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:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("thuggingface/multi-nb-tfidf-model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Multinomial Naive Bayes with TF-IDF
This is a Multinomial Naive Bayes model trained with a TF-IDF vectorizer on a phishing detection dataset. It was trained using scikit-learn with the support of the National Innovation Centre for Data (NICD) at Newcastle University.
Usage
You can load and use this model with the following script:
import joblib
from huggingface_hub import hf_hub_download
# Download the model from Hugging Face
model_path = hf_hub_download(repo_id="your_username/multi-nb-tfidf-model", filename="multi-nb-tfidf-model.pkl")
# Load the model
model = joblib.load(model_path)
# Use the model for predictions
sample_text = ["Example text to classify"]
predictions = model.predict(sample_text)
print(predictions)
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