diff --git a/spacy/cli/evaluate.py b/spacy/cli/evaluate.py
index d9be95fae..42e077dc2 100644
--- a/spacy/cli/evaluate.py
+++ b/spacy/cli/evaluate.py
@@ -33,16 +33,23 @@ numpy.random.seed(0)
     data_path=("Location of JSON-formatted evaluation data", "positional", None, str),
     gold_preproc=("Use gold preprocessing", "flag", "G", bool),
     gpu_id=("Use GPU", "option", "g", int),
+    displacy_path=("Directory to output rendered parses as HTML", "option", "dp", str),
+    displacy_limit=("Limit of parses to render as HTML", "option", "dl", int)
 )
-def evaluate(cmd, model, data_path, gpu_id=-1, gold_preproc=False):
+def evaluate(cmd, model, data_path, gpu_id=-1, gold_preproc=False,
+             displacy_path=None, displacy_limit=25):
     """
-    Train a model. Expects data in spaCy's JSON format.
+    Evaluate a model. To render a sample of parses in a HTML file, set an output
+    directory as the displacy_path argument.
     """
     util.use_gpu(gpu_id)
     util.set_env_log(False)
     data_path = util.ensure_path(data_path)
+    displacy_path = util.ensure_path(displacy_path)
     if not data_path.exists():
         prints(data_path, title="Evaluation data not found", exits=1)
+    if displacy_path and not displacy_path.exists():
+        prints(displacy_path, title="Visualization output directory not found", exits=1)
     corpus = GoldCorpus(data_path, data_path)
     nlp = util.load_model(model)
     dev_docs = list(corpus.dev_docs(nlp, gold_preproc=gold_preproc))
@@ -50,18 +57,27 @@ def evaluate(cmd, model, data_path, gpu_id=-1, gold_preproc=False):
     scorer = nlp.evaluate(dev_docs, verbose=False)
     end = timer()
     nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
-    print('Time', end-begin, 'words', nwords, 'w.p.s', nwords/(end-begin))
-    print_results(scorer)
+    print_results(scorer, time=end - begin, words=nwords,
+                  wps=nwords / (end - begin))
+    if displacy_path:
+        docs, golds = zip(*dev_docs)
+        render_deps = 'parser' in nlp.meta.get('pipeline', [])
+        render_ents = 'ner' in nlp.meta.get('pipeline', [])
+        render_parses(docs, displacy_path, model_name=model, limit=displacy_limit,
+                      deps=render_deps, ents=render_ents)
+        prints(displacy_path, title="Generated %s parses as HTML" % displacy_limit)
 
 
-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 render_parses(docs, output_path, model_name='', limit=250, deps=True, ents=True):
+    docs[0].user_data['title'] = model_name
+    if ents:
+        with (output_path / 'entities.html').open('w') as file_:
+            html = displacy.render(docs[:limit], style='ent', page=True)
+            file_.write(html)
+    if deps:
+        with (output_path / 'parses.html').open('w') as file_:
+            html = displacy.render(docs[:limit], style='dep', page=True, options={'compact': True})
+            file_.write(html)
 
 
 def print_progress(itn, losses, dev_scores, wps=0.0):
@@ -88,8 +104,11 @@ def print_progress(itn, losses, dev_scores, wps=0.0):
     print(tpl.format(itn, **scores))
 
 
-def print_results(scorer):
+def print_results(scorer, time, words, wps):
     results = {
+        'Time': '%.2f s' % time,
+        'Words': words,
+        'Words/s': '%.0f' % wps,
         'TOK': '%.2f' % scorer.token_acc,
         'POS': '%.2f' % scorer.tags_acc,
         'UAS': '%.2f' % scorer.uas,