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--- |
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license: apache-2.0 |
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datasets: |
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- samadpls/querypls-prompt2sql-dataset |
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- b-mc2/sql-create-context |
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tags: |
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- stabilityai/StableBeluga-7B |
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- langchain |
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- opensource |
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- stabilityai |
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- SatbleBeluga-7B |
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language: |
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- en |
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pipeline_tag: text2text-generation |
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--- |
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<img src='https://cdn-uploads.huggingface.co/production/uploads/648dd721b91c3ead953a5ae0/zUj6oxW4WHXQjFHYhTduY.png' align='right'> |
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# 🛢💬 Querypls-Prompt2SQL |
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## Overview |
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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. |
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## Model Usage |
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To get started with the model in Python, you can use the following code: |
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```python |
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from transformers import pipeline, AutoTokenizer |
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question = "how to get all employees from table0" |
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prompt = f'Your task is to create SQL query of the following {question}, just SQL query and no text' |
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tokenizer = AutoTokenizer.from_pretrained("samadpls/querypls-prompt2sql") |
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pipe = pipeline(task='text-generation', model="samadpls/querypls-prompt2sql", tokenizer=tokenizer, max_length=200) |
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result = pipe(prompt) |
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print(result[0]['generated_text']) |
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``` |
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Adjust the `question` variable with the desired question, and the generated SQL query will be printed. |
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## Training Details |
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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: |
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```yaml |
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Copy code |
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Step Training Loss Validation Loss |
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943 2.332100 2.652054 |
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1886 2.895300 2.551685 |
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2829 2.427800 2.498556 |
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3772 2.019600 2.472013 |
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4715 3.391200 2.465390 |
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``` |
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`However, note that the model may be too large to load in certain environments.` |
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For more information and details, please refer to the provided [documentation](https://huggingface.co/stabilityai/StableBeluga-7B). |
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## Model Card Authors |
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- 🤖 [samadpls](https://github.com/samadpls) |
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