Update README.md

README.md

@@ -142,6 +142,9 @@ def get_final_answer(model, tokenizer, inputs, table_df):
 _, answer, _ = get_final_answer(model, tokenizer, inputs, table_df)
 print(answer)
 ```
+If the question is asking for a count, like how many changes have been completed, the answer would just be one number.
+If it is asking a question about the most common incident status or root cause, the answer would be the root cause or status the model
+predicts. 
 
 ## Limitations
 The model does still not come close to a 100% accuracy. Possiblly using a larger model could help. It does seem to only be able to take in a limited size for tables, larger than a whole system. Once again, possibly a larger model could help. Also this needs to take in a question and table in dataframe format, so more preocessing is necessary than just a regular prompt.