---
language:
- en
license: mit
tags:
- text-to-sql
- t5
- wikisql
- natural-language-processing
- code-generation
datasets:
- salesforce/wikisql
pipeline_tag: text-generation
widget:
- text: "translate to SQL: What is the average score? | columns: player (text), score (real), team (text)"
  example_title: "Average Score Example"
- text: "translate to SQL: Who won the championship in 2020? | columns: year (real), champion (text), runner_up (text)"
  example_title: "Filtered Query Example"
---

# NL-SQL-T5-Small: Fine-Tuned Text-to-SQL Transformer

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) specialized for translating English natural language questions into executable SQL queries based on table schemas.

Developed as part of the **Natural Language Data Analysis Agent** project by **Solayman Sameh Solayman & Habiba Ayman Ahmed**.

---

## 📊 Model Details & Performance

- **Base Architecture:** T5-small (Encoder-Decoder Transformer, 60M parameters)
- **Training Dataset:** [WikiSQL](https://huggingface.co/datasets/salesforce/wikisql) (56,355 training queries, 8,421 validation, 15,878 test)
- **Exact Match Accuracy:** **61.8%** on the WikiSQL test split (BLEU: **0.824**)
- **Input Format:** `translate to SQL: {question} | columns: {col1} ({type1}), {col2} ({type2})`

---

## 🚀 How to Use

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
model_id = "Solayman-Sameh/nl-sql-t5-small"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

# Prepare schema-aware prompt
question = "What is the average salary of engineers?"
columns = ["name (text)", "role (text)", "salary (real)"]
prompt = f"translate to SQL: {question} | columns: {', '.join(columns)}"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Generated SQL:", sql_query)
# Output: SELECT AVG salary FROM table WHERE role = engineer
```

---

## 👥 Authors & Acknowledgments

- **Solayman Sameh Solayman** ([@Solayman-Sameh](https://huggingface.co/Solayman-Sameh))
- **Habiba Ayman Ahmed** ([@Habibaa11](https://huggingface.co/Habibaa11))

Developed as part of the **NL Data Analysis Agent** project.