---
language: 
- en
license: mit
library_name: transformers
tags:
- text-classification
- code-quality
- documentation
- code-comments
- developer-tools
- code-review
- distilbert
datasets:
- synthetic
metrics:
- accuracy
- f1
- precision
- recall
base_model: distilbert-base-uncased
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  example_title: "Outdated Comment"
- text: "Validates user input against SQL injection attacks using parameterized queries"
  example_title: "Excellent Example 2"
- text: "magic happens here"
  example_title: "Unclear Example 2"
model-index:
- name: code-comment-classifier
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Synthetic Code Comments
      type: synthetic
    metrics:
    - type: accuracy
      value: 0.9485
      name: Accuracy
      verified: false
    - type: f1
      value: 0.9468
      name: F1 Score
      verified: false
    - type: precision
      value: 0.9535
      name: Precision
      verified: false
    - type: recall
      value: 0.9485
      name: Recall
      verified: false
---

# 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| 🚫 **Outdated** | 92.3% | 100% | Deprecated, obsolete, or TODO comments |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)

```python
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

```python
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](https://huggingface.co/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](https://huggingface.co/transformers/)
- Base model: [DistilBERT](https://huggingface.co/distilbert-base-uncased) 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:

```bibtex
@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](https://huggingface.co/Snaseem2026)
- 📫 Issues: Report on the model's discussion tab

---

<div align="center">

**Made with ❤️ for the developer community**

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Transformers](https://img.shields.io/badge/Transformers-4.35+-blue.svg)](https://github.com/huggingface/transformers)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)

[🤗 Model Hub](https://huggingface.co/Snaseem2026/code-comment-classifier) • [Report Issue](https://huggingface.co/Snaseem2026/code-comment-classifier/discussions)

</div>

## 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

```bibtex
@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}}
}
```

---

Built with [Hugging Face Transformers](https://huggingface.co/transformers/) • Base model: [DistilBERT](https://huggingface.co/distilbert-base-uncased)