diff --git a/bin/parser/nn_train.py b/bin/parser/nn_train.py
index 375996f4f..e0ae846b5 100755
--- a/bin/parser/nn_train.py
+++ b/bin/parser/nn_train.py
@@ -84,7 +84,8 @@ def _merge_sents(sents):
 def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
           seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
           verbose=False,
-          eta=0.01, mu=0.9, n_hidden=100, word_vec_len=10, pos_vec_len=10):
+          eta=0.01, mu=0.9, n_hidden=100,
+          nv_word=10, nv_tag=10, nv_label=10):
     dep_model_dir = path.join(model_dir, 'deps')
     pos_model_dir = path.join(model_dir, 'pos')
     ner_model_dir = path.join(model_dir, 'ner')
@@ -99,8 +100,15 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
     os.mkdir(ner_model_dir)
     setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
 
-    Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
-                 labels=Language.ParserTransitionSystem.get_labels(gold_tuples))
+    Config.write(dep_model_dir, 'config',
+        seed=seed,
+        features=feat_set,
+        labels=Language.ParserTransitionSystem.get_labels(gold_tuples),
+        vector_lengths=(nv_word, nv_tag, nv_label),
+        hidden_nodes=n_hidden,
+        eta=eta,
+        mu=mu
+    )
     Config.write(ner_model_dir, 'config', features='ner', seed=seed,
                  labels=Language.EntityTransitionSystem.get_labels(gold_tuples),
                  beam_width=0)
@@ -110,16 +118,17 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
     
     nlp = Language(data_dir=model_dir)
 
-    def make_model(n_classes, input_spec, model_dir):
-        print input_spec
-        n_in = sum(n_cols * len(fields) for (n_cols, fields) in input_spec)
+    def make_model(n_classes, (words, tags, labels), model_dir):
+        n_in = (nv_word  * len(words)) + \
+               (nv_tag   * len(tags)) + \
+               (nv_label * len(labels))
         print 'Compiling'
         debug, train_func, predict_func = compile_theano_model(n_classes, n_hidden,
                                                         n_in, 0.0, 0.0)
         print 'Done'
         return TheanoModel(
             n_classes,
-            input_spec,
+            ((nv_word, words), (nv_tag, tags), (nv_label, labels)),
             train_func,
             predict_func,
             model_loc=model_dir, 
@@ -226,14 +235,23 @@ def write_parses(Language, dev_loc, model_dir, out_loc, beam_width=None):
     n_sents=("Number of training sentences", "option", "n", int),
     n_iter=("Number of training iterations", "option", "i", int),
     verbose=("Verbose error reporting", "flag", "v", bool),
-    debug=("Debug mode", "flag", "d", bool),
+
+    nv_word=("Word vector length", "option", "W", int),
+    nv_tag=("Tag vector length", "option", "T", int),
+    nv_label=("Label vector length", "option", "L", int),
+    nv_hidden=("Hidden nodes length", "option", "H", int),
+    eta=("Learning rate", "option", "E", float),
+    mu=("Momentum", "option", "M", float),
 )
 def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
-         debug=False, corruption_level=0.0, gold_preproc=False, beam_width=1,
+         corruption_level=0.0, gold_preproc=False,
+         nv_word=10, nv_tag=10, nv_label=10, nv_hidden=10,
+         eta=0.1, mu=0.9,
          eval_only=False):
     gold_train = list(read_json_file(train_loc))
     nlp = train(English, gold_train, model_dir,
                feat_set='embed',
+               nv_word=nv_word, nv_tag=nv_tag, nv_label=nv_label,
                gold_preproc=gold_preproc, n_sents=n_sents,
                corruption_level=corruption_level, n_iter=n_iter,
                verbose=verbose)