Model Card for text2sql
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
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