| --- |
| language: en |
| license: mit |
| tags: |
| - text-classification |
| - code-quality |
| - documentation |
| - code-comments |
| - developer-tools |
| - distilbert |
| datasets: |
| - synthetic |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| description: > |
| This model classifies code snippets based on the quality and presence of comments. |
| It helps automate code review and can be integrated into developer tooling for documentation assessment. |
| pipeline_tag: text-classification |
| widget: |
| - text: 'This function calculates the Fibonacci sequence using dynamic programming |
| to avoid redundant calculations. Time complexity: O(n), Space complexity: O(n)' |
| example_title: Excellent Comment |
| - text: Calculates the sum of two numbers and returns the result |
| example_title: Helpful Comment |
| - text: does stuff with numbers |
| example_title: Unclear Comment |
| - text: 'DEPRECATED: Use calculate_new() instead. This method will be removed in v2.0' |
| example_title: Outdated Comment |
| --- |
| |
| # 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| - **Outdated**: Comments that may no longer reflect the current code |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 |
|
|
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| ### Using the Model |
|
|
| ```python |
| 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: |
|
|
| ```bash |
| python train.py --config config.yaml |
| ``` |
|
|
| To generate synthetic training data: |
|
|
| ```bash |
| 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](LICENSE) file for details. |
|
|
| ## 🙏 Acknowledgments |
|
|
| - Built with [Hugging Face Transformers](https://huggingface.co/transformers/) |
| - Base model: [DistilBERT](https://huggingface.co/distilbert-base-uncased) |
|
|
| ## 📮 Contact |
|
|
| 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. |
|
|
| --- |
|
|
| **Note**: This model is designed for educational and productivity purposes. Always review automated suggestions with human judgment. |
|
|
