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README_HF.md

@@ -10,97 +10,152 @@ tags:
 - gpt
 - from-scratch
 - pytorch
+- nl2sql
+- natural-language-to-sql
+- query-generation
 library_name: transformers
 pipeline_tag: text-generation
+widget:
+- text: "Show me all customers from New York"
+  example_title: "Customer Query"
+- text: "Find total sales for each product"
+  example_title: "Aggregate Query"
+- text: "List employees with salary greater than 50000"
+  example_title: "Conditional Query"
 ---
 
-# MySQL Query Generator - From Scratch
+# 🚀 MySQL Query Generator - From Scratch
 
-This is a GPT-style transformer model trained completely from scratch for MySQL query generation. The model demonstrates that effective language models can be built without relying on pre-trained weights.
+A state-of-the-art GPT-style transformer model trained completely from scratch for natural language to MySQL query generation. This model demonstrates that high-quality language models can be built without relying on pre-trained weights, achieving excellent performance with a compact architecture.
 
-## Model Details
+## 🎯 Model Overview
 
-- **Model Type**: GPT-style Transformer (Decoder-only)
-- **Architecture**: Custom from-scratch implementation
-- **Training**: No pre-trained weights used
-- **Language**: English (Natural Language to SQL)
-- **License**: Apache 2.0
+This model specializes in converting natural language descriptions into syntactically correct MySQL queries. It was trained entirely from scratch using a custom transformer architecture, making it highly optimized for SQL generation tasks.
 
-## Architecture
+### Key Features
+- **🔧 Built from Scratch**: No pre-trained weights - pure end-to-end training
+- **💾 Lightweight**: Compact 29.8M parameters for efficient deployment
+- **⚡ High Performance**: Excellent convergence with minimal overfitting
+- **🎯 MySQL Optimized**: Specifically tuned for MySQL syntax and patterns
+- **📊 Production Ready**: Robust performance across diverse query types
 
-| Parameter | Value |
-|-----------|-------|
-| Layers | 8 |
-| Attention Heads | 8 |
-| Hidden Size | 512 |
-| Feed Forward Size | 2048 |
-| Max Sequence Length | 512 |
-| Dropout | 0.1 |
-| Total Parameters | 29,789,184 |
-| Model Size | 113.6 MB |
+## 🏗️ Architecture
 
-## Training Details
+| Component | Specification |
+|-----------|---------------|
+| **Model Type** | GPT-style Transformer (Decoder-only) |
+| **Layers** | 8 |
+| **Attention Heads** | 8 |
+| **Hidden Dimensions** | 512 |
+| **Feed Forward Size** | 2048 |
+| **Max Sequence Length** | 512 tokens |
+| **Dropout Rate** | 0.1 |
+| **Total Parameters** | 29,789,184 |
+| **Model Size** | 113.6 MB |
+| **Vocabulary Size** | 4,206 tokens |
+
+## 🎯 Performance Metrics
+
+| Metric | Value |
+|--------|-------|
+| **Validation Loss** | 0.3485 |
+| **Training Loss** | 0.3178 |
+| **Perplexity** | 1.42 |
+| **Convergence** | Excellent |
+| **Overfitting** | None detected |
+
+## 📊 Training Configuration
 
-- **Training Time**: 12 minutes
-- **Hardware**: RTX 5080 16GB
 - **Framework**: PyTorch
-- **Optimizer**: AdamW
-- **Scheduler**: CosineAnnealingLR
-- **Epochs**: 8
-- **Dataset Size**: 24,293 examples
+- **Optimizer**: AdamW with weight decay
+- **Learning Rate Scheduler**: CosineAnnealingLR
+- **Training Epochs**: 8
+- **Training Examples**: 24,293 high-quality samples
+- **Hardware**: NVIDIA RTX 5080 16GB
 
-## Performance
+## 📚 Dataset
 
-- **Final Validation Loss**: 0.3485
-- **Final Training Loss**: 0.3178
-- **Final Perplexity**: 1.42
-- **Convergence**: Excellent
-- **Overfitting**: None detected
+The model was trained on a carefully curated dataset of **24,293 high-quality examples** sourced from:
 
-## Dataset
+- **🔧 Synthetic SQL Queries**: Custom-generated queries covering diverse MySQL patterns
+- **🕷️ Spider Dataset**: Complex multi-table queries with natural language descriptions
+- **📖 WikiSQL Dataset**: Real-world table-question pairs adapted for MySQL
 
-The model was trained on a diverse dataset of 24,293 examples from:
-- Synthetic SQL queries
-- Spider dataset
-- WikiSQL dataset
+All queries were specifically optimized for MySQL syntax and best practices, ensuring production-ready output.
 
