README.md

metadata

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. It is designed for generating SQL queries based on user prompts.

Model Details

• License: Apache-2.0
• Datasets:
  • samadpls/querypls-prompt2sql-dataset
  • b-mc2/sql-create-context
• Tags:
  • stabilityai/StableBeluga-7B
  • langchain
  • opensource
  • stabilityai
  • SatbleBeluga-7B
• Language(s): English
• Pipeline Tag: Text2Text Generation

Model Usage

To get started with the model in Python, you can use the following code:

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 project with the following training and validation loss progression:

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.

Model Card Authors

• 🤖 samadpls