BART Large CNN model trained for converting NLP queries to SQL queries. 
The model was trained on the Spider dataset Link: https://yale-lily.github.io/spider

The model was trained using Google colab. 

Hyperparameters:
"num epochs" = 3
"learning rate" = 1e-5
"batch size" = 8
"weight decay" = 0.01
"max input length" = 256
"max target length" = 256
"model name" : "facebook/bart-large-cnn"



use code :


from typing import List
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("upadhyay/sql")
model = AutoModelForSeq2SeqLM.from_pretrained("upadhyay/sql")

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="what is salary?", table=["id", "name", "age"]))



{}
transformers pytorch bart text2text-generation autotrain_compatible endpoints_compatible region:us