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-k128-codebook8192 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-300M-sae-layer23-k128-codebook8192 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-300M-sae-layer23-k128-codebook8192")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-300M-sae-layer23-k128-codebook8192", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bcf8a7ed076e9eecd93c0d03d6da5e2884225fbb61e225f912642f63ef8a40c
- Size of remote file:
- 63 MB
- SHA256:
- ebccd90b2ed942148056ab059878731a71eeccc65a1dd70380fd5feda27e49d0
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