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- # MySQL Query Generator - From Scratch
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- A GPT-style transformer model trained completely from scratch for MySQL query generation. This project demonstrates training a language model from scratch without using any pre-trained weights.
 
 
 
 
 
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- ## πŸš€ Features
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- - **Built from Scratch**: Pure PyTorch implementation of GPT-style transformer architecture
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- - **MySQL Focused**: Specifically trained for MySQL query generation
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- - **Lightweight**: 29.8M parameters, 113MB model size
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- - **Fast Training**: Trained in just 12 minutes on RTX 5080 16GB
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- - **Production Ready**: Excellent convergence with no overfitting detected
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- ## πŸ“Š Model Architecture
 
 
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- - **Type**: GPT-style Transformer (Decoder-only)
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- - **Layers**: 8
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- - **Attention Heads**: 8
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- - **Hidden Size**: 512
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- - **Feed Forward Size**: 2048
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- - **Max Sequence Length**: 512
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- - **Dropout**: 0.1
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- - **Total Parameters**: 29,789,184
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- ## 🎯 Performance
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- - **Final Validation Loss**: 0.3485
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- - **Final Training Loss**: 0.3178
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- - **Final Perplexity**: 1.42
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- - **Training Time**: 12 minutes
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- - **Hardware**: RTX 5080 16GB
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- ## πŸ“ˆ Dataset
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- - **Size**: 24,293 training examples
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- - **Sources**:
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- - Synthetic SQL queries
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- - Spider dataset
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- - WikiSQL dataset
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- - **Specificity**: MySQL-optimized queries
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- - **Diversity**: High variety of query patterns
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- ## πŸ› οΈ Usage
 
 
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- The model is designed for natural language to SQL query generation tasks, specifically optimized for MySQL databases.
 
 
 
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- ## πŸ“ Files
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- - `SQLModel.ipynb`: Complete training and evaluation notebook
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- - `best_pretrained_model.pt`: Best model checkpoint
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- - `complete_model_package.pt`: Full model package
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- - `training_curves.png`: Training loss visualization
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- - `pretraining_curves.png`: Pre-training metrics
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- - `model_info.json`: Detailed model specifications
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- - `performance_evaluation.json`: Performance metrics
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- ## πŸ”§ Training Configuration
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- - **Framework**: PyTorch
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- - **Optimizer**: AdamW
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- - **Scheduler**: CosineAnnealingLR
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- - **Epochs**: 8
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- - **No Pre-trained Weights**: Trained completely from scratch
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## πŸ“„ License
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- Open Source
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- ## 🀝 Contributing
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- This is an open source project. Contributions are welcome!
 
 
 
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- ## πŸ“ž Contact
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- Open source community project.
 
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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ tags:
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+ - text-generation
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+ - sql
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+ - mysql
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+ - transformer
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+ - gpt
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+ - from-scratch
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+ - pytorch
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+ - nl2sql
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+ - natural-language-to-sql
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+ - query-generation
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ widget:
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+ - text: "Show me all customers from New York"
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+ example_title: "Customer Query"
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+ - text: "Find total sales for each product"
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+ example_title: "Aggregate Query"
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+ - text: "List employees with salary greater than 50000"
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+ example_title: "Conditional Query"
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+ ---
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+
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+ # πŸš€ MySQL Query Generator - From Scratch
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+
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+ 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.
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+
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+ ## 🎯 Model Overview
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+
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+ 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.
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+
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+ ### Key Features
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+ - **πŸ”§ Built from Scratch**: No pre-trained weights - pure end-to-end training
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+ - **πŸ’Ύ Lightweight**: Compact 29.8M parameters for efficient deployment
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+ - **⚑ High Performance**: Excellent convergence with minimal overfitting
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+ - **🎯 MySQL Optimized**: Specifically tuned for MySQL syntax and patterns
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+ - **πŸ“Š Production Ready**: Robust performance across diverse query types
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+
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+ ## πŸ—οΈ Architecture
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+
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+ | Component | Specification |
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+ |-----------|---------------|
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+ | **Model Type** | GPT-style Transformer (Decoder-only) |
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+ | **Layers** | 8 |
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+ | **Attention Heads** | 8 |
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+ | **Hidden Dimensions** | 512 |
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+ | **Feed Forward Size** | 2048 |
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+ | **Max Sequence Length** | 512 tokens |
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+ | **Dropout Rate** | 0.1 |
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+ | **Total Parameters** | 29,789,184 |
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+ | **Model Size** | 113.6 MB |
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+ | **Vocabulary Size** | 4,206 tokens |
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+
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+ ## 🎯 Performance Metrics
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+
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+ | Metric | Value |
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+ |--------|-------|
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+ | **Validation Loss** | 0.3485 |
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+ | **Training Loss** | 0.3178 |
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+ | **Perplexity** | 1.42 |
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+ | **Convergence** | Excellent |
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+ | **Overfitting** | None detected |
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+
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+ ## πŸ“Š Training Configuration
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+ - **Framework**: PyTorch
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+ - **Optimizer**: AdamW with weight decay
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+ - **Learning Rate Scheduler**: CosineAnnealingLR
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+ - **Training Epochs**: 8
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+ - **Training Examples**: 24,293 high-quality samples
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+ - **Hardware**: NVIDIA RTX 5080 16GB
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+ ## πŸ“š Dataset
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+ The model was trained on a carefully curated dataset of **24,293 high-quality examples** sourced from:
 
