Instructions to use scikit-learn/Fish-Weight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use scikit-learn/Fish-Weight with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("scikit-learn/Fish-Weight", "example.pkl") ) # 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 example.pkl from scikit-learn/Fish-Weight: direct link, hf CLI and curl.
- Browser
- Download file 118 kB
-
https://huggingface.co/scikit-learn/Fish-Weight/resolve/main/example.pkl
- Command line
-
hf download hf://scikit-learn/Fish-Weight/example.pkl
-
curl -L -o example.pkl https://huggingface.co/scikit-learn/Fish-Weight/resolve/main/example.pkl
118 kB
- Xet hash:
- 5646b762d015ed352c4dca5f2c02257e77b8602595656797eed931f937ef3ebc
- Size of remote file:
- 118 kB
- SHA256:
- 10a9f41e1edb4d43ae237933edd469c930e9a1c1635d7ed2dd2743a88c807db2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.