diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py
old mode 100644
new mode 100755
index 1e26158310..50be048f4a
--- a/gensim/models/word2vec.py
+++ b/gensim/models/word2vec.py
@@ -36,14 +36,14 @@
   array([-0.00449447, -0.00310097,  0.02421786, ...], dtype=float32)
 
 The word vectors can also be instantiated from an existing file on disk in the word2vec C format
-as a KeyedVectors instance::
+as a KeyedVectors instance.
 
-    NOTE: It is impossible to continue training the vectors loaded from the C format because hidden weights,
-    vocabulary frequency and the binary tree is missing::
+NOTE: It is impossible to continue training the vectors loaded from the C format because hidden weights,
+vocabulary frequency and the binary tree is missing::
 
-        >>> from gensim.models import KeyedVectors
-        >>> word_vectors = KeyedVectors.load_word2vec_format('/tmp/vectors.txt', binary=False)  # C text format
-        >>> word_vectors = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True)  # C binary format
+    >>> from gensim.models import KeyedVectors
+    >>> word_vectors = KeyedVectors.load_word2vec_format('/tmp/vectors.txt', binary=False)  # C text format
+    >>> word_vectors = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True)  # C binary format
 
 
 You can perform various NLP word tasks with the model. Some of them