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-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-300M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="biohub/ESMC-300M")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("biohub/ESMC-300M", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cae44ac38e82c84303984bb2f1a402ff427f006fd21158288cc500ba62926e19
- Size of remote file:
- 1.33 GB
- SHA256:
- 0772d8fe64bb25e14fe6f23b80e3c9a7d215d0da3c6cba5bd356d7c0e0bb22cc
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