Instructions to use nateraw/custom-sklearn-pipe-objects with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use nateraw/custom-sklearn-pipe-objects with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("nateraw/custom-sklearn-pipe-objects", "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 sklearn_transformer.joblib from nateraw/custom-sklearn-pipe-objects: direct link, hf CLI and curl.
- Browser
- Download file 49 Bytes
-
https://huggingface.co/nateraw/custom-sklearn-pipe-objects/resolve/main/sklearn_transformer.joblib
- Command line
-
hf download hf://nateraw/custom-sklearn-pipe-objects/sklearn_transformer.joblib
-
curl -L -o sklearn_transformer.joblib https://huggingface.co/nateraw/custom-sklearn-pipe-objects/resolve/main/sklearn_transformer.joblib
49 Bytes
- Xet hash:
- a4935c1a9380a052e4b54a6548aa266944b490e29a4e37c7b41b88972914676f
- Size of remote file:
- 49 Bytes
- SHA256:
- 178dec1149bf2ee473bd1413e59b51a033ac90ae79df4e1d77e4e09e35245c14
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