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:
- cb50d5802af9847e91fae472ae6df3a3d15c35b5eb8702ef30275f75c1e39bde
- Size of remote file:
- 561 Bytes
- SHA256:
- a3aa7e324c30cf231a4d1024b079027fd0b757d04bce5b50aa49d02d55d4841d
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