[P] Automated Code Comment Quality Assessment with 94.85% Accuracy – Open Source

Built a text classifier that automatically rates code comment quality to help with documentation reviews. **Quick Stats:** - 🎯 94.85% accuracy on test set - 🤖 Fine-tuned DistilBERT (66.96M params) - 🆓 MIT License (free to use) - ⚡ Easy integration with Transformers **Categories:** 1. Excellent (100% precision) - Comprehensive, clear documentation 2. Helpful (89% precision) - Good but could be better 3. Unclear (100% precision) - Vague or confusing 4. Outdated (92% precision) - Deprecated/TODO comments **Try it:** ```python pip install transformers torch from transformers import pipeline classifier = pipeline("text-classification", model="Snaseem2026/code-comment-classifier") # Test examples comments = [ "This function implements binary search with O(log n) complexity", "does stuff", "TODO: fix later" ] for comment in comments: result = classifier(comment) print(f"{result['label']}: {comment}") 

Model: https://huggingface.co/Snaseem2026/code-comment-classifier

Potential applications:

• CI/CD integration for documentation quality gates
• Real-time IDE feedback
• Codebase health metrics
• Developer training tools

Feedback and suggestions welcome!