--- license: apache-2.0 datasets: - samadpls/querypls-prompt2sql-dataset - b-mc2/sql-create-context tags: - stabilityai/StableBeluga-7B - langchain - opensource - stabilityai - SatbleBeluga-7B language: - en pipeline_tag: text2text-generation --- # 🛢💬 Querypls-Prompt2SQL ## Overview Querypls-Prompt2SQL is a 💬 text-to-SQL generation model developed by [samadpls](https://github.com/samadpls). It is designed for generating SQL queries based on user prompts. ## Model Usage To get started with the model in Python, you can use the following code: ```python from transformers import pipeline, AutoTokenizer question = "how to get all employees from table0" prompt = f'Your task is to create SQL query of the following {question}, just SQL query and no text' tokenizer = AutoTokenizer.from_pretrained("samadpls/querypls-prompt2sql") pipe = pipeline(task='text-generation', model="samadpls/querypls-prompt2sql", tokenizer=tokenizer, max_length=200) result = pipe(prompt) print(result[0]['generated_text']) ``` Adjust the `question` variable with the desired question, and the generated SQL query will be printed. ## Training Details The model was trained on Google Colab, and its purpose is to be used in the [Querypls](https://github.com/samadpls/Querypls) project with the following training and validation loss progression: ```yaml Copy code Step Training Loss Validation Loss 943 2.332100 2.652054 1886 2.895300 2.551685 2829 2.427800 2.498556 3772 2.019600 2.472013 4715 3.391200 2.465390 ``` `However, note that the model may be too large to load in certain environments.` For more information and details, please refer to the provided [documentation](https://huggingface.co/stabilityai/StableBeluga-7B). ## Model Card Authors - 🤖 [samadpls](https://github.com/samadpls)