metadata

license: apache-2.0
datasets:
  - PipableAI/pip-txt-to-sql-spider-bird-dataset
language:
  - en
metrics:
  - accuracy
tags:
  - sql
  - code
  - text2sql
  - instruction_tuned
  - basemodel
  - jax
  - pytorch
  - tensorflow
  - text-generation-inference
library_name: transformers
pipeline_tag: text-generation
widget:
  - text: >-
      <schema>CREATE TABLE system(JobID: String,GID: String, UID: String,
      Start:Time(yyyy/mm/dd), End: Time,ElapsedRaw: Time, CPUTimeRAW:
      Time,NCPUS: Number,NNodes: Number, NodeList: List,  State:String,
      Timelimit: Time);</schema><question>Get UID and job id for Jobs that
      started on Jan 20 , 2023 ended on feb 14 2023 and has job id
      20</question><sql>
    example_title: example

pipSQL-1.3b

pipableAi

What have we built?

A 1.3 bn SQL model that outperforms most SQL expert models and chatgpt on popular benchmarks. This is a distilled model built on the deepseek base model.

How we built it?

We used softmax cross entropy and a modified form of policy grad along with Q loss, optimized in an EM set up.

Benchmarking :

For benchmarking purposes we are using Semantic Evaluation for Text-to-SQL with Distilled Test Suites, an officially accepted evaluation framework for Spider, SParC, and CoSQL which was proposed by a research team of Yale and Berkeley. The benchmark contains 2200 test data points Here is the link to run the evaluation:

Test Suite SQL Eval

model	easy	medium	hard	extra
sqlcoder-7b-2	72.0	58.0	40.6	37.3
pipSQL-1.3b	71.1	49.9	31.5	24.1
pipSQL-7b	63.0	40.0	30.2	25.0
sqlcoder-7b	60.6	48.2	28.3	20.4
gpt-3.5	58.8	44.7	31.0	28.4

We have also benchmarked it on defog eval. It contains 200 test data points handpicked by defog team. Here is the link to it:

Defog SQL-Eval These are the results -

License

The model is open source under apache 2.0. License

Usage

Installation

pip install transformers

Prompt

prompt = f"""<schema>{schema}</schema>
<question>{question}</question>
<sql>"""

PyTorch

from transformers import AutoModelForCasualLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")

inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])

Flax

from transfomers import FlaxAutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = FlaxAutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")

inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])

TensorFlow

from transfomers import TFAutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = TFAutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")

inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])

Examples

Schema

CREATE TABLE Products (
  product_id number,
  parent_product_id number,
  product_name text,
  product_price number,
  product_color text,
  product_size text,
  product_description text);

CREATE TABLE Customers (
  customer_id number,
  gender_code text,
  customer_first_name text,
  customer_middle_initial text,
  customer_last_name text,
  email_address text,
  login_name text,
  login_password text,
  phone_number text,
  address_line_1 text,
  town_city text,
  county text,
  country text);

CREATE TABLE Customer_Payment_Methods (
  customer_id number,
  payment_method_code text);

CREATE TABLE Invoices (
  invoice_number number,
  invoice_status_code text,
  invoice_date time);

CREATE TABLE Orders (
  order_id number,
  customer_id number,
  order_status_code text,
  date_order_placed time);

CREATE TABLE Order_Items (
  order_item_id number,
  product_id number,
  order_id number,
  order_item_status_code text);

CREATE TABLE Shipments (
  shipment_id number,
  order_id number,
  invoice_number number,
  shipment_tracking_number text,
  shipment_date time);

CREATE TABLE Shipment_Items (
  shipment_id number,
  order_item_id number);

Questions

What is the most popular payment method?

SELECT payment_method_code FROM Customer_Payment_Methods GROUP BY payment_method_code ORDER BY count(*) DESC LIMIT 1;

What are the product price and the product size of the products whose price is above average?

SELECT product_price ,  product_size FROM products WHERE product_price  > (SELECT avg(product_price) FROM products)

What is the most uncommon order status?

SELECT order_status_code FROM orders GROUP BY order_status_code ORDER BY count(*) ASC LIMIT 1;

###Team Avi Kothari,Gyan Ranhan, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya