Update README.md

README.md

@@ -32,11 +32,36 @@ You can use this model with a Transformers `pipeline` for token classification:
 >>> sentences = ['Die Pflanze sei im Laufe der Zeit unscheinbarer geworden und damit für Menschen schwerer zu finden, berichten die Forscher im Fachmagazin Current Biology.']
 >>> journal_identifier(sentences)
 
-[[{'entity': 'J-Start', 'score': np.float32(0.9984914), 'index': 27, 'word': 'Cur', 'start': 138, 'end': 141},
-  {'entity': 'J-Start', 'score': np.float32(0.9978611), 'index': 28, 'word': '##rent', 'start': 141, 'end': 145},
-  {'entity': 'J-Inner', 'score': np.float32(0.99738055), 'index': 29, 'word': 'Bio', 'start': 146, 'end': 149},
-  {'entity': 'J-Inner', 'score': np.float32(0.9970715), 'index': 30, 'word': '##log', 'start': 149, 'end': 152},
-  {'entity': 'J-Inner', 'score': np.float32(0.99715745), 'index': 31, 'word': '##y', 'start': 152, 'end': 153}]]
+[[{'entity': 'J-Start',
+   'score': np.float32(0.9984914),
+   'index': 27,
+   'word': 'Cur',
+   'start': 138,
+   'end': 141},
+  {'entity': 'J-Start',
+   'score': np.float32(0.9978611),
+   'index': 28,
+   'word': '##rent',
+   'start': 141,
+   'end': 145},
+  {'entity': 'J-Inner',
+   'score': np.float32(0.99738055),
+   'index': 29,
+   'word': 'Bio',
+   'start': 146,
+   'end': 149},
+  {'entity': 'J-Inner',
+   'score': np.float32(0.9970715),
+   'index': 30,
+   'word': '##log',
+   'start': 149,
+   'end': 152},
+  {'entity': 'J-Inner',
+   'score': np.float32(0.99715745),
+   'index': 31,
+   'word': '##y',
+   'start': 152,
+   'end': 153}]]
 ```
 
 ### Limitations