Feature Extraction
Transformers
English
biology
esm
protein
sparse-autoencoder
interpretability
protein-embeddings
protein-language-model
unsupervised-learning
Instructions to use biohub/ESMC-SAE-Overview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-SAE-Overview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-SAE-Overview")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-SAE-Overview", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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# ESMC Sparse Autoencoder (SAE) Explanation
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license:
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- mit
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- other
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license_link: https://github.com/Biohub/esm/blob/main/THIRD_PARTY_NOTICE.md
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language:
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- en
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tags:
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- biology
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- esm
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- protein
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- sparse-autoencoder
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- interpretability
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- protein-embeddings
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- feature-extraction
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- protein-language-model
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- unsupervised-learning
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- transformers
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---
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# ESMC Sparse Autoencoder (SAE) Explanation
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