| --- |
| license: cc-by-4.0 |
| tags: |
| - Text-to-sql |
| library_name: transformers |
| --- |
| # OGSQL-Mistral7B |
|
|
|  |
|
|
| ### Model Description |
| OGSQL-Mistral7B was fine-tuned for the task of converting natural language text into SQL queries. |
|
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|
|
| - **Model type**: Mixture Of Experts (MoE) |
| - **Language(s) (NLP)**: SQL (target language for generation) |
| - **Finetuned from model**: Mistral 7B instruct |
|
|
| ## Use Case |
| OGSQL-7B is designed to facilitate the conversion of natural language queries into structured SQL commands, aiding in database querying without the need for manual SQL knowledge. |
|
|
| ## How to Get Started with the Model |
| ```python |
| # Example code to load and use the model |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
| |
| model_name = "OGSQL-Mistral7B" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
| |
| def generate_sql(query): |
| inputs = tokenizer.encode(query, return_tensors="pt") |
| outputs = model.generate(inputs) |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) |
| |
| # Example use |
| query = """ |
| using this context: |
| -- Create Customers Table |
| CREATE TABLE Customers ( |
| customer_id INTEGER PRIMARY KEY, |
| name TEXT NOT NULL, |
| email TEXT, |
| join_date DATE |
| ); |
| |
| -- Create Products Table |
| CREATE TABLE Products ( |
| product_id INTEGER PRIMARY KEY, |
| name TEXT NOT NULL, |
| price DECIMAL(10, 2) |
| ); |
| |
| -- Create Orders Table |
| CREATE TABLE Orders ( |
| order_id INTEGER PRIMARY KEY, |
| customer_id INTEGER, |
| product_id INTEGER, |
| order_date DATE, |
| quantity INTEGER, |
| total_price DECIMAL(10, 2), |
| FOREIGN KEY (customer_id) REFERENCES Customers(customer_id), |
| FOREIGN KEY (product_id) REFERENCES Products(product_id) |
| ); |
| |
| show me all the orders from last month , sort by date |
| |
| |
| """ |
| print(generate_sql(query)) |
| |
| ``` |
|
|
|
|
| ## alternatively you can use this notebook: |
| [](https://colab.research.google.com/drive/1pQuIuCdoFMG76AH3BNZzep8PgRaZkkYS?usp=sharing) |