README.md

metadata

language: en
license: mit
tags:
  - text-classification
  - code-quality
  - documentation
  - code-comments
  - developer-tools
  - distilbert
datasets:
  - synthetic
metrics:
  - accuracy
  - f1
  - precision
  - recall
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    example_title: Outdated Comment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• Outdated: Comments that may no longer reflect the current code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

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labels = ["excellent", "helpful", "unclear", "outdated"]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.

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Note: This model is designed for educational and productivity purposes. Always review automated suggestions with human judgment.