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SQLCreator

Model Overview

This model is designed to generate SQL queries based on input prompts. It is based on GPT-2 and trained with custom datasets.

Usage

To use this model, follow these steps:

  1. Install the necessary libraries:
    pip install transformers
    
  2. Load the model and tokenizer:
    from transformers import AutoModelForCausalLM, AutoTokenizer
    
    model_name = “Kasivs/SQLCreator"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    
    # Example usage
    inputs = tokenizer("SELECT * FROM users WHERE", return_tensors="pt")
    outputs = model.generate(inputs["input_ids"])
    print(tokenizer.decode(outputs[0]))
    

Training

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License

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Citation

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Inference Examples
Inference API (serverless) does not yet support flair models for this pipeline type.

Dataset used to train Kasivs/SQLCreator