README.md

---
language: en
widget:
- text: >-
    convert question and table into SQL query. tables: people_name(id,name),
    people_age(people_id,age). question: how many people with name jui and age
    less than 25
license: cc-by-sa-4.0
pipeline_tag: text2text-generation
inference:
  parameters:
    max_length: 512
    num_beams: 10
    top_k: 10
---

This is an upgraded version of [https://huggingface.co./juierror/flan-t5-text2sql-with-schema](https://huggingface.co./juierror/flan-t5-text2sql-with-schema). 

It supports the '<' sign and can handle multiple tables.

# How to use
```python
from typing import List
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("juierror/flan-t5-text2sql-with-schema-v2")
model = AutoModelForSeq2SeqLM.from_pretrained("juierror/flan-t5-text2sql-with-schema-v2")

def get_prompt(tables, question):
    prompt = f"""convert question and table into SQL query. tables: {tables}. question: {question}"""
    return prompt

def prepare_input(question: str, tables: Dict[str, List[str]]):
    tables = [f"""{table_name}({",".join(tables[table_name])})""" for table_name in tables]
    tables = ", ".join(tables)
    prompt = get_prompt(tables, question)
    input_ids = tokenizer(prompt, max_length=512, return_tensors="pt").input_ids
    return input_ids

def inference(question: str, tables: Dict[str, List[str]]) -> str:
    input_data = prepare_input(question=question, tables=tables)
    input_data = input_data.to(model.device)
    outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=512)
    result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
    return result

print(inference("how many people with name jui and age less than 25", {
    "people_name": ["id", "name"],
    "people_age": ["people_id", "age"]
}))

print(inference("what is id with name jui and age less than 25", {
    "people_name": ["id", "name", "age"]
})))
```

# Dataset
- [CoSQL](https://yale-lily.github.io/cosql)
- [spider](https://yale-lily.github.io/spider)
- [SParC](https://yale-lily.github.io/sparc)