Update README.md

README.md

@@ -9,9 +9,15 @@ datasets:
 inference: false
 ---
 
-## Flair NER model `en-ner-conll03-v0.4.pt`
+## English NER in Flair (default model)
 
-Imported from https://nlp.informatik.hu-berlin.de/resources/models/ner/
+This is the standard 4-class NER model for English that ships with Flair.
+
+Classes: 
+PER (person name)
+LOC (location name)
+ORG (organization name)
+MISC (other names)
 
 ### Demo: How to use in Flair
 
@@ -19,22 +25,27 @@ Imported from https://nlp.informatik.hu-berlin.de/resources/models/ner/
 from flair.data import Sentence
 from flair.models import SequenceTagger
 
-sentence = Sentence(
-	"My name is Julien, I currently live in Paris, I work at Hugging Face, Inc."
-)
-
-tagger = SequenceTagger.load("julien-c/flair-ner")
+# load tagger
+tagger = SequenceTagger.load("flair/ner-english")
 
+# make example sentence
+sentence = Sentence("George Washington went to Washington")
 
 # predict NER tags
 tagger.predict(sentence)
 
-# print sentence with predicted tags
-print(sentence.to_tagged_string())
+# print sentence
+print(sentence)
 
+# print predicted NER spans
+print('The following NER tags are found:')
+# iterate over entities and print
+for entity in sentence.get_spans('ner'):
+    print(entity)
 
 ```
 
 yields the following output:
 
-> `My name is Julien <S-PER> , I currently live in Paris <S-LOC> , I work at Hugging <B-LOC> Face <E-LOC> .`
+> `Span [1,2]: "George Washington"   [− Labels: PER (0.9968)]
+Span [5]: "Washington"   [− Labels: LOC (0.9994)]`