README.md

---
library_name: transformers
tags: []
---

# Model Card for Model ID

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Fine Tuning using Spider

Some passing the schema with examples and some, if no longer than 2048 tokens, with sql examples, like:
Below are some sample questions and their corresponding SQL queries that might help you answer the user's question:

[QUESTION]What is the number of cars with a greater accelerate than the one with the most horsepower?[/QUESTION]

[SQL]SELECT COUNT(*)
FROM CARS_DATA
WHERE Accelerate >
    (SELECT Accelerate
     FROM CARS_DATA
     ORDER BY Horsepower DESC
     LIMIT 1);[/SQL]

#<|ddl|>
The query will run on a database with the following schema:

CREATE TABLE car_makers (
	"Id" INTEGER, 
	"Maker" TEXT, 
	"FullName" TEXT, 
	"Country" TEXT, 
	PRIMARY KEY ("Id"), 
	FOREIGN KEY("Country") REFERENCES countries ("CountryId")
)

/*
2 rows from car_makers table:
Id	Maker	FullName	Country
1	amc	American Motor Company	1
2	volkswagen	Volkswagen	2
*/


Max sequence length: 2048

After filtering the dataset, there are 7665 rows remaining

Prompt:
```
"""# <|system|>
You are a chatbot tasked with responding to questions about an SQLite database.
Your responses must always consist of valid SQL code, and only that.
If you are unable to generate SQL for a question, respond with 'I do not know'.{sql_example}

# <|ddl|>
The query will run on a database with the following schema:
{ddl}

# <|user|>
{question}

# <|assistant|>
[SQL]"""
```



## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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### Model Sources [optional]

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## Uses

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### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Glossary [optional]

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