| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - text2text-generation |
| - natural-language-to-sql |
| - natural-language-to-mongodb |
| - query-generation |
| - database |
| - t5 |
| datasets: |
| - custom |
| metrics: |
| - bleu |
| - accuracy |
| pipeline_tag: text-generation |
| widget: |
| - text: "Find employees where salary is greater than 50000" |
| example_title: "SELECT Query" |
| - text: "Delete orders with total_amount less than 1000" |
| example_title: "DELETE Query" |
| - text: "Update employees set department to Sales where employee_id is 101" |
| example_title: "UPDATE Query" |
| - text: "Insert a new employee with name John Doe, email john@example.com, department Engineering" |
| example_title: "INSERT Query" |
| --- |
| |
| # NoNoQL - Natural Language to SQL/MongoDB Query Generator |
|
|
| **NoNoQL** (formerly TexQL) is a T5-based transformer model that converts natural language queries into both SQL and MongoDB queries. It supports SELECT, INSERT, UPDATE, DELETE, and other database operations. |
|
|
| ## 🎯 Model Description |
|
|
| This model translates natural language database queries into syntactically correct SQL and MongoDB commands. It's trained on a custom dataset of 30,000+ query pairs covering various database operations, tables, and query patterns. |
|
|
| ### Key Features |
|
|
| - ✅ **Dual Output**: Generates both SQL and MongoDB queries from a single natural language input |
| - ✅ **Multi-Operation Support**: SELECT, INSERT, UPDATE, DELETE, CREATE TABLE, and more |
| - ✅ **Comparison Operators**: Handles greater than, less than, equal to, and other comparisons |
| - ✅ **Complex Queries**: Supports WHERE clauses, aggregations, ordering, and limiting |
| - ✅ **Post-Processing**: Includes fixes for common model hallucinations and syntax errors |
|
|
| ## 📊 Model Details |
|
|
| - **Model Architecture**: T5 (Text-to-Text Transfer Transformer) |
| - **Base Model**: google/t5-small |
| - **Parameters**: ~60M |
| - **Training Data**: 30,000+ natural language to SQL/MongoDB query pairs |
| - **Training Strategy**: Unified model trained on both SQL and MongoDB simultaneously |
| - **Input Format**: `translate to {sql|mongodb}: {natural_language_query}` |
|
|
| ### Supported Tables/Collections |
|
|
| - **employees**: employee_id, name, email, department, salary, hire_date, age |
| - **departments**: department_id, department_name, manager_id, budget, location |
| - **projects**: project_id, project_name, start_date, end_date, budget, status |
| - **orders**: order_id, customer_name, product_name, quantity, order_date, total_amount |
| - **products**: product_id, product_name, category, price, stock_quantity, supplier |
| |
| ## 🚀 Usage |
| |
| ### Installation |
| |
| ```bash |
| pip install transformers torch |
| ``` |
| |
| ### Basic Usage |
| |
| ```python |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| |
| # Load model and tokenizer |
| model_name = "mohhhhhit/nonoql" # Replace with your HF model path |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
|
|
| # Generate SQL query |
| def generate_query(natural_language, target_type='sql'): |
| input_text = f"translate to {target_type}: {natural_language}" |
| inputs = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True) |
| |
| outputs = model.generate( |
| **inputs, |
| max_length=512, |
| num_beams=10, |
| temperature=0.3, |
| repetition_penalty=1.2, |
| length_penalty=0.8, |
| early_stopping=True |
| ) |
| |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) |
| |
| # Example usage |
| nl_query = "Find employees where salary is greater than 50000" |
| |
| sql_query = generate_query(nl_query, target_type='sql') |
| print(f"SQL: {sql_query}") |
| # Output: SELECT * FROM employees WHERE salary > 50000; |
|
|
| mongodb_query = generate_query(nl_query, target_type='mongodb') |
| print(f"MongoDB: {mongodb_query}") |
| # Output: db.employees.find({"salary": {$gt: 50000}}); |
| ``` |
| |
| ### Example Queries |
| |
| | Natural Language | SQL Output | MongoDB Output | |
| |-----------------|------------|----------------| |
| | Show all employees | `SELECT * FROM employees;` | `db.employees.find({});` | |
| | Find products where price is less than 100 | `SELECT * FROM products WHERE price < 100;` | `db.products.find({"price": {$lt: 100}});` | |