 
 
 
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+ - **πŸ”§ Synthetic SQL Queries**: Custom-generated queries covering diverse MySQL patterns
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+ - **πŸ•·οΈ Spider Dataset**: Complex multi-table queries with natural language descriptions
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+ - **πŸ“– WikiSQL Dataset**: Real-world table-question pairs adapted for MySQL
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+ All queries were specifically optimized for MySQL syntax and best practices, ensuring production-ready output.
 
 
 
 
 
 
 
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+ ## πŸš€ Usage
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+ This model excels at converting natural language descriptions into syntactically correct MySQL queries. Perfect for:
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+ - Database query assistants
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+ - Business intelligence tools
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+ - Educational SQL learning platforms
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+ - Automated report generation
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+ ### Example Queries
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+ ```python
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+ # Basic Selection
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+ "Show me all customers from New York"
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+ # β†’ SELECT * FROM customers WHERE city = 'New York';
 
 
 
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+ # Aggregation
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+ "Find total sales for each product"
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+ # β†’ SELECT product_name, SUM(sales) FROM sales_table GROUP BY product_name;
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+ # Conditional Filtering
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+ "List employees with salary greater than 50000"
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+ # β†’ SELECT * FROM employees WHERE salary > 50000;
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+ ```
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+ ## πŸ“ Model Files
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+ | File | Description |
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+ |------|-------------|
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+ | `best_pretrained_model.pt` | Optimized model checkpoint for inference |
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+ | `complete_model_package.pt` | Full model package with all components |
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+ | `model_info.json` | Detailed model specifications and metadata |
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+ | `training_metrics.json` | Comprehensive training performance data |
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+ | `SQLModel.ipynb` | Complete training and evaluation notebook |
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+ ## πŸ”¬ Technical Details
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+ ### Model Capabilities
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+ - **Multi-table Joins**: Handles complex relationships between tables
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+ - **Aggregation Functions**: SUM, COUNT, AVG, MIN, MAX operations
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+ - **Conditional Logic**: WHERE clauses with AND/OR operators
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+ - **Sorting & Grouping**: ORDER BY and GROUP BY operations
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+ - **Subqueries**: Nested query generation for complex requirements
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+
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+ ### Limitations
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+ - Optimized specifically for MySQL syntax (may not work with other SQL dialects)
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+ - Best performance on queries similar to training data patterns
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+ - May require fine-tuning for highly specialized domain vocabularies
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+
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+ ## πŸ“– Citation
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+
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+ If you use this model in your research or applications, please cite:
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+
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+ ```bibtex
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+ @misc{mysql-query-generator-from-scratch,
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+ title={MySQL Query Generator: A GPT-style Transformer Trained From Scratch},
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+ author={Anonymous},
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+ year={2025},
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+ howpublished={\\url{https://huggingface.co/karthik-2905/nl2sql-pretrained}},
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+ note={Natural Language to SQL Query Generation}
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+ }
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+ ```
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  ## πŸ“„ License
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+ This model is released under the **Apache 2.0 License**, allowing for both commercial and non-commercial use.
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+ ## 🀝 Community & Support
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+ - **Open Source**: Community-driven development
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+ - **Contributions Welcome**: Feel free to submit improvements
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+ - **Issues & Feedback**: Report problems or suggest enhancements
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+ - **Educational Use**: Perfect for learning NL2SQL concepts
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+ ---
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+ **⭐ If you find this model useful, please give it a star and share it with others!**