Feature Extraction
Transformers
English
esmc_sae
biology
esm
protein
sparse-autoencoder
interpretability
protein-embeddings
protein-language-model
unsupervised-learning
Instructions to use biohub/ESMC-300M-sae-layer23-k32-codebook65536 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-300M-sae-layer23-k32-codebook65536 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-300M-sae-layer23-k32-codebook65536")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-300M-sae-layer23-k32-codebook65536", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6fdf940119fec4c7e97ac80da1b88ee9df077eda2e24d2100bf9b1ce167b8a7
- Size of remote file:
- 504 MB
- SHA256:
- bbb659942548da2e945849bee2b9580545557688e9e2b29df525bad5f22db1a4
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