diff --git a/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/build_sentence_piece_vocab_node.cc b/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/build_sentence_piece_vocab_node.cc
index 9d3a17b559..eaf8378639 100644
--- a/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/build_sentence_piece_vocab_node.cc
+++ b/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/build_sentence_piece_vocab_node.cc
@@ -74,6 +74,10 @@ Status BuildSentenceVocabNode::ValidateParams() {
     RETURN_STATUS_SYNTAX_ERROR(err_msg);
   }
 
+  if (!col_names_.empty()) {
+    RETURN_IF_NOT_OK(ValidateDatasetColumnParam("BuildVocabNode", "columns", col_names_));
+  }
+
   return Status::OK();
 }
 
diff --git a/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.cc b/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.cc
index 45c31406e2..64734274dc 100644
--- a/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.cc
+++ b/mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.cc
@@ -198,8 +198,7 @@ void LiteMat::Init(int width, int height, void *p_data, LDataType data_type) {
   c_step_ = height_ * width_;
   size_ = c_step_ * channel_ * elem_size_;
   data_ptr_ = p_data;
-  ref_count_ = new int[1];
-  *ref_count_ = 0;
+  ref_count_ = nullptr;
 }
 
 void LiteMat::Init(int width, int height, int channel, LDataType data_type) {
@@ -227,8 +226,7 @@ void LiteMat::Init(int width, int height, int channel, void *p_data, LDataType d
   c_step_ = height_ * width_;
   size_ = c_step_ * channel_ * elem_size_;
   data_ptr_ = p_data;
-  ref_count_ = new int[1];
-  *ref_count_ = 0;
+  ref_count_ = nullptr;
 }
 
 bool LiteMat::IsEmpty() const { return data_ptr_ == 0 || data_ptr_ == nullptr || c_step_ * channel_ == 0; }
diff --git a/mindspore/dataset/text/transforms.py b/mindspore/dataset/text/transforms.py
index 5d3e160a6c..a8ec4ff560 100644
--- a/mindspore/dataset/text/transforms.py
+++ b/mindspore/dataset/text/transforms.py
@@ -76,7 +76,7 @@ class Lookup(cde.LookupOp):
         >>> # Load vocabulary from list
         >>> vocab = text.Vocab.from_list(['深', '圳', '欢', '迎', '您'])
         >>> # Use Lookup operator to map tokens to ids
-        >>> lookup = text.Lookup(vocab, "<unk>")
+        >>> lookup = text.Lookup(vocab)
         >>> data1 = data1.map(operations=[lookup])
     """