| --- |
| language: en |
| datasets: |
| - wikisql |
| widget: |
| - text: "question: get people name with age equal 25 table: id, name, age" |
| --- |
| There are an upgraded version that support multiple tables and support "<" sign using Flan-T5 as a based model [here](https://huggingface.co/juierror/flan-t5-text2sql-with-schema-v2). |
|
|
| # How to use |
| ```python |
| from typing import List |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| |
| tokenizer = AutoTokenizer.from_pretrained("juierror/text-to-sql-with-table-schema") |
| model = AutoModelForSeq2SeqLM.from_pretrained("juierror/text-to-sql-with-table-schema") |
| |
| def prepare_input(question: str, table: List[str]): |
| table_prefix = "table:" |
| question_prefix = "question:" |
| join_table = ",".join(table) |
| inputs = f"{question_prefix} {question} {table_prefix} {join_table}" |
| input_ids = tokenizer(inputs, max_length=700, return_tensors="pt").input_ids |
| return input_ids |
| |
| def inference(question: str, table: List[str]) -> str: |
| input_data = prepare_input(question=question, table=table) |
| input_data = input_data.to(model.device) |
| outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=700) |
| result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True) |
| return result |
| |
| print(inference(question="get people name with age equal 25", table=["id", "name", "age"])) |
| ``` |