anilajax/text2sql_industry_standard

Industry standard text to sql generation with high accuracy.

Sample code to begin with:

import torch from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained('anilajax/text2sql_industry_standard')

device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = T5ForConditionalGeneration.from_pretrained('anilajax/text2sql_industry_standard') model = model.to(device) model.eval()

def generate_sql(input_prompt): # Tokenize the input prompt inputs = tokenizer(input_prompt, padding=True, truncation=True, return_tensors="pt").to(device)

# Forward pass
with torch.no_grad():
    outputs = model.generate(**inputs, max_length=512)

# Decode the output IDs to a string (SQL query in this case)
generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)

return generated_sql

input_prompt = "provide count of students where class = 10"

generated_sql = generate_sql(input_prompt)

print(f"The generated SQL query is: {generated_sql}") #expected output - SELECT COUNT(*) FROM students WHERE class = 10