c8278648aa85b2f4590ea49a6374de532e644254659010d25084c2b7e15d
- Size of remote file:
- 4.86 GB
- SHA256:
- 9f3c29afea073517b2cbc3ad21730f78f6dbd83ba154ea5c29146c6f6ffaf27c
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