Remove paraphrase and swap fine-tuning task order

app.py

@@ -131,27 +131,6 @@ if st.button("Fill the mask"):
         st.write("_English_ _translation:_", translate(result_sequence, "en", "es"))
         st.write(result)
 
-st.markdown(
-    """
-    ### Fine-tuning to XNLI for paraphrase identification
-    Here you can play with the RoBERTa Base Gaussian Seq Len 512 model fine-tuned to XNLI.
-    """
-)
-
-pawsx_model_url = "bertin-project/bertin-base-xnli-es"
-paraphrase_prompt = st.selectbox("Paraphrase Prompt", ["Random", "Custom"])
-if paraphrase_prompt == "Custom":
-    paraphrase_prompt_box = "Enter two sentences separated by </s> here..."
-else:
-    paraphrase_prompt_box = random.choice(PAWS_X_PROMPT_LIST)
-text = st.text_area("Enter text", paraphrase_prompt_box)
-if st.button("Clasify paraphrasing"):
-    with st.spinner(text="Clasifying paraphrasing..."):
-        st.subheader("Classification result")
-        paraphrase_score = load_model_pair_classification(text, pawsx_model_url)
-        st.write("_English_ _translation:_", translate(text, "en", "es"))
-        st.write(paraphrase_score)
-
 
 def make_color_palette(labels):
     color_palette = sns.color_palette(n_colors=len(labels))
@@ -266,6 +245,27 @@ if st.button("Recognize named entities"):
         else:
             st.write("No entities found")
 
+st.markdown(
+    """
+    ### Fine-tuning to XNLI
+    Here you can play with the RoBERTa Base Gaussian Seq Len 512 model fine-tuned to XNLI.
+    """
+)
+
+pawsx_model_url = "bertin-project/bertin-base-xnli-es"
+paraphrase_prompt = st.selectbox("Paraphrase Prompt", ["Random", "Custom"])
+if paraphrase_prompt == "Custom":
+    paraphrase_prompt_box = "Enter two sentences separated by </s> here..."
+else:
+    paraphrase_prompt_box = random.choice(PAWS_X_PROMPT_LIST)
+text = st.text_area("Enter text", paraphrase_prompt_box)
+if st.button("Clasify paraphrasing"):
+    with st.spinner(text="Clasifying paraphrasing..."):
+        st.subheader("Classification result")
+        paraphrase_score = load_model_pair_classification(text, pawsx_model_url)
+        st.write("_English_ _translation:_", translate(text, "en", "es"))
+        st.write(paraphrase_score)
+
 st.markdown(
     """
     ### Team members