Update README.md

README.md

@@ -20,13 +20,35 @@ pip install torch==2.2.2
 
 ## Usage
 
-*Input Format:*
+Please exercise caution when formatting the input.
 
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("alpecevit/flan-t5-base-text2sql")
+model = AutoModelForSeq2SeqLM.from_pretrained("alpecevit/flan-t5-base-text2sql")
+
+input_text = """
+transform question and schema to SQL query. question: Who are the top 5 most paid employess by first name, last name, and salary ? schema: employee(salary, bdate, dno, ssn, fname, sex, superssn, address, minit, lname), department(dnumber, mgrstartdate, dname, mgrssn), dept_locations(dnumber, dlocation), project(pnumber, dnum, pname, plocation), works_on(pno, hours, essn), dependent(bdate, essn, dependent_name, sex, relationship).
+"""
+
+token_input = tokenizer(input_text, return_tensors="pt").input_ids
+output = model.generate(token_input, max_new_tokens=128)
+query = tokenizer.decode(output[0], skip_special_tokens=True)
+
+print("Predicted Query:", query)
 ```
-transform question and schema to SQL query. question: Who are the top 5 most paid employess by first name, last name, and salary ?
-schema: employee(salary, bdate, dno, ssn, fname, sex, superssn, address, minit, lname), department(dnumber, mgrstartdate, dname, mgrssn), dept_locations(dnumber, dlocation), project(pnumber, dnum, pname, plocation), works_on(pno, hours, essn), dependent(bdate, essn, dependent_name, sex, relationship).
+
+*Output:*
+
+```
+SELECT fname, lname, salary FROM employee ORDER BY salary DESC LIMIT 5
 ```
 
-```python
+## Evaluation
 
+The fine-tuned model was evaluated using the combination of test splits of the above datasets. [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge) metrics were utilized for the assessment, and the results are outlined below.
+
+```
+{'rouge1': 0.8740305983060861, 'rouge2': 0.7763397400315798, 'rougeL': 0.8449832130213266, 'rougeLsum': 0.8447120646910007}
 ```