Instructions to use thuggingface/multi-nb-tfidf-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thuggingface/multi-nb-tfidf-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("thuggingface/multi-nb-tfidf-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from thuggingface/multi-nb-tfidf-model: direct link, hf CLI and curl.
- Browser
- Download file 232 Bytes
-
https://huggingface.co/thuggingface/multi-nb-tfidf-model/resolve/main/config.json
- Command line
-
hf download hf://thuggingface/multi-nb-tfidf-model/config.json
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curl -L -o config.json https://huggingface.co/thuggingface/multi-nb-tfidf-model/resolve/main/config.json
232 Bytes
| { | |
| "model_type": "sklearn", | |
| "model_name": "Multinomial Naive Bayes with TF-IDF", | |
| "framework": "scikit-learn", | |
| "description": "A Multinomial Naive Bayes model trained with TF-IDF vectorizer on a phishing detection dataset." | |
| } | |