Update README.md

README.md

@@ -21,7 +21,34 @@ The Token labels are Person, Organisation, Location, Building, Event, Product, A
 ## How to use the model
 
 ```python
+def print_ner(sentences):
+    """Cleaning and printing NER results
+
+    """
+    for sentence in sentences:
+        last_entity_type = sentence[0]['entity']
+        last_index = sentence[0]['index']
+        word = sentence[0]['word']
+        for i, token in enumerate(sentence):
+            if (i > 0):
+                if (token['entity'] == last_entity_type) and (token['index'] == last_index + 1):
+                    word = word + '' + token['word']
+
+                else:
+                    word = word.replace('▁', ' ')
+                    print(f"{word[1:]} {last_entity_type}")
+                    word = token['word']
+                last_entity_type = token['entity']
+                last_index = token['index']
+
+                if i == len(sentence) - 1:
+                    word = word.replace('▁', ' ')
+                    print(f"{word[1:]} {last_entity_type}")
+
+
 from transformers import pipeline
 pipe = pipeline(model='RashidNLP/NER-Deberta')
-pipe(["Elon Musk will be at SpaceX's Starbase facility in Boca Chica for the orbital launch of starship next month"])
+sentence = pipe(["Elon Musk will be at SpaceX's Starbase facility in Boca Chica for the orbital launch of starship next month"])
+print_ner(sentence)
+
 ```