diff --git a/examples/training/ner_multitask_objective.py b/examples/training/ner_multitask_objective.py
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+++ b/examples/training/ner_multitask_objective.py
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+'''This example shows how to add a multi-task objective that is trained
+alongside the entity recognizer. This is an alternative to adding features
+to the model.
+
+The multi-task idea is to train an auxiliary model to predict some attribute,
+with weights shared between the auxiliary model and the main model. In this
+example, we're predicting the position of the word in the document.
+
+The model that predicts the position of the word encourages the convolutional
+layers to include the position information in their representation. The
+information is then available to the main model, as a feature.
+
+The overall idea is that we might know something about what sort of features
+we'd like the CNN to extract. The multi-task objectives can encourage the
+extraction of this type of feature. The multi-task objective is only used
+during training. We discard the auxiliary model before run-time.
+
+The specific example here is not necessarily a good idea --- but it shows
+how an arbitrary objective function for some word can be used.
+
+Developed and tested for spaCy 2.0.6
+'''
+import random
+import plac
+import spacy
+import os.path
+from spacy.gold import read_json_file, GoldParse
+
+random.seed(0)
+
+PWD = os.path.dirname(__file__)
+
+TRAIN_DATA = list(read_json_file(os.path.join(PWD, 'training-data.json')))
+
+
+
+def get_position_label(i, words, tags, heads, labels, ents):
+    '''Return labels indicating the position of the word in the document.
+    '''
+    if len(words) < 20:
+        return 'short-doc'
+    elif i == 0:
+        return 'first-word'
+    elif i < 10:
+        return 'early-word'
+    elif i < 20:
+        return 'mid-word'
+    elif i == len(words)-1:
+        return 'last-word'
+    else:
+        return 'late-word'
+
+
+def main(n_iter=10):
+    nlp = spacy.blank('en')
+    ner = nlp.create_pipe('ner')
+    ner.add_multitask_objective(get_position_label)
+    nlp.add_pipe(ner)
+
+    print("Create data", len(TRAIN_DATA))
+    optimizer = nlp.begin_training(get_gold_tuples=lambda: TRAIN_DATA)
+    for itn in range(n_iter):
+        random.shuffle(TRAIN_DATA)
+        losses = {}
+        for text, annot_brackets in TRAIN_DATA:
+            annotations, _ = annot_brackets
+            doc = nlp.make_doc(text)
+            gold = GoldParse.from_annot_tuples(doc, annotations[0])
+            nlp.update(
+                [doc],  # batch of texts
+                [gold],  # batch of annotations
+                drop=0.2,  # dropout - make it harder to memorise data
+                sgd=optimizer,  # callable to update weights
+                losses=losses)
+        print(losses.get('nn_labeller', 0.0), losses['ner'])
+
+    # test the trained model
+    for text, _ in TRAIN_DATA:
+        doc = nlp(text)
+        print('Entities', [(ent.text, ent.label_) for ent in doc.ents])
+        print('Tokens', [(t.text, t.ent_type_, t.ent_iob) for t in doc])
+
+
+if __name__ == '__main__':
+    plac.call(main)