| | Update employees set department to Sales where employee_id is 101 | `UPDATE employees SET department = 'Sales' WHERE employee_id = 101;` | `db.employees.updateMany({employee_id: 101}, {$set: {department: "Sales"}});` | |
| | Delete orders with total_amount less than 1000 | `DELETE FROM orders WHERE total_amount < 1000;` | `db.orders.deleteMany({"total_amount": {$lt: 1000}});` | |
| | Insert a new employee with name John, email john@example.com | `INSERT INTO employees (name, email) VALUES ('John', 'john@example.com');` | `db.employees.insertOne({"name": "John", "email": "john@example.com"});` | |
| |
| ## 🎓 Training |
| |
| ### Dataset |
| |
| - **Size**: 30,000+ query pairs |
| - **Operations**: SELECT (40%), INSERT (20%), UPDATE (20%), DELETE (15%), CREATE (5%) |
| - **Tables**: 5 main tables with realistic schemas |
| - **Generation**: Synthetic data with varied patterns and complexity |
| |
| ### Training Configuration |
| |
| ```python |
| training_args = { |
| "learning_rate": 3e-4, |
| "per_device_train_batch_size": 8, |
| "per_device_eval_batch_size": 8, |
| "num_train_epochs": 10, |
| "weight_decay": 0.01, |
| "warmup_steps": 500, |
| "max_seq_length": 512, |
| } |
| ``` |
| |
| ### Evaluation Metrics |
|
|
| - **BLEU Score**: ~85% |
| - **Exact Match**: ~78% |
| - **Syntax Correctness**: ~92% (after post-processing) |
|
|
| ## ⚙️ Post-Processing |
|
|
| The model includes several post-processing fixes to handle common issues: |
|
|
| 1. **Comparison Operators**: Converts `=` to `>`, `<`, `>=`, `<=` based on keywords like "greater than", "less than" |
| 2. **Operation Type**: Fixes wrong operations (e.g., SELECT when DELETE is intended) |
| 3. **MongoDB Syntax**: Adds missing curly braces and converts to proper MongoDB operators |
| 4. **UPDATE Queries**: Reconstructs malformed UPDATE statements |
| 5. **CREATE TABLE**: Fixes hallucinated columns in table creation |
|
|
| ## ⚠️ Limitations |
|
|
| - **Schema Awareness**: Model is trained on specific tables; may not generalize to completely new schemas |
| - **Complex Joins**: Limited support for multi-table JOINs and subqueries |
| - **Advanced Features**: May struggle with window functions, CTEs, and advanced SQL features |
| - **Hallucinations**: Can generate incorrect column names for unseen patterns (mitigated by post-processing) |
| - **Case Sensitivity**: Works best with lowercase natural language inputs |
|
|
| ## 📝 Known Issues & Fixes |
|
|
| | Issue | Fix Applied | |
| |-------|-------------| |
| | Model outputs `=` instead of `>` or `<` | Post-processing detects comparison keywords and replaces operators | |
| | MongoDB missing `{}` braces | Adds curly braces around query objects | |
| | `SELECT` instead of `DELETE` | Detects operation intent from keywords | |
| | Incomplete UPDATE queries | Reconstructs from natural language parsing | |
|
|
| ## 🛠️ Use Cases |
|
|
| - **Database Query Assistants**: Help non-technical users query databases |
| - **Educational Tools**: Teach SQL/MongoDB syntax through examples |
| - **Prototyping**: Quickly generate queries for testing |
| - **Documentation**: Auto-generate query examples |
| - **Migration Tools**: Convert between SQL and MongoDB syntaxes |
|
|
| ## 📄 Citation |
|
|
| If you use this model in your research or application, please cite: |
|
|
| ```bibtex |
| @misc{nonoql2026, |
| title={NoNoQL: Natural Language to SQL and MongoDB Query Generation}, |
| author={Mohit Panchal}, |
| year={2026}, |
| howpublished={\url{https://huggingface.co/mohhhhhit/nonoql}}, |
| } |
| ``` |
|
|
| ## 📜 License |
|
|
| This model is released under the Apache 2.0 License. |
|
|
| ## 🤝 Contributing |
|
|
| Contributions, feedback, and suggestions are welcome! Please feel free to: |
| - Report issues or bugs |
| - Suggest new features |
| - Improve the training data |
| - Add support for more database systems |
|
|
| ## 🔗 Links |
|
|
| - **Model Repository**: [Hugging Face](https://huggingface.co/mohhhhhit/nonoql) |
| - **GitHub**: [Source Code](https://github.com/mohhhit/NoNoQL) |
| - **Demo**: [Streamlit App](your-demo-url) |
|
|
| ## 🙏 Acknowledgments |
|
|
| - Built on the T5 architecture by Google Research |
| - Trained using the Hugging Face Transformers library |
| - Inspired by the need for more accessible database querying tools |
|
|
| --- |
|
|
| **Note**: This model is designed for educational and prototyping purposes. Always validate generated queries before executing them on production databases. |
|
|
