Instructions to use GonzagueD/whale-tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use GonzagueD/whale-tuning with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("GonzagueD/whale-tuning", "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 vectorizer.pkl from GonzagueD/whale-tuning: direct link, hf CLI and curl.
- Browser
- Download file 18.1 kB
-
https://huggingface.co/GonzagueD/whale-tuning/resolve/main/vectorizer.pkl
- Command line
-
hf download hf://GonzagueD/whale-tuning/vectorizer.pkl
-
curl -L -o vectorizer.pkl https://huggingface.co/GonzagueD/whale-tuning/resolve/main/vectorizer.pkl
18.1 kB
- Xet hash:
- b649b7e04b92f6dd81b222b8eaa97cb45ae3694892e491e538050651c885da1e
- Size of remote file:
- 18.1 kB
- SHA256:
- 5abaaaf4068d040a4521ad42cb6ffaec2572c0af131b5f852da63935d3beb3e2
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