readme: ---
  language: 
  - en
  datasets:
  - wikisql
  widget: 
  - text: "English to SQL: Show me the average age of of wines in Italy by provinces"
  - text: "English to SQL: What is the current series where the new series began in June 2011?"
  ---
  #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)
  ```
  