README.md

---
license: cc-by-nc-sa-4.0
language:
- en
library_name: transformers
tags:
- text-to-sql
- text2sql
- nlp2sql
- nlp-to-sql
- SQL
---
# Model Card for text2sql

<!-- Provide a quick summary of what the model is/does. -->

LLM instruction finetuned for Text-to-SQL task. 


## How to Get Started with the Model

Use the code below to get started with the model.

```
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
                                "dataeaze/dataeaze-text2sql-codellama_7b_instruct-clinton_text_to_sql_v1", 
                                torch_dtype=torch.bfloat16,
                                device_map='auto'
                                )

tokenizer = AutoTokenizer.from_pretrained("dataeaze/dataeaze-text2sql-codellama_7b_instruct-clinton_text_to_sql_v1")
# print("model device :", model.device)
tokenizer.pad_token = tokenizer.eos_token
model.eval()

prompt = """ Below are sql tables schemas paired with instruction that describes a task. 
Using valid SQLite, write a response that appropriately completes the request for the provided tables. 
### Instruction: How many transactions were made by a customer in a specific month? 
### Database: RewardsProgramDB61 
### Input: 
CREATE SCHEMA RewardsProgram;

CREATE TABLE Customer (
    CustomerID INT NOT NULL AUTO_INCREMENT,
    FirstName VARCHAR(50) NOT NULL,
    LastName VARCHAR(50) NOT NULL,
    Email VARCHAR(100) UNIQUE NOT NULL,
    Phone VARCHAR(20) UNIQUE,
    DateOfBirth DATE,
    PRIMARY KEY (CustomerID)
);

CREATE TABLE Membership (
    MembershipID INT NOT NULL AUTO_INCREMENT,
    MembershipType VARCHAR(50) NOT NULL,
    DiscountPercentage DECIMAL(5, 2) NOT NULL,
    ValidFrom DATETIME,
    ValidTo DATETIME,
    CustomerID INT NOT NULL,
    PRIMARY KEY (MembershipID),
    FOREIGN KEY (CustomerID) REFERENCES Customer(CustomerID)
);

CREATE TABLE Transaction (
    TransactionID INT NOT NULL AUTO_INCREMENT,
    TransactionDate TIMESTAMP,
    TotalAmount DECIMAL(10, 2) NOT NULL,
    CustomerID INT NOT NULL,
    PRIMARY KEY (TransactionID),
    FOREIGN KEY (CustomerID) REFERENCES Customer(CustomerID)
);

CREATE TABLE TransactionDetail (
    TransactionDetailID INT NOT NULL AUTO_INCREMENT,
    TransactionID INT NOT NULL,
    ProductID INT NOT NULL,
    Quantity INT NOT NULL,
    UnitPrice DECIMAL(10, 2) NOT NULL,
    PRIMARY KEY (TransactionDetailID),
    FOREIGN KEY (TransactionID) REFERENCES Transaction(TransactionID),
    FOREIGN KEY (ProductID) REFERENCES Product(ProductID)
);

CREATE TABLE Product (
    ProductID INT NOT NULL AUTO_INCREMENT,
    ProductName VARCHAR(100) NOT NULL,
    UnitPrice DECIMAL(10, 2) NOT NULL,
    AvailableQuantity INT NOT NULL,
    CreatedDate DATETIME,
    PRIMARY KEY (ProductID)
);

ALTER TABLE Membership ADD CONSTRAINT FK_Membership_Customer FOREIGN KEY (CustomerID) REFERENCES Customer(CustomerID);

ALTER TABLE TransactionDetail ADD CONSTRAINT FK_TransactionDetail_Transaction FOREIGN KEY (TransactionID) REFERENCES Transaction(TransactionID);

ALTER TABLE TransactionDetail ADD CONSTRAINT FK_TransactionDetail_Product FOREIGN KEY (ProductID) REFERENCES Product(ProductID);"
"""

input_ids = tokenizer(prompt, padding=True, return_tensors='pt')
outputs = model.generate(
    input_ids=input_ids['input_ids'].to(model.device),
    attention_mask=input_ids['attention_mask'].to(model.device),
    max_new_tokens=3072,
)

generated_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_query)


```



### Results

```
model-index:
  - name: dataeaze/dataeaze-text2sql-codellama_7b_instruct-dzsql
    results:
    - task:
        type: text-to-sql
      dataset:
        name: SPIDER 1.0
        type: text-to-sql
      metrics:
        - name: Execution with Values
          type: Execution with Values
          value: 64.3
        - name: Exact Set Match without Values
          type: Exact Set Match without Values
          value: 29.6
      source:
        name: Spider 1.0 - Leaderboard
        url: https://yale-lily.github.io/spider
```