Instructions to use juierror/flan-t5-text2sql-with-schema with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juierror/flan-t5-text2sql-with-schema with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("juierror/flan-t5-text2sql-with-schema") model = AutoModelForSeq2SeqLM.from_pretrained("juierror/flan-t5-text2sql-with-schema", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from juierror/flan-t5-text2sql-with-schema: direct link, hf CLI and curl.
- Browser
- Download file 1.35 kB
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https://huggingface.co/juierror/flan-t5-text2sql-with-schema/resolve/refs%2Fpr%2F15/README.md
- Command line
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hf download hf://juierror/flan-t5-text2sql-with-schema@refs/pr/15/README.md
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curl -L -o README.md https://huggingface.co/juierror/flan-t5-text2sql-with-schema/resolve/refs%2Fpr%2F15/README.md
1.35 kB
metadata
language: en
datasets:
- wikisql
widget:
- text: 'question: get people name with age equal 25 table: id, name, age'
license: apache-2.0
There are an upgraded version that support multiple tables and support "<" sign here.
How to use
from typing import List
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("juierror/flan-t5-text2sql-with-schema")
model = AutoModelForSeq2SeqLM.from_pretrained("juierror/flan-t5-text2sql-with-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=512, 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"]))