Update README.md

README.md

@@ -18,17 +18,14 @@ metrics:
 library_name: transformers
 ---
 
-Industry standard text to sql generation with high accuracy. 
+# Industry standard text to sql generation with high accuracy. 
 
-sample code to start with:
+Sample code to begin with:
 
 import torch
 from transformers import T5Tokenizer, T5ForConditionalGeneration
-
-# Initialize the tokenizer from Hugging Face Transformers library
 tokenizer = T5Tokenizer.from_pretrained('anilajax/text2sql_industry_standard')
 
-# Load the model
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model = T5ForConditionalGeneration.from_pretrained('anilajax/text2sql_industry_standard')
 model = model.to(device)
@@ -53,5 +50,5 @@ 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
+#expected output - SELECT COUNT(*) FROM students WHERE class = 10