| | --- |
| | 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"])) |
| | ``` |