Code Comment Quality Classifier ๐Ÿ”

A machine learning model that automatically classifies code comments into quality categories to help improve code documentation and review processes.

๐ŸŽฏ 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  • Outdated: Comments that may no longer reflect the current code

๐Ÿš€ 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labels = ["excellent", "helpful", "unclear", "outdated"]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]}")

๐Ÿ‹๏ธ 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

๐Ÿ“Š 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  • Classes: 4 (excellent, helpful, unclear, outdated)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

๐ŸŽ“ 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

๐Ÿ“ Project Structure

.
โ”œโ”€โ”€ README.md
โ”œโ”€โ”€ LICENSE
โ”œโ”€โ”€ requirements.txt
โ”œโ”€โ”€ config.yaml
โ”œโ”€โ”€ train.py                    # Main training script
โ”œโ”€โ”€ inference.py                # Inference script
โ”œโ”€โ”€ src/
โ”‚   โ”œโ”€โ”€ __init__.py
โ”‚   โ”œโ”€โ”€ data_loader.py         # Data loading utilities
โ”‚   โ”œโ”€โ”€ model.py               # Model definition
โ”‚   โ””โ”€โ”€ utils.py               # Helper functions
โ”œโ”€โ”€ scripts/
โ”‚   โ”œโ”€โ”€ generate_data.py       # Generate synthetic training data
โ”‚   โ”œโ”€โ”€ evaluate.py            # Evaluation script
โ”‚   โ””โ”€โ”€ upload_to_hub.py       # Upload model to Hugging Face Hub
โ”œโ”€โ”€ data/
โ”‚   โ””โ”€โ”€ .gitkeep
โ””โ”€โ”€ MODEL_CARD.md              # Hugging Face model card

๐Ÿค 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

๐Ÿ“ License

This project is licensed under the MIT License - see the LICENSE file for details.

๐Ÿ™ Acknowledgments

๐Ÿ“ฎ Contact

For questions or feedback, please open a discussion on the model's Hugging Face page or reach out via Hugging Face.


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

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