Update app.py

app.py

@@ -33,9 +33,9 @@ Please note that the scope of the model is not to run lexical entailment or hype
 
 - An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)
 
-- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a vehicle)
+- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle)
 
-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
+Please note that, as in the examples above, the adjective should be the same for both phrases, and that the Hypernym(Noun) should be a true hypernym of the selected noun.
 
 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!"""
@@ -50,7 +50,6 @@ interface_words = gr.Interface(
             outputs=gr.outputs.Label(label="In training-distribution?"),
             examples=examples_w,
 )
-# interface_words.launch()
 
 interface_model = gr.Interface.load(
             "huggingface/lorenzoscottb/bert-base-cased-PLANE-ood-2",
@@ -58,8 +57,6 @@ interface_model = gr.Interface.load(
             examples=examples,
             title=title,
 )
-# interface_model.launch()
-
 
 gr.TabbedInterface(
     [interface_model, interface_words], ["Test Model", "Check if words in/out-distribution"]