README.md

---
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  example_title: Outdated Comment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- **Outdated**: Comments that may no longer reflect the current code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labels = ["excellent", "helpful", "unclear", "outdated"]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.