Update README.md

README.md

@@ -24,16 +24,24 @@ It was trained and evaluated as a baseline with the aspect-normalized [UTCD](htt
 You can use the model like this:
 
 ```python
-from zeroshot_classifier.models import BinaryBertCrossEncoder
-model = BinaryBertCrossEncoder(model_name='claritylab/zero-shot-vanilla-binary-bert')
-
-text = "I'd like to have this track onto my Classical Relaxations playlist."
-labels = [
-    'Add To Playlist', 'Book Restaurant', 'Get Weather', 'Play Music', 'Rate Book', 'Search Creative Work',
-    'Search Screening Event'
-]
-query = [[text, lb] for lb in labels]
-logits = model.predict(query, apply_softmax=True)
-print(logits)
+>>> from zeroshot_classifier.models import BinaryBertCrossEncoder
+>>> model = BinaryBertCrossEncoder(model_name='claritylab/zero-shot-vanilla-binary-bert')
+
+>>> text = "I'd like to have this track onto my Classical Relaxations playlist."
+>>> labels = [
+>>>     'Add To Playlist', 'Book Restaurant', 'Get Weather', 'Play Music', 'Rate Book', 'Search Creative Work',
+>>>     'Search Screening Event'
+>>> ]
+>>> query = [[text, lb] for lb in labels]
+>>> logits = model.predict(query, apply_softmax=True)
+>>> print(logits)
+
+[[1.1909954e-04 9.9988091e-01]
+ [9.9997509e-01 2.4927122e-05]
+ [9.9997497e-01 2.5082643e-05]
+ [2.4483365e-04 9.9975520e-01]
+ [9.9996781e-01 3.2211588e-05]
+ [9.9985993e-01 1.4002046e-04]
+ [9.9976152e-01 2.3845369e-04]]
 ```