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--- |
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language: en |
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license: mit |
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tags: |
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- text-classification |
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- code-quality |
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- documentation |
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- code-comments |
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- developer-tools |
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- distilbert |
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datasets: |
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- synthetic |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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pipeline_tag: text-classification |
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widget: |
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- text: 'This function calculates the Fibonacci sequence using dynamic programming |
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to avoid redundant calculations. Time complexity: O(n), Space complexity: O(n)' |
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example_title: Excellent Comment |
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- text: Calculates the sum of two numbers and returns the result |
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example_title: Helpful Comment |
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- text: does stuff with numbers |
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example_title: Unclear Comment |
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- text: 'DEPRECATED: Use calculate_new() instead. This method will be removed in v2.0' |
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example_title: Outdated Comment |
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--- |
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# Code Comment Quality Classifier 🔍 |
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A machine learning model that automatically classifies code comments into quality categories to help improve code documentation and review processes. |
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## 🎯 What Does This Model Do? |
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This model analyzes code comments and classifies them into four categories: |
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- **Excellent**: Clear, comprehensive, and highly informative comments |
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- **Helpful**: Good comments that add value but could be improved |
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- **Unclear**: Vague or confusing comments that don't add much value |
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- **Outdated**: Comments that may no longer reflect the current code |
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## 🚀 Quick Start |
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### Installation |
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```bash |
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pip install -r requirements.txt |
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``` |
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### Using the Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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# Load the model and tokenizer |
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model_name = "Snaseem2026/code-comment-classifier" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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# Classify a comment |
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comment = "This function calculates the fibonacci sequence using dynamic programming" |
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inputs = tokenizer(comment, return_tensors="pt", truncation=True, max_length=512) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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predicted_class = torch.argmax(predictions, dim=-1).item() |
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labels = ["excellent", "helpful", "unclear", "outdated"] |
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print(f"Comment quality: {labels[predicted_class]}") |
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``` |
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## 🏋️ Training the Model |
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To train the model on your own data: |
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```bash |
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python train.py --config config.yaml |
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``` |
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To generate synthetic training data: |
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```bash |
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python scripts/generate_data.py |
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``` |
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## 📊 Model Details |
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- **Base Model**: DistilBERT (distilbert-base-uncased) |
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- **Task**: Multi-class text classification |
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- **Classes**: 4 (excellent, helpful, unclear, outdated) |
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- **Training Data**: Synthetic code comments with quality labels |
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- **License**: MIT |
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## 🎓 Use Cases |
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- **Code Review Automation**: Automatically flag low-quality comments during PR reviews |
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- **Documentation Quality Checks**: Audit codebases for documentation quality |
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- **Developer Education**: Help developers learn what makes good code comments |
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- **IDE Integration**: Real-time feedback on comment quality while coding |
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## 📁 Project Structure |
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``` |
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. |
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├── README.md |
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├── LICENSE |
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├── requirements.txt |
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├── config.yaml |
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├── train.py # Main training script |
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├── inference.py # Inference script |
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├── src/ |
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│ ├── __init__.py |
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│ ├── data_loader.py # Data loading utilities |
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│ ├── model.py # Model definition |
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│ └── utils.py # Helper functions |
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├── scripts/ |
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│ ├── generate_data.py # Generate synthetic training data |
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│ ├── evaluate.py # Evaluation script |
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│ └── upload_to_hub.py # Upload model to Hugging Face Hub |
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├── data/ |
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│ └── .gitkeep |
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└── MODEL_CARD.md # Hugging Face model card |
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``` |
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## 🤝 Contributing |
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This is an open-source project! Contributions are welcome. Please feel free to: |
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- Report bugs or issues |
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- Suggest new features |
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- Submit pull requests |
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- Improve documentation |
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## 📝 License |
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. |
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## 🙏 Acknowledgments |
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- Built with [Hugging Face Transformers](https://huggingface.co/transformers/) |
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- Base model: [DistilBERT](https://huggingface.co/distilbert-base-uncased) |
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## 📮 Contact |
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For questions or feedback, please open a discussion on the model's [Hugging Face page](https://huggingface.co/Snaseem2026/code-comment-classifier/discussions) or reach out via Hugging Face. |
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--- |
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**Note**: This model is designed for educational and productivity purposes. Always review automated suggestions with human judgment. |
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