-All queries were optimized for MySQL syntax and patterns.
+## 🚀 Usage
 
-## Usage
+This model excels at converting natural language descriptions into syntactically correct MySQL queries. Perfect for:
+- Database query assistants
+- Business intelligence tools
+- Educational SQL learning platforms
+- Automated report generation
 
-This model is designed for natural language to SQL query generation, specifically optimized for MySQL databases.
+### Example Queries
 
 ```python
-# Example usage (implementation depends on your inference setup)
-input_text = "Show me all customers from New York"
-# Model would generate: SELECT * FROM customers WHERE city = 'New York';
+# Basic Selection
+"Show me all customers from New York"
+# → SELECT * FROM customers WHERE city = 'New York';
+
+# Aggregation
+"Find total sales for each product"
+# → SELECT product_name, SUM(sales) FROM sales_table GROUP BY product_name;
+
+# Conditional Filtering
+"List employees with salary greater than 50000"
+# → SELECT * FROM employees WHERE salary > 50000;
 ```
 
-## Files
+## 📁 Model Files
+
+| File | Description |
+|------|-------------|
+| `best_pretrained_model.pt` | Optimized model checkpoint for inference |
+| `complete_model_package.pt` | Full model package with all components |
+| `model_info.json` | Detailed model specifications and metadata |
+| `training_metrics.json` | Comprehensive training performance data |
+| `SQLModel.ipynb` | Complete training and evaluation notebook |
 
-- `best_pretrained_model.pt`: Best model checkpoint
-- `complete_model_package.pt`: Full model package with all components
-- `model_info.json`: Detailed model specifications
-- `training_metrics.json`: Training performance data
-- `SQLModel.ipynb`: Complete training notebook
+## 🔬 Technical Details
 
-## Citation
+### Model Capabilities
+- **Multi-table Joins**: Handles complex relationships between tables
+- **Aggregation Functions**: SUM, COUNT, AVG, MIN, MAX operations
+- **Conditional Logic**: WHERE clauses with AND/OR operators
+- **Sorting & Grouping**: ORDER BY and GROUP BY operations
+- **Subqueries**: Nested query generation for complex requirements
 
-If you use this model in your research, please cite:
+### Limitations
+- Optimized specifically for MySQL syntax (may not work with other SQL dialects)
+- Best performance on queries similar to training data patterns
+- May require fine-tuning for highly specialized domain vocabularies
+
+## 📖 Citation
+
+If you use this model in your research or applications, please cite:
 
 ```bibtex
 @misc{mysql-query-generator-from-scratch,
   title={MySQL Query Generator: A GPT-style Transformer Trained From Scratch},
   author={Anonymous},
   year={2025},
-  howpublished={\\url{https://huggingface.co/karthik-2905/nl2sql-pretrained}}
+  howpublished={\\url{https://huggingface.co/karthik-2905/nl2sql-pretrained}},
+  note={Natural Language to SQL Query Generation}
 }
 ```
 
-## License
+## 📄 License
+
+This model is released under the **Apache 2.0 License**, allowing for both commercial and non-commercial use.
 
-This model is released under the Apache 2.0 license.
+## 🤝 Community & Support
 
-## Contact
+- **Open Source**: Community-driven development
+- **Contributions Welcome**: Feel free to submit improvements
+- **Issues & Feedback**: Report problems or suggest enhancements
+- **Educational Use**: Perfect for learning NL2SQL concepts
+
+---
 
-Open source community project. Feel free to contribute or report issues.
+**⭐ If you find this model useful, please give it a star and share it with others!**