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-codebook131072 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-codebook131072 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-codebook131072")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-600M-sae-layer27-k64-codebook131072", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4478aa0d24431dbba9084feb7bcad8ea676443c11e78a1e32616fe81f3363245
- Size of remote file:
- 1.21 GB
- SHA256:
- 9956478b4bdd953a3aa70cccf1b312c337f2bb3f33b477a74a7cd1ee65f32ad6
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