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--- |
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library_name: transformers |
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tags: |
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- text-to-SQL |
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- SQL |
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- code-generation |
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- NLQ-to-SQL |
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- text2SQL |
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datasets: |
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- salmane11/BanQies |
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language: |
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- en |
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base_model: |
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- bigcode/starcoder2-3b |
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--- |
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# BanQL-3B |
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## Model Description |
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BanQL is a family of Code LLMs dedicated solely for the text-to-SQL task in the Financial domain. |
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The checkpoint included in this repository is based on [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) and further finetuned on [BanQies](https://huggingface.co/datasets/salmane11/BanQies), a dataset generated using [SelectCraft](https://github.com/ezzini/SelectCraft) composed of NLQ-SQL pairs from the financial domain. |
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## Finetuning Procedure |
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BanQL was fine-tuned using PEFT (Parameter-Efficient Fine-Tuning) techniques, specifically LoRA (Low-Rank Adaptation) adapters. |
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## Intended Use and Limitations |
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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. |
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## How to Use |
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Example 1: Loans_DB |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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device="cuda" |
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tokenizer = AutoTokenizer.from_pretrained("salmane11/BanQL-3b") |
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model = AutoModelForCausalLM.from_pretrained("salmane11/BanQL-3b").to(device) |
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input_text = """ |
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CREATE TABLE Loans { |
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loan_id number, |
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client_id number, |
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budget real, |
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duration number, |
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interest real, |
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status varchar |
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} |
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CREATE TABLE Clients { |
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client_id number, |
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first_name varchar, |
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last_name varchar, |
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email varchar, |
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city varchar, |
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year_of_birth number |
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} |
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CREATE TABLE Accounts { |
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account_id number, |
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client_id number, |
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balance real, |
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type varchar |
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} |
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CREATE TABLE Deposits{ |
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deposit_id number, |
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account_id number, |
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source varchar, |
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amount real |
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} |
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-- Using valid SQLite, answer the following question for the tables provided above. |
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-- What is the duration and budget of the loan id 16342? |
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SELECT""" |
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encoding = tokenizer.encode_plus(input_text,pad_to_max_length=True, return_tensors="pt").to(device) |
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input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device) |
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outputs = model.generate( |
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input_ids=input_ids, attention_mask=attention_masks, |
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max_length=512, |
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do_sample=True, |
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top_k=120, |
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top_p=0.95, |
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early_stopping=True, |
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) |
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line = tokenizer.decode(outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True) |
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query_begining = line.find("SELECT") |
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print(line[query_begining:]) |
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``` |
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Example 2: Transactions_DB |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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device="cuda" |
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tokenizer = AutoTokenizer.from_pretrained("salmane11/BanQL-3b") |
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model = AutoModelForCausalLM.from_pretrained("salmane11/BanQL-3b").to(device) |
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input_text = """ |
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CREATE TABLE Transactions { |
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transaction_id number, |
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timestamp_id number, |
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primary_contract_id number, |
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client_id number, |
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beneficiary_id number, |
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transaction_amount real, |
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is_fraudulent boolean, |
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product_family_code varchar, |
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amount_currency varchar |
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} |
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CREATE TABLE Beneficiary { |
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beneficiary_id number, |
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bank_branch_id number, |
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country_name varchar, |
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country_code varchar, |
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} |
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CREATE TABLE Source { |
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primary_contract_id number, |
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client_id number, |
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counterparty_bank_branch_id number, |
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counterparty_donor_id number, |
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} |
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CREATE TABLE Time{ |
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timestamp_id number, |
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week_number number, |
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day_number number, |
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hour_number number, |
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day_name varchar, |
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year number, |
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month_number number |
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} |
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-- Using valid SQLite, answer the following question for the tables provided above. |
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-- How many transactions for the client id 15482? |
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SELECT""" |
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encoding = tokenizer.encode_plus(input_text,pad_to_max_length=True, return_tensors="pt").to(device) |
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input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device) |
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outputs = model.generate( |
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input_ids=input_ids, attention_mask=attention_masks, |
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max_length=512, |
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do_sample=True, |
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top_k=120, |
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top_p=0.95, |
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early_stopping=True, |
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) |
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line = tokenizer.decode(outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True) |
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query_begining = line.find("SELECT") |
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print(line[query_begining:]) |
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``` |
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## Cite our work |
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Citation |
