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MODEL_CARD.md

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+---
+language: en
+license: mit
+tags:
+  - text-classification
+  - code-quality
+  - documentation
+  - code-comments
+  - developer-tools
+datasets:
+  - synthetic
+metrics:
+  - accuracy
+  - f1
+  - precision
+  - recall
+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+    example_title: "Outdated Comment"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 🔍
+
+## Model Description
+
+This model automatically classifies code comments into four quality categories to help improve code documentation and review processes. It's designed to assist developers in maintaining high-quality code documentation by identifying comments that may need improvement.
+
+**Categories:**
+- 🌟 **Excellent**: Clear, comprehensive, and highly informative comments that explain the "why" and "how"
+- ✅ **Helpful**: Good comments that add value but could be more detailed
+- ⚠️ **Unclear**: Vague or confusing comments that don't provide sufficient information
+- 🚫 **Outdated**: Comments that may no longer reflect the current code or are marked as deprecated
+
+## Intended Uses
+
+### Primary Use Cases
+- **Code Review Automation**: Automatically flag low-quality comments during pull request reviews
+- **Documentation Quality Audits**: Scan codebases to identify areas needing documentation improvements
+- **Developer Education**: Help developers learn what constitutes good code comments
+- **IDE Integration**: Provide real-time feedback on comment quality while coding
+
+### Out-of-Scope Use Cases
+- Generating new comments (this is a classification model, not a generation model)
+- Evaluating code quality (only evaluates comments, not the code itself)
+- Security analysis or vulnerability detection
+- Production-critical decision making without human review
+
+## How to Use
+
+### Quick Start
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load 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 fibonacci numbers 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]}")
+```
+
+### Batch Processing
+
+```python
+comments = [
+    "Handles user authentication and session management",
+    "does stuff",
+    "TODO: fix this later"
+]
+
+inputs = tokenizer(comments, return_tensors="pt", truncation=True, 
+                   padding=True, max_length=512)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=-1)
+
+for comment, pred in zip(comments, predictions):
+    print(f"{comment}: {labels[pred.item()]}")
+```
+
+## Training Data
+
+### Dataset
+The model was trained on a synthetic dataset of code comments carefully crafted to represent the four quality categories. The training data consists of:
+
+- **Total samples**: ~1,000 comments
+- **Distribution**: Balanced across all four categories
+- **Language**: English code comments
+- **Sources**: Synthetic data based on common patterns in real-world code comments
+
+### Data Creation
+The synthetic dataset was created by:
+1. Identifying common patterns in high-quality and low-quality code comments
+2. Generating representative examples for each category
+3. Creating variations to increase diversity
+4. Ensuring balanced representation across all classes
+
+**Note**: This model was trained on synthetic data. For production use, consider fine-tuning on domain-specific comments from your codebase.
+
+## Training Procedure
+
+### Preprocessing
+- Text tokenization using DistilBERT tokenizer
+- Maximum sequence length: 512 tokens
+- Truncation and padding applied
+
+### Training Hyperparameters
+
+```yaml
+- Base Model: distilbert-base-uncased
+- Training Epochs: 3
+- Batch Size: 16 (train), 32 (eval)
+- Learning Rate: 2e-5
+- Weight Decay: 0.01
+- Warmup Steps: 500
+- Optimizer: AdamW
+```
+
+### Training Infrastructure
+- Framework: Hugging Face Transformers
+- Hardware: CPU/GPU compatible
+- Training Time: ~10-30 minutes (depending on hardware)
+
+## Evaluation Results
+
+### Metrics
+
+The model achieves the following performance on the test set:
+
+| Metric | Score |
+|--------|-------|
+| Accuracy | 0.9485 (94.85%) |
+| Precision (weighted) | 0.9535 (95.35%) |
+| Recall (weighted) | 0.9485 (94.85%) |
+| F1 Score (weighted) | 0.9468 (94.68%) |
+
+### Per-Class Performance
+
+| Class | Precision | Recall | F1-Score |
+|-------|-----------|--------|----------|
+| Excellent | 1.0000 (100%) | 1.0000 (100%) | 1.0000 (100%) |
+| Helpful | 0.8889 (88.9%) | 1.0000 (100%) | 0.9412 (94.1%) |
+| Unclear | 1.0000 (100%) | 0.7917 (79.2%) | 0.8837 (88.4%) |
+| Outdated | 0.9231 (92.3%) | 1.0000 (100%) | 0.9600 (96.0%) |
+
+### 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
+
+### Known Limitations
+
+1. **Synthetic Training Data**: The model was trained on synthetic data and may not capture all nuances of real-world code comments
+2. **Language**: Only trained on English comments
+3. **Context**: Evaluates comments in isolation without code context
+4. **Domain**: May perform differently on specialized domains (e.g., scientific computing, embedded systems)
+5. **Subjectivity**: Comment quality can be subjective; the model reflects patterns in the training data
+
+### Recommendations
+
+- Use as a supplementary tool, not a replacement for human review
+- Fine-tune on domain-specific data for better performance
+- Validate predictions in your specific use case
+- Combine with other code quality tools for comprehensive analysis
+
+## Bias and Fairness
+
+### Potential Biases
+
+- **Style Bias**: May favor certain commenting styles over others
+- **Verbosity Bias**: Longer comments may be rated higher regardless of actual quality
+- **Pattern Bias**: Trained on specific patterns that may not represent all commenting approaches
+
+### Mitigation Strategies
+
+- Train on diverse comment styles
+- Regular evaluation on real-world data
+- User feedback integration
+- Continuous model improvement
+
+## Environmental Impact
+
+- **Base Model**: DistilBERT (~66M parameters)
+- **Carbon Footprint**: Minimal for training on small synthetic dataset
+- **Inference**: Efficient, suitable for real-time applications
+
+## 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},
+  howpublished = {\url{https://huggingface.co/Snaseem2026/code-comment-classifier}}
+}
+```
+
+## Model Card Authors
+
+- Sharyar Naseem (@Snaseem2026)
+
+## Model Card Contact
+
+For questions or feedback, please open an issue on the model's discussion tab or contact via Hugging Face.
+
+## License
+
+MIT License - See [LICENSE](LICENSE) file for details.
+
+## Acknowledgments
+
+- Built with [Hugging Face 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
+
+---
+
+**Disclaimer**: This model is provided for educational and productivity purposes. Always apply human judgment when evaluating code quality and documentation.