---
language: en
pipeline_tag: text2text-generation
tags:
- text-to-sql
- t5
- natural-language-processing
- sql
license: apache-2.0
datasets:
- gretelai/synthetic_text_to_sql
base_model:
- Salesforce/codet5-base
---

# Text-to-SQL T5 Model (`16pramodh/t2s_model`)

## Model Description
This is a **T5-based text-to-SQL model** trained to convert **natural language questions** into **SQL queries**.  
It works by taking in:

natural language query [SEP] table schema

and producing a SQL statement based on the provided database schema.

The model is based on `T5ForConditionalGeneration` and supports **text2text-generation** via the Hugging Face Inference API.

---

## Intended Use
- **Input:** English natural language question **plus** the database schema.
- **Output:** SQL query that can be executed on the described database.

---

## Example

**Input:**
Get the names and emails of all customers who signed up after January 1, 2024 [SEP] CREATE TABLE customers (customer_id INT PRIMARY KEY, name VARCHAR(50), email VARCHAR(100), signup_date DATE);

**Output:**
SELECT name, email FROM customers WHERE signup_date > '2024-01-01';

---

## How to Use

### Hugging Face Inference API
```bash
curl -X POST \
  -H "Authorization: Bearer YOUR_HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"inputs": "Get the names and emails of all customers who signed up after January 1, 2024 [SEP] CREATE TABLE customers (customer_id INT PRIMARY KEY, name VARCHAR(50), email VARCHAR(100), signup_date DATE);"}' \
  https://api-inference.huggingface.co/models/16pramodh/t2s_model
```

### Python (Transformers)
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "16pramodh/t2s_model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

input_text = "Get the names and emails of all customers who signed up after January 1, 2024 [SEP] CREATE TABLE customers (customer_id INT PRIMARY KEY, name VARCHAR(50), email VARCHAR(100), signup_date DATE);"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---