update examples

app.py

@@ -68,13 +68,19 @@ with app:
                 The model allows for two use cases:
                 1. *Single Search:* The input query consists of a single word. When provided a bi-, tri-, or even fourgram, the quality of the model output depends on the presence of the query token in the model's vocabulary. N-grams should be concated by an underscore (e.g., "machine_learning" or "artifical_intelligence").
                 2. *Multi Search:* The input query may consist of several words or n-grams, seperated by comma, semi-colon or newline. It then computes the average vector over all inputs and performs semantic search based on the average input token.
-
-                #### Examples
-                - transformation
-                - climate_change
-                - risk, political_risk, uncertainty
                 """
             )
+            gr.Examples(
+                examples = [
+                    ["transformation", 20],
+                    ["climate_change", 50],
+                    ["risk, political_risk, uncertainty", 250],
+                ],
+                inputs = [text_in, n],
+                outputs = [df_out, f_out, text_in],
+                fn = semantic_search,
+                cache_examples=True
+            )
         with gr.Column():
             text_in = gr.Textbox(lines=1, placeholder="Insert text", label="Search Query")
             with gr.Row():
@@ -82,13 +88,7 @@ with app:
                 compute_bt = gr.Button("Search")
             df_out = gr.Dataframe(interactive=False)
             f_out = gr.File(interactive=False, label="Download")
-    gr.Examples(
-        examples = [["transformation", 3], ["climate_change", 3], ["risk, political_risk, uncertainty", 5]],
-        inputs = [text_in, n],
-        outputs = [df_out, f_out, text_in],
-        fn = semantic_search,
-        cache_examples=True
-    )
+   
     gr.Markdown(
         """
         <p style="text-align: center;">