Instructions to use elzahar05/code-task-classifier-lgbm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use elzahar05/code-task-classifier-lgbm with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("elzahar05/code-task-classifier-lgbm", "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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Download README.md from elzahar05/code-task-classifier-lgbm: direct link, hf CLI and curl.
- Browser
- Download file 541 Bytes
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https://huggingface.co/elzahar05/code-task-classifier-lgbm/resolve/main/README.md
- Command line
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hf download hf://elzahar05/code-task-classifier-lgbm/README.md
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curl -L -o README.md https://huggingface.co/elzahar05/code-task-classifier-lgbm/resolve/main/README.md
541 Bytes
| library_name: sklearn | |
| tags: [text-classification, lightgbm, tfidf, code] | |
| # Code Task Classifier (TF-IDF + LightGBM) | |
| Distilled from Qwen2.5-Coder pseudo-labels (26 programming task categories). | |
| ```python | |
| import joblib | |
| from huggingface_hub import hf_hub_download | |
| path = hf_hub_download("elzahar05/code-task-classifier-lgbm", "student_lgbm_model.joblib") | |
| art = joblib.load(path) | |
| X = art["vectorizer"].transform(["Write a function to reverse a linked list"]) | |
| print(art["label_encoder"].inverse_transform(art["model"].predict(X))) | |
| ``` | |