checking pipeline output

README.md

@@ -1,6 +1,6 @@
 ---
 title: Semantic Frame Augmentation
-emoji: 🚀
+emoji: 🔬
 colorFrom: gray
 colorTo: yellow
 sdk: streamlit

app.py

@@ -5,6 +5,7 @@ from random import choice
 
 with open("sentences.pt", 'rb') as f:
     sentences = torch.load(f)
+sentence = choice(sentences)
 
 baseline_classifier = pipeline(
     model="Dagobert42/mobilebert-uncased-biored-finetuned-ner",
@@ -20,10 +21,8 @@ augmented_classifier = pipeline(
 st.title("Semantic Frame Augmentation")
 st.caption("Analysing difficult low-resource domains with only a handful of examples")
 
-st.write("This space uses a googel/mobilebert-uncased model for named entity ")
-augment = st.toggle('Use augmented model for ', value=False)
-
-sentence = choice(sentences)
+st.write("This space uses a googel/mobilebert-uncased model for NER")
+augment = st.toggle('Use augmented model for NER', value=False)
 
 if augment:
     st.write("with augmentation:")
@@ -39,6 +38,4 @@ txt = st.text_area(
     )
 
 st.subheader("Entity analysis:")
-for token in tokens:
-    st.write(token['entity_group'])
-    st.write(sentence[token["start"] : token["end"]])
+st.write(tokens)