code-comment-classifier / MODEL_CARD.md

Snaseem2026

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metadata

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    example_title: Outdated Comment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🚫 Outdated: Comments that may no longer reflect the current code or are marked as deprecated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

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

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:

Identifying common patterns in high-quality and low-quality code comments
Generating representative examples for each category
Creating variations to increase diversity
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

- 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

Synthetic Training Data: The model was trained on synthetic data and may not capture all nuances of real-world code comments
Language: Only trained on English comments
Context: Evaluates comments in isolation without code context
Domain: May perform differently on specialized domains (e.g., scientific computing, embedded systems)
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:

@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 file for details.

Acknowledgments

Built with Hugging Face Transformers
Base model: DistilBERT 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.