Update app.py

app.py

@@ -37,8 +37,8 @@ Please note that the scope of the model is not to run lexical entailment or hype
 
 Please note that, as in the examples above, the adjective should be the same for both phrases, and that the Hypernym(Noun) shoul be a true hypernym of the selected noun
 
-The current model achieves an accuracy of 90% on out-of-distribution evaluation
-Coming soon: check if words were in training data!"""
+The current model achieves an accuracy of 90% on out-of-distribution evaluation.
+Use the next page to check if your test-items (i.e. adjective, noun and hypernyms) were part of the training data!"""
 
 examples = [["A red car is a vehicle"], ["A fake smile is a smile"], ["A small cat is a small animal"]]
 examples_w = [["red,car,vehicle"], ["fake,smile"], ["small,cat,animal"]]
@@ -47,7 +47,7 @@ examples_w = [["red,car,vehicle"], ["fake,smile"], ["small,cat,animal"]]
 interface_words = gr.Interface(
             fn=test_input,
             inputs=gr.Textbox(label="Input:word_1,word2,...,word_n"),
-            outputs=gr.outputs.Label(label="**In training-distribution?**"),
+            outputs=gr.outputs.Label(label="In training-distribution?"),
             examples=examples_w,
 )
 # interface_words.launch()