---
language:
- en
license: mit
library_name: transformers
pipeline_tag: text-classification
tags:
- code
- code-classification
- programming-language
- codebert
- xgboost
- random-forest
- gradient-boosting
- ensemble
- machine-learning
---

# Nexar Quantum Language Classifier

A high-performance programming language classification model developed for the **Nexar Quantum Code Analysis Engine**.

The model predicts the programming language of a source code snippet using a hybrid ensemble architecture combining a fine-tuned CodeBERT transformer with classical machine learning models.

---

# Model Overview

The classifier combines multiple models to improve prediction accuracy:

- CodeBERT Transformer
- XGBoost
- Random Forest
- Gradient Boosting
- Weighted Ensemble

The ensemble leverages transformer-based semantic understanding together with statistical TF-IDF features for robust language identification.

---

# Supported Languages

The model is trained to classify:

- Python
- Q#
- Qiskit
- OpenQASM
- Cirq

*(The exact list depends on the training dataset.)*

---

# Architecture

```
Source Code
      │
      ▼
 ┌──────────────┐
 │  CodeBERT    │
 └──────────────┘
        │
        ▼

TF-IDF Features
        │
        ▼
 ┌──────────────┐
 │  XGBoost     │
 ├──────────────┤
 │ RandomForest │
 ├──────────────┤
 │GradientBoost │
 └──────────────┘
        │
        ▼
 Weighted Ensemble
        │
        ▼
 Predicted Language
```

---

# Training

The model was trained using:

- Hugging Face Transformers
- Microsoft CodeBERT
- Scikit-learn
- XGBoost
- PyTorch

Feature extraction:

- TF-IDF
- 1–3 gram features
- 5,000 maximum features

---

# Files

| File | Description |
|------|-------------|
| codebert/ | Fine-tuned CodeBERT model |
| tfidf.pkl | TF-IDF Vectorizer |
| xgboost.pkl | XGBoost classifier |
| random_forest.pkl | Random Forest classifier |
| gradient_boosting.pkl | Gradient Boosting classifier |
| label_encoder.pkl | Label encoder |
| ensemble_weights.json | Ensemble weights |

---

# Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("yashodhajayasinghe/nexar-quantum-language-classifier")
model = AutoModelForSequenceClassification.from_pretrained("yashodhajayasinghe/nexar-quantum-language-classifier")
```

---

# Intended Uses

Suitable for:

- Programming language detection
- Static code analysis
- Source code indexing
- Repository analytics
- Intelligent developer tools
- AI-assisted code understanding

---

# Limitations

Performance may decrease for:

- Extremely short code snippets
- Mixed-language files
- Obfuscated or minified code
- Languages not included in the training dataset

---

# Security Notice

This repository contains several serialized Scikit-learn models (`.pkl` files).

These files were generated using **joblib** and contain standard Scikit-learn model objects only.

Python pickle files are inherently executable during deserialization, therefore Hugging Face's automated malware scanner may display heuristic warnings. Users should only load pickle files from trusted sources.

---

# Citation

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

```
Nexar Quantum Language Classifier
Nexar Quantum Code Analysis Engine
2026
```

---

# License

This project is released under the MIT License.

---

# Author

**Yashodha Lasith Jayasinghe**

Software Engineer | AI & Machine Learning Developer

GitHub: https://github.com/yashodalasith

Hugging Face: https://huggingface.co/yashodhajayasinghe