Snaseem2026/code-comment-classifier

Code Comment Quality Classifier 🔍

Automatically classify code comments into quality categories to improve code documentation and review processes.

🎯 Model Description

This fine-tuned DistilBERT model analyzes code comments and classifies them into 4 quality categories:

Category	Precision	Recall	Description
🌟 Excellent	100%	100%	Clear, comprehensive, highly informative comments with context
✅ Helpful	88.9%	100%	Good comments that add value but could be more detailed
⚠️ Unclear	100%	79.2%	Vague, confusing, or uninformative comments
🚫 Outdated	92.3%	100%	Deprecated, obsolete, or TODO comments

📊 Overall Performance

• Accuracy: 94.85%
• F1 Score: 94.68%
• *🚀 Quick Start

Using Transformers Pipeline (Easiest)

from transformers import pipeline

# Load the classifier
classifier = pipeline("text-classification", model="Snaseem2026/code-comment-classifier")

# Classify comments
comments = [
    "This function uses dynamic programming for O(n) time complexity",
    "does stuff",
    "DEPRECATED: use new_function() instead"
]

results = classifier(comments)
for comment, result in zip(comments, results):
    print(f"{comment}: {result['label']} ({result['score']:.2%} confidence)")

Manual Usage with Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
mod💡 Use Cases

### 1. **Code Review Automation**
Automatically flag low-quality comments during pull request reviews:
```python
def check_pr_comments(file_comments):
    classifier = pipeline("text-classification", model="Snaseem2026/code-comment-classifier")
    results = classifier(file_comments)
    return [c for c, r in zip(file_comments, results) if r['label'] in ['unclear', 'outdated']]

2. Documentation Quality Audits

Scan codebases to identify documentation that needs improvement.

3. Developer Education

Help developers learn what constitutes good documentation practices.

4. IDE Integration

Provide real-time feedback on comment quality while coding.

5. Technical Debt Analysis

Identify outdated comments and TODOs that need addressing.

🏋️ Training Details

Model Architecture

• Base Model: distilbert-base-uncased
• Parameters: 66.96 million
• Model Type: Sequence Classification
• Framework: PyTorch + Hugging Face Transformers

Training Data

• Dataset Size: 970 samples (776 train, 97 validation, 97 test)
• Data Source: Synthetic code comments
• Classes: 4 (balanced distribution)
• Language: English

Training Hyperparameters

• Epochs: 3
• Batch Size: 16 (train), 32 (eval)
• Learning Rate: 2e-5
• Optimizer: AdamW
• Weight Decay: 0.01
• Warmup Steps: 500
• Max Sequence Length: 512 tokenselpful", "unclear", "outdated"] print(f"Quality: {labels[predicted_class]} (confidence: {confidence:.2%})")

### Batch Processing

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="Snaseem2026/code-comment-classifier")

comments = [
    "Implements binary search with O(log n) time complexity",
    "TODO fix later",
    "Handles user authentication",
   📈 Evaluation Results

### Test Set Performance (97 samples)

          precision    recall  f1-score   support

excellent 1.0000 1.0000 1.0000 25 helpful 0.8889 1.0000 0.9412 24 unclear 1.0000 0.7917 0.8837 24 outdated 0.9231 1.0000 0.9600 24

accuracy                         0.9485        97

macro avg 0.9530 0.9479 0.9462 97 weighted avg 0.9535 0.9485 0.9468 97

### Key Findings
- ✨ **Perfect classification** of excellent comments (100% precision & recall)
- 🎯 **Zero false negatives** for helpful and outdated comments
- ⚠️ Slight challenge distinguishing unclear comments from other categories
- 📊 Strong overall performance with 94.85% accuracy

## ⚠️ Limitations

1. **Synthetic Training Data**: Model trained on synthetic examples; may require fine-tuning for specific domains (e.g., scientific computing, embedded systems)
2. **English Only**: Currently supports English code comments only
3. **No Code Context**: Evaluates comments in isolation without analyzing the actual code
4. **Subjectivity**: Comment quality is inherently subjective; model reflects patterns in training data
5. **Short Comments**: May struggle with very short comments (< 3 words)

## 🎯 Intended Use

### Recommended Use
- Supplementary tool in code review automation
- Documentation quality auditing
- Developer education and training
- IDE plugins for real-time feedback

### Not Recommended
- Sole decision-maker for code quality
- Production-critical systems without human oversight
- Evaluating non-English comments
- Analyzing code quality (only evaluates comments)

## 🔧 How to Improve Performance

### Fine-tune on Your Domain
```python
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments

# Load the pre-trained model
model = AutoModelForSequenceClassification.from_pretrained("Snaseem2026/code-comment-classifier")

# Fine-tune on your domain-specific data
training_args = TrainingArguments(
    output_dir="./fine_tuned_model",
    learning_rate=1e-5,  # Lower learning rate for fine-tuning
    num_train_epochs=2,
    per_device_train_batch_size=8,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=your_dataset,
)
trainer.train()

📝 License

MIT License - Free to use, modify, and distribute for commercial and non-commercial purposes.

🙏 Acknowledgments

• Built with 🤗 Transformers
• Base model: DistilBERT by Hugging Face
• Inspired by the need for better code documentation practices in software development

📚 Citation

If you use this model in your research or application, please cite:

@misc{code-comment-classifier-2026,
  author = {Naseem, Sharyar},
  title = {Code Comment Quality Classifier},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/Snaseem2026/code-comment-classifier}}
}

📧 Contact

For questions, suggestions, or collaboration:

• 🤗 Hugging Face: @Snaseem2026
• 📫 Issues: Report on the model's discussion tab

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Made with ❤️ for the developer community

🤗 Model Hub • Report Issue

Limitations

• Trained on synthetic data; may require fine-tuning for specific domains
• English comments only
• Evaluates comments in isolation without code context
• Comment quality assessment is subjective

Intended Use

This model is designed for educational and productivity purposes. Use as a supplementary tool in code review processes, not as a replacement for human judgment.

License

MIT License - Free to use, modify, and distribute.

Citation

@misc{code-comment-classifier-2026,
  title={Code Comment Quality Classifier},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/your-username/code-comment-classifier}}
}

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Built with Hugging Face Transformers • Base model: DistilBERT