dsivakumar/text2sql

#import transformers

from transformers import (
    T5ForConditionalGeneration,
    T5Tokenizer,
)

#load model

model = T5ForConditionalGeneration.from_pretrained('dsivakumar/text2sql')
tokenizer = T5Tokenizer.from_pretrained('dsivakumar/text2sql')

#predict function

def get_sql(query,tokenizer,model):
    source_text= "English to SQL: "+query
    source_text = ' '.join(source_text.split())
    source = tokenizer.batch_encode_plus([source_text],max_length= 128, pad_to_max_length=True, truncation=True, padding="max_length", return_tensors='pt')
    source_ids = source['input_ids'] #.squeeze()
    source_mask = source['attention_mask']#.squeeze()
    generated_ids = model.generate(
      input_ids = source_ids.to(dtype=torch.long),
      attention_mask = source_mask.to(dtype=torch.long), 
      max_length=150, 
      num_beams=2,
      repetition_penalty=2.5, 
      length_penalty=1.0, 
      early_stopping=True
      )
    preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
    return preds
 
#test

query="Show me the average age of of wines in Italy by provinces"
sql = get_sql(query,tokenizer,model)
print(sql)

#https://huggingface.co/mrm8488/t5-small-finetuned-wikiSQL
def get_sql(query):
  input_text = "translate English to SQL: %s </s>" % query
  features = tokenizer([input_text], return_tensors='pt')

  output = model.generate(input_ids=features['input_ids'], 
               attention_mask=features['attention_mask'])
  
  return tokenizer.decode(output[0])

query = "How many models were finetuned using BERT as base model?"

get_sql(query)