Snaseem2026
/

code-comment-classifier

@@ -1,314 +1,121 @@
----
-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-  example_title: "Outdated Comment"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-| 🚫 **Outdated** | 92.3% | 100% | Deprecated, obsolete, or TODO comments |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)

 # 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 an issue on the GitHub repository or reach out on Hugging Face.
 ---
+**Note**: This model is designed for educational and productivity purposes. Always review automated suggestions with human judgment.