Feature Extraction
Transformers
English
esmc_sae
biology
esm
protein
sparse-autoencoder
interpretability
protein-embeddings
protein-language-model
unsupervised-learning
Instructions to use biohub/ESMC-600M-sae-layer27-k128-codebook32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-600M-sae-layer27-k128-codebook32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-600M-sae-layer27-k128-codebook32768")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-600M-sae-layer27-k128-codebook32768", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a659d42d4e990b583774bd3531a8534ea105410427a68ae48839801e6e86138
- Size of remote file:
- 302 MB
- SHA256:
- 2a21c01b501d76f7c88cac477f5d832a916ce8f31286a5523449466fb675b3aa
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