Instructions to use brain-bzh/reve-positions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brain-bzh/reve-positions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="brain-bzh/reve-positions", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("brain-bzh/reve-positions", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -15,7 +15,7 @@ Wrapper to provide electrode positions to use the [REVE EEG Foundation Model](ht
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- **Developed by:** the [BRAIN team](https://www.imt-atlantique.fr/en/research-innovation/teams/brain) and [UdeM](https://www.umontreal.ca/en/)
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- **Funded by :** AI@IMT,
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REVE (Representation for EEG with Versatile Embeddings) is a pretrained model explicitly designed to generalize across diverse EEG signals.
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- **Developed by:** the [BRAIN team](https://www.imt-atlantique.fr/en/research-innovation/teams/brain) and [UdeM](https://www.umontreal.ca/en/)
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- **Funded by :** AI@IMT, ANR JCJC ENDIVE, Jean Zay (with project numbers), Alliance Canada and Region Bretagne.
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REVE (Representation for EEG with Versatile Embeddings) is a pretrained model explicitly designed to generalize across diverse EEG signals.
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