Instructions to use MENG21/stud-fac-eval-NAIVEBAYES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MENG21/stud-fac-eval-NAIVEBAYES with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MENG21/stud-fac-eval-NAIVEBAYES")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MENG21/stud-fac-eval-NAIVEBAYES", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download label_encoder.pkl from MENG21/stud-fac-eval-NAIVEBAYES: direct link, hf CLI and curl.
- Browser
- Download file 255 Bytes
-
https://huggingface.co/MENG21/stud-fac-eval-NAIVEBAYES/resolve/main/label_encoder.pkl
- Command line
-
hf download hf://MENG21/stud-fac-eval-NAIVEBAYES/label_encoder.pkl
-
curl -L -o label_encoder.pkl https://huggingface.co/MENG21/stud-fac-eval-NAIVEBAYES/resolve/main/label_encoder.pkl
255 Bytes
- Xet hash:
- a1fa5ba056d8fdba5c7c6e4322b6dcd7f280ab81d6a4a5b6c478396192331cf5
- Size of remote file:
- 255 Bytes
- SHA256:
- eab335634b7bbf508130833d83a40a5f84b97bccf9fa537b44e1da1ace16f493
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