Update README.md

README.md

@@ -15,4 +15,43 @@ tags:
 metrics:
 - accuracy
 - character
----
+library_name: transformers
+---
+
+Industry standard text to sql generation with high accuracy. 
+
+sample code to start 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)
+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
+