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:
- 427ef61ac4ed278c30a58a7ad2c4d05b1f96e7f32b37d61d90beaaf2cee9b6ac
- Size of remote file:
- 336 MB
- SHA256:
- 2a4e8fae6abcb4f4468f4c0ef73e755fb42d8edf6986cc21cd1cdb687da5eed3
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