Tabular Classification
Scikit-learn
Joblib
English
timeseries
detection
anomaly-detection
energy
battery-storage
vrfb
gaussian-mixture-model
one-class-svm
Instructions to use EnerTEF/Anomaly-Detection-Service with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use EnerTEF/Anomaly-Detection-Service with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("EnerTEF/Anomaly-Detection-Service", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Download test_observations.csv from EnerTEF/Anomaly-Detection-Service: direct link, hf CLI and curl.
- Browser
- Download file 10.5 MB
-
https://huggingface.co/EnerTEF/Anomaly-Detection-Service/resolve/main/test_observations.csv
- Command line
-
hf download hf://EnerTEF/Anomaly-Detection-Service/test_observations.csv
-
curl -L -o test_observations.csv https://huggingface.co/EnerTEF/Anomaly-Detection-Service/resolve/main/test_observations.csv
10.5 MB
- Xet hash:
- 78686cb432756af8cc7461467df57f41d0ca8306d5fc1596a48d6dae25eb37fe
- Size of remote file:
- 10.5 MB
- SHA256:
- e0e1d26b56eb5d05250ba75877132b0d6b5e2600ec57b8955325396fa72ed181
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