Snaseem2026/code-comment-classifier

1---2language: en3license: mit4tags:5- text-classification6- code-quality7- documentation8- code-comments9- developer-tools10- distilbert11datasets:12- synthetic13metrics:14- accuracy15- f116- precision17- recall18description: >19  This model classifies code snippets based on the quality and presence of comments.20  It helps automate code review and can be integrated into developer tooling for documentation assessment.21pipeline_tag: text-classification22widget:23- text: 'This function calculates the Fibonacci sequence using dynamic programming24    to avoid redundant calculations. Time complexity: O(n), Space complexity: O(n)'25  example_title: Excellent Comment26- text: Calculates the sum of two numbers and returns the result27  example_title: Helpful Comment28- text: does stuff with numbers29  example_title: Unclear Comment30- text: 'DEPRECATED: Use calculate_new() instead. This method will be removed in v2.0'31  example_title: Outdated Comment32---33 34# Code Comment Quality Classifier 🔍35 36A machine learning model that automatically classifies code comments into quality categories to help improve code documentation and review processes.37 38## 🎯 What Does This Model Do?39 40This model analyzes code comments and classifies them into four categories:41- **Excellent**: Clear, comprehensive, and highly informative comments42- **Helpful**: Good comments that add value but could be improved43- **Unclear**: Vague or confusing comments that don't add much value44- **Outdated**: Comments that may no longer reflect the current code45 46## 🚀 Quick Start47 48### Installation49 50```bash51pip install -r requirements.txt52```53 54### Using the Model55 56```python57from transformers import AutoTokenizer, AutoModelForSequenceClassification58import torch59 60# Load the model and tokenizer61model_name = "Snaseem2026/code-comment-classifier"62tokenizer = AutoTokenizer.from_pretrained(model_name)63model = AutoModelForSequenceClassification.from_pretrained(model_name)64 65# Classify a comment66comment = "This function calculates the fibonacci sequence using dynamic programming"67inputs = tokenizer(comment, return_tensors="pt", truncation=True, max_length=512)68 69with torch.no_grad():70    outputs = model(**inputs)71    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)72    predicted_class = torch.argmax(predictions, dim=-1).item()73 74labels = ["excellent", "helpful", "unclear", "outdated"]75print(f"Comment quality: {labels[predicted_class]}")76```77 78## 🏋️ Training the Model79 80To train the model on your own data:81 82```bash83python train.py --config config.yaml84```85 86To generate synthetic training data:87 88```bash89python scripts/generate_data.py90```91 92## 📊 Model Details93 94- **Base Model**: DistilBERT (distilbert-base-uncased)95- **Task**: Multi-class text classification96- **Classes**: 4 (excellent, helpful, unclear, outdated)97- **Training Data**: Synthetic code comments with quality labels98- **License**: MIT99 100## 🎓 Use Cases101 102- **Code Review Automation**: Automatically flag low-quality comments during PR reviews103- **Documentation Quality Checks**: Audit codebases for documentation quality104- **Developer Education**: Help developers learn what makes good code comments105- **IDE Integration**: Real-time feedback on comment quality while coding106 107## 📁 Project Structure108 109```110.111├── README.md112├── LICENSE113├── requirements.txt114├── config.yaml115├── train.py                    # Main training script116├── inference.py                # Inference script117├── src/118│   ├── __init__.py119│   ├── data_loader.py         # Data loading utilities120│   ├── model.py               # Model definition121│   └── utils.py               # Helper functions122├── scripts/123│   ├── generate_data.py       # Generate synthetic training data124│   ├── evaluate.py            # Evaluation script125│   └── upload_to_hub.py       # Upload model to Hugging Face Hub126├── data/127│   └── .gitkeep128└── MODEL_CARD.md              # Hugging Face model card129```130 131## 🤝 Contributing132 133This is an open-source project! Contributions are welcome. Please feel free to:134- Report bugs or issues135- Suggest new features136- Submit pull requests137- Improve documentation138 139## 📝 License140 141This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.142 143## 🙏 Acknowledgments144 145- Built with [Hugging Face Transformers](https://huggingface.co/transformers/)146- Base model: [DistilBERT](https://huggingface.co/distilbert-base-uncased)147 148## 📮 Contact149 150For 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.151 152---153 154**Note**: This model is designed for educational and productivity purposes. Always review automated suggestions with human judgment.155