Code Comment Quality Classifier 🔍
A machine learning model that automatically classifies code comments into quality categories to help improve code documentation and review processes.
🎯 What Does This Model Do?
This model analyzes code comments and classifies them into four categories:
- Excellent: Clear, comprehensive, and highly informative comments
- Helpful: Good comments that add value but could be improved
- Unclear: Vague or confusing comments that don't add much value
- Outdated: Comments that may no longer reflect the current code
🚀 Quick Start
Installation
pip install -r requirements.txt
Using the Model
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load the model and tokenizer
model_name = "Snaseem2026/code-comment-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Classify a comment
comment = "This function calculates the fibonacci sequence using dynamic programming"
inputs = tokenizer(comment, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
predicted_class = torch.argmax(predictions, dim=-1).item()
labels = ["excellent", "helpful", "unclear", "outdated"]
print(f"Comment quality: {labels[predicted_class]}")
🏋️ Training the Model
To train the model on your own data:
python train.py --config config.yaml
To generate synthetic training data:
python scripts/generate_data.py
📊 Model Details
- Base Model: DistilBERT (distilbert-base-uncased)
- Task: Multi-class text classification
- Classes: 4 (excellent, helpful, unclear, outdated)
- Training Data: Synthetic code comments with quality labels
- License: MIT
🎓 Use Cases
- Code Review Automation: Automatically flag low-quality comments during PR reviews
- Documentation Quality Checks: Audit codebases for documentation quality
- Developer Education: Help developers learn what makes good code comments
- IDE Integration: Real-time feedback on comment quality while coding
📁 Project Structure
.
├── README.md
├── LICENSE
├── requirements.txt
├── config.yaml
├── train.py # Main training script
├── inference.py # Inference script
├── src/
│ ├── __init__.py
│ ├── data_loader.py # Data loading utilities
│ ├── model.py # Model definition
│ └── utils.py # Helper functions
├── scripts/
│ ├── generate_data.py # Generate synthetic training data
│ ├── evaluate.py # Evaluation script
│ └── upload_to_hub.py # Upload model to Hugging Face Hub
├── data/
│ └── .gitkeep
└── MODEL_CARD.md # Hugging Face model card
🤝 Contributing
This is an open-source project! Contributions are welcome. Please feel free to:
- Report bugs or issues
- Suggest new features
- Submit pull requests
- Improve documentation
📝 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
- Built with Hugging Face Transformers
- Base model: DistilBERT
📮 Contact
For questions or feedback, please open a discussion on the model's Hugging Face page or reach out via Hugging Face.
Note: This model is designed for educational and productivity purposes. Always review automated suggestions with human judgment.
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