diff --git a/examples/train_ner.py b/examples/train_ner.py
deleted file mode 100644
index 3f748a488..000000000
--- a/examples/train_ner.py
+++ /dev/null
@@ -1,64 +0,0 @@
-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
diff --git a/examples/train_pos_tagger.py b/examples/train_pos_tagger.py
deleted file mode 100644
index 43bd607c7..000000000
--- a/examples/train_pos_tagger.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""A quick example for training a part-of-speech tagger, without worrying
-about the tokenization, or other language-specific customizations."""
-
-from __future__ import unicode_literals
-from __future__ import print_function
-
-import plac
-from os import path
-import os
-
-from spacy.vocab import Vocab
-from spacy.tokenizer import Tokenizer
-from spacy.tagger import Tagger
-import random
-
-
-# You need to define a mapping from your data's part-of-speech tag names to the
-# Universal Part-of-Speech tag set, as spaCy includes an enum of these tags.
-# See here for the Universal Tag Set:
-# http://universaldependencies.github.io/docs/u/pos/index.html
-# You may also specify morphological features for your tags, from the universal
-# scheme.
-TAG_MAP = {
-        'N': {"pos": "NOUN"},
-        'V': {"pos": "VERB"},
-        'J': {"pos": "ADJ"}
-    }
-
-# Usually you'll read this in, of course. Data formats vary.
-# Ensure your strings are unicode.
-DATA = [
-    (
-        ["I", "like", "green", "eggs"],
-        ["N", "V",    "J",     "N"]
-    ),
-    (
-        ["Eat", "blue", "ham"],
-        ["V",   "J",    "N"]
-    )
-]
-    
-def ensure_dir(*parts):
-    path_ = path.join(*parts)
-    if not path.exists(path_):
-        os.mkdir(path_)
-    return path_
-
-
-def main(output_dir):
-    ensure_dir(output_dir)
-    ensure_dir(output_dir, "pos")
-    ensure_dir(output_dir, "vocab")
-    
-    vocab = Vocab(tag_map=TAG_MAP)
-    tokenizer = Tokenizer(vocab, {}, None, None, None)
-    # The default_templates argument is where features are specified. See
-    # spacy/tagger.pyx for the defaults.
-    tagger = Tagger.blank(vocab, Tagger.default_templates())
-
-    for i in range(5):
-        for words, tags in DATA:
-            tokens = tokenizer.tokens_from_list(words)
-            tagger.train(tokens, tags)
-        random.shuffle(DATA)
-    tagger.model.end_training()
-    tagger.model.dump(path.join(output_dir, 'pos', 'model'))
-    with io.open(output_dir, 'vocab', 'strings.json') as file_:
-        tagger.vocab.strings.dump(file_)
-
-
-if __name__ == '__main__':
-    plac.call(main)