Fixed vocabulary in the entity linker training example (#5676)

* entity linker training example: model loading changed according to issue 5668 (#5668) + vocab_path is a required argument

* contributor agreement

Author: Matthias Hertel

examples/training/train_entity_linker.py

@@ -60,12 +60,12 @@ def sample_train_data():
     output_dir=("Optional output directory", "option", "o", Path),
     n_iter=("Number of training iterations", "option", "n", int),
 )
-def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
+def main(kb_path, vocab_path, output_dir=None, n_iter=50):
     """Create a blank model with the specified vocab, set up the pipeline and train the entity linker.
     The `vocab` should be the one used during creation of the KB."""
-    vocab = Vocab().from_disk(vocab_path)
     # create blank English model with correct vocab
-    nlp = spacy.blank("en", vocab=vocab)
+    nlp = spacy.blank("en")
+    nlp.vocab.from_disk(vocab_path)
     nlp.vocab.vectors.name = "spacy_pretrained_vectors"
     print("Created blank 'en' model with vocab from '%s'" % vocab_path)