Feature Extraction
Transformers
English
esmc_sae
biology
esm
protein
sparse-autoencoder
interpretability
protein-embeddings
protein-language-model
unsupervised-learning
Instructions to use biohub/ESMC-6B-sae-layer60-k64-codebook8192 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-6B-sae-layer60-k64-codebook8192 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-6B-sae-layer60-k64-codebook8192")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-6B-sae-layer60-k64-codebook8192", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 788b212a55602f989b3e974843baddde71da5ff87f37222659217f6471b91be1
- Size of remote file:
- 168 MB
- SHA256:
- 00df714c61f92d1654ba1f4405e8aa2d7495384ba25620be109526ea881f998a
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