---
datasets:
- avinot/schema-summarization_spider
language:
- en
metrics:
- bleu
- rouge
base_model:
- google/long-t5-tglobal-base
pipeline_tag: translation
tags:
- schema-summarization
---
# Model Card for Model ID
In the Text2SQL pipeline, we often pass the schema of our relational database in order for the model and/or agent to generate proper SQL code to query our database based on the user query.
This method yielded promissing results when using small databases containing few tables with few columns. However in practice within the industry, databases may contain thousands of tables each having hundreds of columns. Hense the motivation of building this model.
This model aims to provide the minimum schema (in term of column count) in order for a Text2SQL model and/or agent to generate the appropriate SQL code. All this solely based on the initial user query.

## Model Details

### Model Description

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- **Developed by:** [Aymeric Vinot](https://huggingface.co/avinot)
- **Model type:** PEFT model from a encoder-decoder transformer architecture
- **Language(s) (NLP):** english
- **Finetuned from model:** [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base)

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## Uses

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### Direct Use

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## How to Get Started with the Model

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## Training Details

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#### Summary



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