diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 7ad94ce9c..8a3446cfe 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -53,7 +53,6 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
     if dev_path and not dev_path.exists():
         prints(dev_path, title="Development data not found", exits=1)
 
-    lang_class = util.get_lang_class(lang)
 
     pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
     if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
@@ -71,22 +70,22 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                                    util.env_opt('batch_to', 64),
                                    util.env_opt('batch_compound', 1.001))
 
-    if resume:
-        prints(output_path / 'model9.pickle', title="Resuming training")
-        nlp = dill.load((output_path / 'model9.pickle').open('rb'))
-    else:
+    if not resume:
+        lang_class = util.get_lang_class(lang)
         nlp = lang_class(pipeline=pipeline)
+    else:
+        print("Load resume")
+        nlp = _resume_model(lang, pipeline)
+        lang_class = nlp.__class__
+
     corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
     n_train_words = corpus.count_train()
-
     optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
     nlp._optimizer = None
 
     print("Itn.\tLoss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
     try:
         for i in range(n_iter):
-            if resume:
-                i += 20
             with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
                 train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
                                                gold_preproc=gold_preproc, max_length=0)
@@ -120,6 +119,17 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                 dill.dump(nlp, file_, -1)
 
 
+def _resume_model(lang, pipeline):
+    nlp = util.load_model(lang)
+    pipes = {getattr(pipe, 'name', None) for pipe in nlp.pipeline}
+    for name in pipeline:
+        if name not in pipes:
+            factory = nlp.Defaults.factories[name]
+            nlp.pipeline.extend(factory(nlp))
+    nlp.meta['pipeline'] = pipeline
+    return nlp
+
+
 def _render_parses(i, to_render):
     to_render[0].user_data['title'] = "Batch %d" % i
     with Path('/tmp/entities.html').open('w') as file_: