Instructions to use recursionpharma/OpenPhenom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use recursionpharma/OpenPhenom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="recursionpharma/OpenPhenom", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("recursionpharma/OpenPhenom", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Kian Kenyon-Dean commited on
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# Masked Autoencoders are Scalable Learners of Cellular Morphology
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Official repo for Recursion's accepted spotlight paper at NeurIPS 2023 Generative AI & Biology workshop.
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Paper: https://arxiv.org/abs/2309.16064
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# Masked Autoencoders are Scalable Learners of Cellular Morphology
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Official repo for Recursion's accepted spotlight paper at [NeurIPS 2023 Generative AI & Biology workshop](https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/GenBio).
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Paper: https://arxiv.org/abs/2309.16064
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