Update README.md

README.md

@@ -10,6 +10,7 @@ tags:
 - sql
 datasets:
 - b-mc2/sql-create-context
+license: other
 ---
 # Model Card
 
@@ -18,23 +19,50 @@ datasets:
 This is my first fine tuned LLM project.
 
 
-## Prompt
+## Usage
 
-query = List the creation year, name and budget of each department
+```
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-f"Translate the following English question to SQL: {query}
+finetunedGPT = GPT2LMHeadModel.from_pretrained("rakeshkiriyath/gpt2Medium_text_to_sql")
+finetunedTokenizer = GPT2Tokenizer.from_pretrained("rakeshkiriyath/gpt2Medium_text_to_sql")
 
-## Output
+def generate_text_to_sql(query, model, tokenizer, max_length=256):
+    prompt = f"Translate the following English question to SQL: {query}"
 
-SELECT creation_year, name, budget FROM department
+    input_tensor = tokenizer.encode(prompt, return_tensors='pt').to('cuda')
+
+    output = model.generate(input_tensor, max_length=max_length, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
+
+    decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    # Return only the SQL part (removing the input text)
+    sql_output = decoded_output[len(prompt):].strip()
+
+    return sql_output
+
+queryList = ["I need a list of employees who joined in the company last 6 months with a salary hike of 30% ",
+             "Give me loginid,status,company of a user who is mapped to the organization XYZ "]
+
+for query in queryList:
+
+  sql_result = generate_text_to_sql(query, finetunedGPT, finetunedTokenizer)
+  print(sql_result,"\n")
+
+```
+
+### Output
+
+SELECT COUNT(*) FROM employees WHERE last_6_months = "6 months" AND salary_hike = "30%" \
+SELECT loginid,status,company FROM user_mapped_to_organization WHERE mapping = "XYZ" 
 
 
 #### Training Hyperparameters
 
-num_train_epochs=1
-per_device_train_batch_size=3
-gradient_accumulation_steps=9
-learning_rate=5e-5
+num_train_epochs=1 \
+per_device_train_batch_size=3 \
+gradient_accumulation_steps=9 \
+learning_rate=5e-5 \
 weight_decay=0.01