Update README.md

README.md

@@ -42,10 +42,8 @@ linker.predict(sentence)
 
 # print the results for each entity mention:
 for span in sentence.get_spans(tagger.label_type):
-    print(f"Span: {span.text}")
-    for candidate_label in span.get_labels(linker.label_type):
-        candidate = dictionary[candidate_label.value]
-        print(f"Candidate: {candidate.concept_name}")
+    for link in span.get_labels(linker.label_type):
+      print(f"{span.text} -> {link.value}")
 ```
 
 As an alternative to downloading the already precomputed model (much storage). You can also build the model