Instructions to use Juanm2/EmotionClassModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Juanm2/EmotionClassModel with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Juanm2/EmotionClassModel", "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 logisticRegModel.pkl from Juanm2/EmotionClassModel: direct link, hf CLI and curl.
- Browser
- Download file 462 kB
-
https://huggingface.co/Juanm2/EmotionClassModel/resolve/main/logisticRegModel.pkl
- Command line
-
hf download hf://Juanm2/EmotionClassModel/logisticRegModel.pkl
-
curl -L -o logisticRegModel.pkl https://huggingface.co/Juanm2/EmotionClassModel/resolve/main/logisticRegModel.pkl
462 kB
- Xet hash:
- 38eb96c06543fd322b73beb73e9f0a38275049c75d35e13f1d9f328102763546
- Size of remote file:
- 462 kB
- SHA256:
- 9b78e629d35e5e75ff7233f12200fed51aed42f8043ddb300c2e8b822f8b075a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.