Update README.md

Add Walhberg - Manny example

README.md

@@ -26,13 +26,14 @@ model.predict([
     ["A person on a horse jumps over a broken down airplane.", "A person is outdoors, on a horse."],
     ["A boy is jumping on skateboard in the middle of a red bridge.", "The boy skates down the sidewalk on a blue bridge"],
     ["A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond drinking water in public."],
-    ["A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond man wearing a brown shirt is reading a book."],    
+    ["A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond man wearing a brown shirt is reading a book."],
+    ["Mark Wahlberg was a fan of Manny.", "Manny was a fan of Mark Wahlberg."],  
 ])
 ```
 
 This returns a numpy array:
 ```
-array([6.1051625e-01, 4.7493601e-04, 9.9639291e-01, 2.1221593e-04, 9.9599433e-01, 1.4126947e-03], dtype=float32)
+array([6.1051559e-01, 4.7493709e-04, 9.9639291e-01, 2.1221573e-04, 9.9599433e-01, 1.4127002e-03, 2.8262993e-03], dtype=float32)
 ```
 
 ## Usage with Transformers AutoModel
@@ -50,7 +51,8 @@ pairs = [
     ["A person on a horse jumps over a broken down airplane.", "A person is outdoors, on a horse."],
     ["A boy is jumping on skateboard in the middle of a red bridge.", "The boy skates down the sidewalk on a blue bridge"],
     ["A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond drinking water in public."],
-    ["A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond man wearing a brown shirt is reading a book."],    
+    ["A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond man wearing a brown shirt is reading a book."],
+    ["Mark Wahlberg was a fan of Manny.", "Manny was a fan of Mark Wahlberg."], 
 ]
 
 inputs = tokenizer.batch_encode_plus(pairs, return_tensors='pt', padding=True)
@@ -64,5 +66,5 @@ with torch.no_grad():
 
 This returns a numpy array:
 ```
-array([6.1051559e-01, 4.7493709e-04, 9.9639291e-01, 2.1221573e-04, 9.9599433e-01, 1.4127002e-03], dtype=float32)
+array([6.1051559e-01, 4.7493709e-04, 9.9639291e-01, 2.1221573e-04, 9.9599433e-01, 1.4127002e-03, 2.8262993e-03], dtype=float32)
 ```