Fix NER training example

examples/train_ner.py

@@ -0,0 +1,64 @@
+from __future__ import unicode_literals, print_function
+import json
+import pathlib
+import random
+
+import spacy
+from spacy.pipeline import EntityRecognizer
+from spacy.gold import GoldParse
+
+
+def train_ner(nlp, train_data, entity_types):
+    ner = EntityRecognizer.blank(nlp.vocab, entity_types=entity_types,
+                                 features=nlp.Defaults.entity_features)
+    for itn in range(5):
+        random.shuffle(train_data)
+        for raw_text, entity_offsets in train_data:
+            doc = nlp.make_doc(raw_text)
+            gold = GoldParse(doc, entities=entity_offsets)
+            ner.update(doc, gold)
+    ner.model.end_training()
+    return ner
+
+
+def main(model_dir=None):
+    if model_dir is not None:
+        model_dir = pathlb.Path(model_dir)
+        if not model_dir.exists():
+            model_dir.mkdir()
+        assert model_dir.isdir()
+
+    nlp = spacy.load('en', parser=False, entity=False, vectors=False)
+
+    train_data = [
+        (
+            'Who is Shaka Khan?',
+            [(len('Who is '), len('Who is Shaka Khan'), 'PERSON')]
+        ),
+        (
+            'I like London and Berlin.',
+            [(len('I like '), len('I like London'), 'LOC'),
+            (len('I like London and '), len('I like London and Berlin'), 'LOC')]
+        )
+    ]
+    ner = train_ner(nlp, train_data, ['PERSON', 'LOC'])
+
+    doc = nlp.make_doc('Who is Shaka Khan?')
+    nlp.tagger(doc)
+    ner(doc)
+    for word in doc:
+        print(word.text, word.tag_, word.ent_type_, word.ent_iob)
+
+    if model_dir is not None:
+        with (model_dir / 'config.json').open('wb') as file_:
+            json.dump(ner.cfg, file_)
+        ner.model.dump(str(model_dir / 'model'))
+
+
+if __name__ == '__main__':
+    main()
+    # Who "" 2
+    # is "" 2
+    # Shaka "" PERSON 3
+    # Khan "" PERSON 1
+    # ? "" 2