Instructions to use Freakdivi/Task_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Freakdivi/Task_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Freakdivi/Task_Classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Freakdivi/Task_Classifier", device_map="auto") - Scikit-learn
How to use Freakdivi/Task_Classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Freakdivi/Task_Classifier", "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
- Xet hash:
- b824d826cb8ac416dc8dd6215689a6a279bb41ad6ffa7aa8ff696550bc10a74f
- Size of remote file:
- 31.6 kB
- SHA256:
- 02355072620b8db29e0397b73264905b25c0b3d6d7aba8f8d68e5f2335f94791
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