metadata
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)