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README.md

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+---
+license: mit
+tags:
+- machine-learning
+- xgboost
+- quantum-enhanced
+- bleu-js
+- classification
+- gradient-boosting
+datasets:
+- custom
+metrics:
+- accuracy
+- f1-score
+- roc-auc
+model-index:
+- name: bleu-xgboost-classifier
+  results:
+  - task:
+      type: classification
+    dataset:
+      name: Custom Dataset
+      type: custom
+    metrics:
+    - type: accuracy
+      value: TBD
+    - type: f1-score
+      value: TBD
+    - type: roc-auc
+      value: TBD
+---
+
+# Bleu.js XGBoost Classifier
+
+## Model Description
+
+This is an XGBoost classification model from the Bleu.js quantum-enhanced AI platform. The model combines classical gradient boosting with quantum computing capabilities for improved performance and feature extraction.
+
+## Model Details
+
+### Model Type
+- **Architecture**: XGBoost Classifier
+- **Framework**: XGBoost with quantum-enhanced features
+- **Task**: Binary Classification
+- **Version**: 1.2.1
+
+### Training Details
+
+#### Training Data
+- **Dataset**: Custom training dataset
+- **Training Script**: `backend/train_xgboost.py`
+- **Data Split**: 80% training, 20% validation
+
+#### Hyperparameters
+- `max_depth`: 6
+- `learning_rate`: 0.1
+- `n_estimators`: 100
+- `objective`: binary:logistic
+- `random_state`: 42
+- `early_stopping_rounds`: 10
+
+#### Preprocessing
+- Feature scaling with StandardScaler
+- Quantum-enhanced feature extraction (optional)
+- Data normalization
+
+### Model Files
+
+- `xgboost_model_latest.pkl`: The trained XGBoost model (latest version)
+- `xgboost_model.pkl`: The trained XGBoost model
+- `scaler_latest.pkl`: Feature scaler for preprocessing (latest version)
+- `scaler.pkl`: Feature scaler for preprocessing
+
+## How to Use
+
+### Installation
+
+```bash
+pip install xgboost numpy scikit-learn
+```
+
+### Basic Usage
+
+```python
+import pickle
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+# Load the model and scaler
+with open('xgboost_model_latest.pkl', 'rb') as f:
+    model = pickle.load(f)
+
+with open('scaler_latest.pkl', 'rb') as f:
+    scaler = pickle.load(f)
+
+# Prepare your data (numpy array with shape: n_samples, n_features)
+X = np.array([[feature1, feature2, ...]])
+
+# Scale the features
+X_scaled = scaler.transform(X)
+
+# Make predictions
+predictions = model.predict(X_scaled)
+probabilities = model.predict_proba(X_scaled)
+
+print(f"Predictions: {predictions}")
+print(f"Probabilities: {probabilities}")
+```
+
+### Using with Bleu.js
+
+```python
+from bleujs import BleuJS
+
+# Initialize BleuJS with quantum enhancements
+bleu = BleuJS(
+    quantum_mode=True,
+    model_path="xgboost_model_latest.pkl",
+    device="cuda"  # or "cpu"
+)
+
+# Process data with quantum features
+results = bleu.process(
+    input_data=your_data,
+    quantum_features=True
+)
+```
+
+### Download from Hugging Face
+
+```python
+from huggingface_hub import hf_hub_download
+import pickle
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="helloblueai/bleu-xgboost-classifier",
+    filename="xgboost_model_latest.pkl"
+)
+
+scaler_path = hf_hub_download(
+    repo_id="helloblueai/bleu-xgboost-classifier",
+    filename="scaler_latest.pkl"
+)
+
+# Load model
+with open(model_path, 'rb') as f:
+    model = pickle.load(f)
+
+with open(scaler_path, 'rb') as f:
+    scaler = pickle.load(f)
+```
+
+## Model Performance
+
+Performance metrics will be updated after evaluation. The model uses:
+- Early stopping to prevent overfitting
+- Cross-validation for robust evaluation
+- Quantum-enhanced features for improved accuracy
+
+## Limitations and Bias
+
+- This model was trained on a specific dataset and may not generalize to other domains
+- Performance may vary depending on input data distribution
+- Quantum enhancements require compatible hardware for optimal performance
+- Model performance depends on data quality and feature engineering
+
+## Training Information
+
+### Training Script
+The model is trained using `backend/train_xgboost.py`:
+
+```python
+params = {
+    "max_depth": 6,
+    "learning_rate": 0.1,
+    "n_estimators": 100,
+    "objective": "binary:logistic",
+    "random_state": 42,
+}
+```
+
+### Evaluation
+- Validation set: 20% of training data
+- Early stopping: 10 rounds
+- Evaluation metric: Log loss (default)
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@software{bleu_js_2024,
+  title={Bleu.js: Quantum-Enhanced AI Platform},
+  author={HelloblueAI},
+  year={2024},
+  url={https://github.com/HelloblueAI/Bleu.js},
+  version={1.2.1}
+}
+```
+
+## License
+
+This model is released under the MIT License. See the LICENSE file for more details.
+
+## Contact
+
+For questions or issues, please contact:
+- **Email**: [email protected]
+- **GitHub**: https://github.com/HelloblueAI/Bleu.js
+- **Organization**: https://huggingface.co/helloblueai
+
+## Acknowledgments
+
+This model is part of the Bleu.js project, which combines classical machine learning with quantum computing capabilities for enhanced performance.
+
+## Related Models
+
+- Bleu.js Quantum Vision Model
+- Bleu.js Hybrid Neural Network
+- Bleu.js Quantum Feature Extractor
+