diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc
index 599df9c3df58db6444d7cb729e1a2c1f9f628b5b..c923e988a55b43ebb7ba6256e7b72a85c124f360 100644
--- a/paddle/operators/mul_op.cc
+++ b/paddle/operators/mul_op.cc
@@ -113,7 +113,7 @@ This operator is used to perform matrix multiplication for input $X$ and $Y$.
 
 The equation is:
 
-    $$Out = X * Y$$
+$$Out = X * Y$$
 
 Both the input $X$ and $Y$ can carry the LoD (Level of Details) information,
 or not. But the output only shares the LoD information with input $X$.
diff --git a/paddle/operators/positive_negative_pair_op.cc b/paddle/operators/positive_negative_pair_op.cc
index ab9f67bfe6b3d6f59b35a57cb8135e9c6d00636e..c607c93a15609e51a3019b91b67aa328abf6a054 100644
--- a/paddle/operators/positive_negative_pair_op.cc
+++ b/paddle/operators/positive_negative_pair_op.cc
@@ -154,13 +154,14 @@ class PositiveNegativePairOpMaker : public framework::OpProtoAndCheckerMaker {
         "Noting that reducing on the first dim will make the LoD info lost.")
         .SetDefault(0);
     AddComment(R"DOC(
-        PositiveNegativePairOp can be used to evaluate Learning To Rank(LTR) 
-        model performance. 
-        Within some context, e.g. the "query", a LTR model generates scores
-        for a list of items, which gives a partial order of the items.
-        PositiveNegativePairOp takes a list of reference rank order 
-        (Input("Label")) and the model generated scores (Input(Score)) as 
-        inputs and counts the pairs that ranked correctly and incorrectly.
+PositiveNegativePairOp can be used to evaluate Learning To Rank(LTR) model's
+performance.
+
+Within some context, e.g. the "query", a LTR model generates scores for a list
+of items, which gives a partial order of the items. PositiveNegativePairOp
+takes a list of reference rank order (Input("Label")) and the model generated
+scores (Input(Score)) as inputs and counts the pairs that ranked correctly
+and incorrectly.
 )DOC");
   }
 };