diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 7ddc8d1cd..fa7d85798 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -17,7 +17,7 @@ from .. import displacy
 
 
 def train(language, output_dir, train_data, dev_data, n_iter, n_sents,
-          use_gpu, tagger, parser, ner, parser_L1):
+          use_gpu, no_tagger, no_parser, no_entities, parser_L1):
     output_path = util.ensure_path(output_dir)
     train_path = util.ensure_path(train_data)
     dev_path = util.ensure_path(dev_data)
@@ -44,9 +44,11 @@ def train(language, output_dir, train_data, dev_data, n_iter, n_sents,
         'lang': language,
         'features': lang.Defaults.tagger_features}
     gold_train = list(read_gold_json(train_path, limit=n_sents))
-    gold_dev = list(read_gold_json(dev_path, limit=n_sents)) if dev_path else None
+    gold_dev = list(read_gold_json(dev_path, limit=n_sents))
 
-    train_model(lang, gold_train, gold_dev, output_path, n_iter, use_gpu=use_gpu)
+    train_model(lang, gold_train, gold_dev, output_path, n_iter,
+                no_tagger=no_tagger, no_parser=no_parser, no_entities=no_entities,
+                use_gpu=use_gpu)
     if gold_dev:
         scorer = evaluate(lang, gold_dev, output_path)
         print_results(scorer)
@@ -65,34 +67,43 @@ def train_config(config):
 def train_model(Language, train_data, dev_data, output_path, n_iter, **cfg):
     print("Itn.\tDep. Loss\tUAS\tNER F.\tTag %\tToken %")
 
-    nlp = Language(pipeline=['token_vectors', 'tags', 'dependencies'])
+    pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
+    if cfg.get('no_tagger') and 'tags' in pipeline:
+        pipeline.remove('tags')
+    if cfg.get('no_parser') and 'dependencies' in pipeline:
+        pipeline.remove('dependencies')
+    if cfg.get('no_entities') and 'entities' in pipeline:
+        pipeline.remove('entities')
+    print(pipeline)
+    nlp = Language(pipeline=pipeline)
     dropout = util.env_opt('dropout', 0.0)
     # TODO: Get spaCy using Thinc's trainer and optimizer
     with nlp.begin_training(train_data, **cfg) as (trainer, optimizer):
-        for itn, epoch in enumerate(trainer.epochs(n_iter, gold_preproc=True)):
+        for itn, epoch in enumerate(trainer.epochs(n_iter, gold_preproc=False)):
             losses = defaultdict(float)
-            to_render = []
             for i, (docs, golds) in enumerate(epoch):
-                state = nlp.update(docs, golds, drop=dropout, sgd=optimizer)
-                losses['dep_loss'] += state.get('parser_loss', 0.0)
-                losses['tag_loss'] += state.get('tag_loss', 0.0)
-                to_render.insert(0, nlp(docs[-1].text))
-                to_render[0].user_data['title'] = "Batch %d" % i
-                with Path('/tmp/entities.html').open('w') as file_:
-                    html = displacy.render(to_render[:5], style='ent', page=True)
-                    file_.write(html)
-                with Path('/tmp/parses.html').open('w') as file_:
-                    html = displacy.render(to_render[:5], style='dep', page=True)
-                    file_.write(html)
+                nlp.update(docs, golds, drop=dropout, sgd=optimizer)
+                for doc in docs:
+                    doc.tensor = None
+                    doc._py_tokens = []
             if dev_data:
                 with nlp.use_params(optimizer.averages):
-                    dev_scores = trainer.evaluate(dev_data).scores
+                    dev_scores = trainer.evaluate(dev_data, gold_preproc=False).scores
             else:
                 dev_scores = defaultdict(float)
             print_progress(itn, losses, dev_scores)
     with (output_path / 'model.bin').open('wb') as file_:
         dill.dump(nlp, file_, -1)
-    #nlp.to_disk(output_path, tokenizer=False)
+
+
+def _render_parses(i, to_render):
+    to_render[0].user_data['title'] = "Batch %d" % i
+    with Path('/tmp/entities.html').open('w') as file_:
+        html = displacy.render(to_render[:5], style='ent', page=True)
+        file_.write(html)
+    with Path('/tmp/parses.html').open('w') as file_:
+        html = displacy.render(to_render[:5], style='dep', page=True)
+        file_.write(html)
 
 
 def evaluate(Language, gold_tuples, path):