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
Upload README.md with huggingface_hub
Browse files
README.md
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
library_name: sklearn
|
| 3 |
+
tags: [text-classification, lightgbm, tfidf, code]
|
| 4 |
+
---
|
| 5 |
+
# Code Task Classifier (TF-IDF + LightGBM)
|
| 6 |
+
|
| 7 |
+
Distilled from Qwen2.5-Coder pseudo-labels (26 programming task categories).
|
| 8 |
+
|
| 9 |
+
```python
|
| 10 |
+
import joblib
|
| 11 |
+
from huggingface_hub import hf_hub_download
|
| 12 |
+
|
| 13 |
+
path = hf_hub_download("elzahar05/code-task-classifier-lgbm", "student_lgbm_model.joblib")
|
| 14 |
+
art = joblib.load(path)
|
| 15 |
+
X = art["vectorizer"].transform(["Write a function to reverse a linked list"])
|
| 16 |
+
print(art["label_encoder"].inverse_transform(art["model"].predict(X)))
|
| 17 |
+
```
|