use better model for NER

app.py

@@ -11,7 +11,7 @@ sentence = choice(sentences)
 st.title("Semantic Frame Augmentation")
 st.subheader("Analysing difficult low-resource domains with only a handful of examples")
 
-st.write("This space uses a google/mobilebert-uncased model for NER")
+st.write("This space uses a xlnet-base-cased model for NER")
 augment = st.toggle('Use augmented model for NER', value=False)
 
 txt = st.text_area(

helpers.py

@@ -1,11 +1,11 @@
 from transformers import pipeline
 
 baseline_classifier = pipeline("ner",
-    model="Dagobert42/biored-finetuned",
+    model="Dagobert42/xlnet-base-cased-biored-augmented",
     aggregation_strategy="simple"
     )
 augmented_classifier = pipeline("ner",
-    model="Dagobert42/biored-augmented",
+    model="Dagobert42/xlnet-base-cased-biored-finetuned",
     aggregation_strategy="simple"
     )