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---
library_name: transformers
tags:
- text-to-SQL
- SQL
- code-generation
- NLQ-to-SQL
- text2SQL
inference:
parameters:
max_length: 200
widget:
- text: |-
CREATE TABLE Loans
{
loan_id number,
client_id number,
budget real,
duration number,
interest real,
status varchar
}
CREATE TABLE Clients
{
client_id number,
first_name varchar,
last_name varchar,
email varchar,
city varchar,
year_of_birth number
}
CREATE TABLE Accounts
{
account_id number,
client_id number,
balance real,
type varchar
}
CREATE TABLE Deposits
{
deposit_id number,
account_id number,
source varchar,
amount real
}
-- Using valid SQLite, answer the following question for the tables provided above.
-- What is the duration and budget of the loan id 16342?
SELECT
example_title: Loan duration
- text: |-
CREATE TABLE Transactions
{
transaction_id number,
timestamp_id number,
primary_contract_id number,
client_id number,
beneficiary_id number,
transaction_amount real,
is_fraudulent boolean,
product_family_code varchar,
amount_currency varchar
}
CREATE TABLE Beneficiary
{
beneficiary_id number,
bank_branch_id number,
country_name varchar,
country_code varchar
}
CREATE TABLE Source
{
primary_contract_id number,
client_id number,
counterparty_bank_branch_id number,
counterparty_donor_id number
}
CREATE TABLE Time
{
timestamp_id number,
week_number number,
day_number number,
hour_number number,
day_name varchar,
year number,
month_number number
}
-- Using valid SQLite, answer the following question for the tables provided above.
-- How many transactions for the client id 15482?
SELECT
example_title: Client Transactions
datasets:
- salmane11/BanQies
language:
- en
base_model:
- bigcode/starcoderbase-1b
---
# BanQL-1B
## Model Description
BanQL is a family of Code LLMs dedicated solely for the text-to-SQL task in the Financial domain.
The checkpoint included in this repository is based on [bigcode/starcoderbase](https://huggingface.co/bigcode/starcoderbase) and further finetuned on [BanQies](https://huggingface.co/datasets/salmane11/BanQies), a dataset generated using [SelectCraft](https://github.com/ezzini/SelectCraft) compose of NLQ-SQL pairs in the financial domain.
## Finetuning Procedure
BanQL was fine-tuned using PEFT (Parameter-Efficient Fine-Tuning) techniques, specifically LoRA (Low-Rank Adaptation) adapters.
## Intended Use and Limitations
The model was designed as a use case to prove the efficiency of SelectCraft in generating large-scale good quality domain-specific text-to-SQL datasets. The model is mainly finetuned on the database schemas displayed above. The prompt format is defined below.
## How to Use
Example 1: Loans_DB
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
device="cuda"
tokenizer = AutoTokenizer.from_pretrained("salmane11/BanQL-1b")
model = AutoModelForCausalLM.from_pretrained("salmane11/BanQL-1b").to(device)
input_text = """
CREATE TABLE Loans {
loan_id number,
client_id number,
budget real,
duration number,
interest real,
status varchar
}
CREATE TABLE Clients {
client_id number,
first_name varchar,
last_name varchar,
email varchar,
city varchar,
year_of_birth number
}
CREATE TABLE Accounts {
account_id number,
client_id number,
balance real,
type varchar
}
CREATE TABLE Deposits{
deposit_id number,
account_id number,
source varchar,
amount real
}
-- Using valid SQLite, answer the following question for the tables provided above.
-- What is the duration and budget of the loan id 16342?
SELECT"""
encoding = tokenizer.encode_plus(input_text, return_tensors="pt").to(device)
input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=512,
do_sample=True,
top_k=120,
top_p=0.95,
early_stopping=True,
)
line = tokenizer.decode(outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)
query_begining = line.find("SELECT")
print(line[query_begining:])
```
Example 2: Transactions_DB
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
device="cuda"
tokenizer = AutoTokenizer.from_pretrained("salmane11/BanQL-1b")
model = AutoModelForCausalLM.from_pretrained("salmane11/BanQL-1b").to(device)
input_text = """
CREATE TABLE Transactions {
transaction_id number,
timestamp_id number,
primary_contract_id number,
client_id number,
beneficiary_id number,
transaction_amount real,
is_fraudulent boolean,
product_family_code varchar,
amount_currency varchar
}
CREATE TABLE Beneficiary {
beneficiary_id number,
bank_branch_id number,
country_name varchar,
country_code varchar,
}
CREATE TABLE Source {
primary_contract_id number,
client_id number,
counterparty_bank_branch_id number,
counterparty_donor_id number,
}
CREATE TABLE Time{
timestamp_id number,
week_number number,
day_number number,
hour_number number,
day_name varchar,
year number,
month_number number
}
-- Using valid SQLite, answer the following question for the tables provided above.
-- How many transactions for the client id 15482?
SELECT"""
encoding = tokenizer.encode_plus(input_text, return_tensors="pt").to(device)
input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=512,
do_sample=True,
top_k=120,
top_p=0.95,
early_stopping=True,
)
line = tokenizer.decode(outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)
query_begining = line.find("SELECT")
print(line[query_begining:])
```
## Cite our work
Citation |