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-k64-codebook32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-300M-sae-layer23-k64-codebook32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-300M-sae-layer23-k64-codebook32768")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-300M-sae-layer23-k64-codebook32768", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d829bf8e1fdee25f67c87fb8e7c1fbbcc9384f74acca1349bcee15da753c3ed2
- Size of remote file:
- 252 MB
- SHA256:
- efb86a8d2cdd34a15a43287164cb6dc6e9ebad22c4c27ca64a31acbd764a57ca
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