README.md

---
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](https://www.linkedin.com/company/pipable.ai/about/)

[colab_notebook](https://colab.research.google.com/drive/1insSxvc3jjAXe0zmdIjmbG3ttb5mpRgQ?usp=sharing)

## 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.
Loss behaviour in the set up mentioned above - 

![image/png](https://cdn-uploads.huggingface.co/production/uploads/658d8095a2a6a6e0da8bb8a6/I80Ru1r4thoYrLagIWALa.png)

## 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](https://github.com/taoyds/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](https://github.com/defog-ai/sql-eval)
These are the results -

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d32c6b921678fdc9de3302/a7Hd1AiwV2NIKmeABdLJm.png)

## License
The model is open source under apache 2.0. License

## Usage

### Installation

```bash
pip install transformers
```

### Prompt
```python
prompt = f"""<schema>{schema}</schema>
<question>{question}</question>
<sql>"""
```

### PyTorch
```python
from transformers import AutoModelForCausalLM, 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
```python
from transformers 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="jax")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
```

### TensorFlow
```python
from transformers 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="tf")
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
```sql
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?
```sql
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?
```sql
SELECT product_price , product_size FROM products WHERE product_price > (SELECT avg(product_price) FROM products)
```

What is the most uncommon order status?
```sql
SELECT order_status_code FROM orders GROUP BY order_status_code ORDER BY count(*) ASC LIMIT 1;
```

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