Instructions to use Phase-Technologies/netuark-classifier-ensemble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Phase-Technologies/netuark-classifier-ensemble with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Phase-Technologies/netuark-classifier-ensemble", "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
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README.md
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datasets:
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- Xerv-AI/netuark-posts-6000
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---
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# NetuArk Posts Classifier (Ensemble Architecture)
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## Model Details
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- **Architecture:** Voting Classifier (Multinomial Naive Bayes + Logistic Regression)
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- **Vectorization:** TF-IDF (N-grams 1-3)
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- **Accuracy:**
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- **Classes:** HackerNews, TechCrunch, TheVerge, FT, GuardianTech, Slashdot, ArsTechnica.
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## Training Data
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datasets:
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- Xerv-AI/netuark-posts-6000
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metrics:
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- accuracy : 94.81%
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# NetuArk Posts Classifier (Ensemble Architecture)
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## Model Details
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- **Architecture:** Voting Classifier (Multinomial Naive Bayes + Logistic Regression)
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- **Vectorization:** TF-IDF (N-grams 1-3)
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- **Accuracy:** 94.81% on the NetuArk-6000 dataset.
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- **Classes:** HackerNews, TechCrunch, TheVerge, FT, GuardianTech, Slashdot, ArsTechnica.
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## Training Data
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