fine tune LLM

app.py

@@ -72,7 +72,7 @@ https://www.kaggle.com/datasets/trainingdatapro/20000-customers-reviews-on-banks
                             label="Issue",
                             info="issue you want to explore about"
                             )
-    out_similar = gr.JSON()
+    out_similar = gr.JSON(label="Similar Verbatim")
 
     btn_similar = gr.Button("Find Similar Verbatim")
     btn_similar.click(fn=similar, inputs=in_similar, outputs=out_similar)
@@ -119,12 +119,13 @@ Using Sentence Embedding to inject Public ML Banks Text Dataset @ https://github
     in_like = gr.Textbox(placeholder="having credit card problem"   ,                                             label="Issue",
                             info="issue you want to explore about")
     out_like = gr.Textbox(placeholder="like score in range [2 to 248] from fine tuning data",
+                          label="like score",
                           info="like score")
 
-    btn_like = gr.Button("Find Like Score")
+    btn_like = gr.Button("Classify Like Score")
     btn_like.click(fn=like, inputs=in_like, outputs=out_like)
     gr.Markdown("""
-As a Data Scientist with a decades of financial industry experience, I recognize the paramount importance of staying closely tuned to our customer's needs and opinions. In this use case statement, we outline how fine-tuning a Language Model (LLM) on a custom dataset can provide valuable insights into customer sentiment across crucial areas such as service, sales, point of failure, product, and emerging trends.
+As a Data Scientist with a decades of financial industry experience, I recognize the paramount importance of staying closely tuned to our customer's needs and opinions. In this app, Fine Tune LLM, we have shown how fine-tuning a Language Model (LLM) on a custom dataset can provide valuable insights into customer sentiment across crucial areas such as service, sales, point of failure, product, and emerging trends.
 
 Objective:
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