Update app.py

app.py

@@ -17,7 +17,11 @@ model = model.to(device)
 examples = [
     [
         "audio/672-122797-0026.wav",
-        "monetary-value, biological-classification, desire, demographic-group, object-category, relationship-role, reflexive-pronoun, furniture-type"
+        "biological-classification, desire, demographic-group, object-category, relationship-role, reflexive-pronoun, furniture-type"
+    ],
+    [
+        "audio/7021-85628-0025.wav",
+        "action-goal, person's-title, emotional-connection, personal-qualities, pronoun-target, assignmentaction, physical-action, family-role"
     ],
     [
         "audio/672-122797-0024.wav",
@@ -30,10 +34,6 @@ examples = [
     [
         "audio/672-122797-0048.wav",
         "weapon, emotional-state, household-chore, atmosphere-quality"
-    ],
-    [
-        "audio/7021-85628-0025.wav",
-        "action-goal, person's-title, emotional-connection, personal-qualities, pronoun-target, assignmentaction, physical-action, family-role"
     ]
 ]
 
@@ -126,6 +126,7 @@ with gr.Blocks(title="WhisperNER v1") as demo:
         # Whisper-NER: ASR with zero-shot NER
 
         WhisperNER is a unified model for automatic speech recognition (ASR) and named entity recognition (NER), with zero-shot capabilities.
+        The WhisperNER model is designed as a strong base model for the downstream task of ASR with NER, and can be fine-tuned on specific datasets for improved performance.
 
         ## Links