Update README.md

updated model card

README.md

@@ -90,21 +90,36 @@ Addressing the power of LLM in fintuned downstream task. Implemented as a person
 
 ### How to use
 
-# Load model directly
 ```python
+query_question_with_context = """sql_prompt: Which economic diversification efforts in
+the 'diversification' table have a higher budget than the average budget for all economic diversification efforts in the 'budget' table?
+sql_context: CREATE TABLE diversification (id INT, effort VARCHAR(50), budget FLOAT); CREATE TABLE
+budget (diversification_id INT, diversification_effort VARCHAR(50), amount FLOAT);"""
+```
+
 # Use a pipeline as a high-level helper
+```python
 from transformers import pipeline
 
 sql_generator = pipeline("text2text-generation", model="SwastikM/bart-large-nl2sql")
 
+sql = sql_generator(query_question_with_context)[0]['generated_text']
 
-query_question_with_context = """sql_prompt: Which economic diversification efforts in
-the 'diversification' table have a higher budget than the average budget for all economic diversification efforts in the 'budget' table?
-sql_context: CREATE TABLE diversification (id INT, effort VARCHAR(50), budget FLOAT); CREATE TABLE
-budget (diversification_id INT, diversification_effort VARCHAR(50), amount FLOAT);"""
+print(sql)
+```
 
-sql = sql_generator(query_question_with_context)[0]['generated_text']
+# Load model directly
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("SwastikM/bart-large-nl2sql")
+model = AutoModelForSeq2SeqLM.from_pretrained("SwastikM/bart-large-nl2sql")
+
+inputs = tokenizer(query_question_with_context, return_tensors="pt").input_ids
+outputs = model.generate(inputs, max_new_tokens=100, do_sample=False)
 
+sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(sql)
 ```