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 vectorizer.pkl from MENG21/stud-fac-eval-NAIVEBAYES: direct link, hf CLI and curl.
- Browser
- Download file 108 kB
-
https://huggingface.co/MENG21/stud-fac-eval-NAIVEBAYES/resolve/main/vectorizer.pkl
- Command line
-
hf download hf://MENG21/stud-fac-eval-NAIVEBAYES/vectorizer.pkl
-
curl -L -o vectorizer.pkl https://huggingface.co/MENG21/stud-fac-eval-NAIVEBAYES/resolve/main/vectorizer.pkl
108 kB
- Xet hash:
- 0209ce72d3b0b94543f7def8373c4c31b314b5abcf3a97fc602e4cf56d6db215
- Size of remote file:
- 108 kB
- SHA256:
- 69b7712bf6ce9d7d2017785a0a37fce7f2ccd05c6ff8459d2e267670a558320b
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