Feature Extraction
Transformers
English
esmc_sae
biology
esm
protein
sparse-autoencoder
interpretability
protein-embeddings
protein-language-model
unsupervised-learning
Instructions to use biohub/ESMC-600M-sae-layer27-k64-codebook8192 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-600M-sae-layer27-k64-codebook8192 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-600M-sae-layer27-k64-codebook8192")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-600M-sae-layer27-k64-codebook8192", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cebad767dd5f8257559a02b14729df120223c19e0901a25a8b3a84f859120b2
- Size of remote file:
- 75.6 MB
- SHA256:
- 8b85267e8ee53d4d81ff597ece411889e0b564a6617bc4635cc4529ce9d7ab9b
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