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-k64-codebook16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-6B-sae-k64-codebook16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-6B-sae-k64-codebook16384")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-6B-sae-k64-codebook16384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65805dad2cecec6c2aecfda3f92c70e7a43b4daa5611ae3dbffd196fdc9310ab
- Size of remote file:
- 336 MB
- SHA256:
- f59a016d7e0143d780017651f4a2f334171dcaee99566fbd4a220967ebac8f25
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