Remove old example

examples/training/load_ner.py

@@ -1,22 +0,0 @@
-# Load NER
-from __future__ import unicode_literals
-import spacy
-import pathlib
-from spacy.pipeline import EntityRecognizer
-from spacy.vocab import Vocab
-
-def load_model(model_dir):
-    model_dir = pathlib.Path(model_dir)
-    nlp = spacy.load('en', parser=False, entity=False, add_vectors=False)
-    with (model_dir / 'vocab' / 'strings.json').open('r', encoding='utf8') as file_:
-        nlp.vocab.strings.load(file_)
-    nlp.vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin')
-    ner = EntityRecognizer.load(model_dir, nlp.vocab, require=True)
-    return (nlp, ner)
-
-(nlp, ner) = load_model('ner')
-doc = nlp.make_doc('Who is Shaka Khan?')
-nlp.tagger(doc)
-ner(doc)
-for word in doc:
-    print(word.text, word.orth, word.lower, word.tag_, word.ent_type_, word.ent_iob)