Feature Extraction
Transformers
Safetensors
PyTorch
reve
eeg
neuroscience
foundation-model
custom_code
Eval Results (legacy)
Instructions to use brain-bzh/reve-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brain-bzh/reve-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="brain-bzh/reve-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("brain-bzh/reve-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card metric labels: Accuracy or balanced accuracy?
#2
by Alexcchristou - opened
Could the evaluation metadata use “Balanced accuracy” for the entries taken from Table 2 of the paper?
For example, the README metadata lists TUAB = 0.8315 and BCICIV2a = 0.6396 with both metric type and name set to “Accuracy”. These match the REVE-Base values in Table 2, whose caption specifies balanced accuracy.
Current metadata · Paper, Table 2
Making the metric explicit would help readers compare results on datasets with uneven class counts. I have checked the card and table, not rerun the evaluations; this is a labelling question, not a challenge to the reported scores.
Hello,
It was changed, thanks for the suggestion.
jonathan-lys changed discussion status to closed