diff --git a/spacy/_ml.py b/spacy/_ml.py
index 2e95aa55b..22a501f0f 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -505,7 +505,7 @@ def getitem(i):
     return layerize(getitem_fwd)
 
 def build_tagger_model(nr_class, **cfg):
-    embed_size = util.env_opt('embed_size', 4000)
+    embed_size = util.env_opt('embed_size', 1000)
     if 'token_vector_width' in cfg:
         token_vector_width = cfg['token_vector_width']
     else:
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 99099cad8..830aac551 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -240,12 +240,12 @@ cdef class Parser:
     Base class of the DependencyParser and EntityRecognizer.
     """
     @classmethod
-    def Model(cls, nr_class, token_vector_width=128, hidden_width=300, depth=1, **cfg):
+    def Model(cls, nr_class, token_vector_width=128, hidden_width=200, depth=1, **cfg):
         depth = util.env_opt('parser_hidden_depth', depth)
         token_vector_width = util.env_opt('token_vector_width', token_vector_width)
         hidden_width = util.env_opt('hidden_width', hidden_width)
         parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
-        embed_size = util.env_opt('embed_size', 4000)
+        embed_size = util.env_opt('embed_size', 1000)
         tok2vec = Tok2Vec(token_vector_width, embed_size,
                           pretrained_dims=cfg.get('pretrained_dims', 0))
         tok2vec = chain(tok2vec, flatten)