diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 33b9028115f..debd2d6417a 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -135,6 +135,7 @@ class Dataset:
     """
 
     def __init__(self, num_parallel_workers=None):
+        # Note: children and parent are internal variables, not recommand for external using.
         self.children = []
         self.parent = []
         self.num_parallel_workers = num_parallel_workers
diff --git a/mindspore/dataset/text/utils.py b/mindspore/dataset/text/utils.py
index 153ec484318..1dc3012ce95 100644
--- a/mindspore/dataset/text/utils.py
+++ b/mindspore/dataset/text/utils.py
@@ -185,7 +185,14 @@ class SentencePieceVocab(cde.SentencePieceVocab):
                 character set.
             model_type(SentencePieceModel): Choose from unigram (default), bpe, char, or word. The input sentence
                 must be pretokenized when using word type.
-            params(dict): A dictionary with no incoming parameters.
+            params(dict): A dictionary with no incoming parameters(The parameters are derived from SentencePiece
+                library).
+
+            .. code-block ::
+                {
+                  "input_sentence_size" : 0,
+                  "max_sentencepiece_length" : 16
+                }
         """
         return super().from_file(file_path, vocab_size, character_coverage,
                                  DE_C_INTER_SENTENCEPIECE_MODE[model_type], params)
diff --git a/tests/ut/python/dataset/test_sentencepiece_tokenizer.py b/tests/ut/python/dataset/test_sentencepiece_tokenizer.py
index efcb656c105..e78c58e5a33 100644
--- a/tests/ut/python/dataset/test_sentencepiece_tokenizer.py
+++ b/tests/ut/python/dataset/test_sentencepiece_tokenizer.py
@@ -21,7 +21,7 @@ VOCAB_FILE = "../data/dataset/test_sentencepiece/botchan.txt"
 DATA_FILE = "../data/dataset/testTokenizerData/sentencepiece_tokenizer.txt"
 
 
-def test_from_vocab_to_str():
+def test_from_vocab_to_str_UNIGRAM():
     vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
     tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
     dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
@@ -33,6 +33,43 @@ def test_from_vocab_to_str():
             assert value == expect[key]
 
 
+def test_from_vocab_to_str_BPE():
+    vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.BPE, {})
+    tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
+    dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
+    dataset = dataset.map(operations=tokenizer)
+    expect = ['▁I', '▁saw', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'c', 'ope', '.']
+    for i in dataset.create_dict_iterator():
+        ret = to_str(i["text"])
+        for key, value in enumerate(ret):
+            assert value == expect[key]
+
+
+def test_from_vocab_to_str_CHAR():
+    vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.CHAR, {})
+    tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
+    dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
+    dataset = dataset.map(operations=tokenizer)
+    expect = ['▁', 'I', '▁', 's', 'a', 'w', '▁', 'a', '▁', 'g', 'i', 'r', 'l', '▁', 'w', 'i', 't', 'h',\
+              '▁', 'a', '▁', 't', 'e', 'l', 'e', 's', 'c', 'o', 'p', 'e', '.']
+    for i in dataset.create_dict_iterator():
+        ret = to_str(i["text"])
+        for key, value in enumerate(ret):
+            assert value == expect[key]
+
+
+def test_from_vocab_to_str_WORD():
+    vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.WORD, {})
+    tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
+    dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
+    dataset = dataset.map(operations=tokenizer)
+    expect = ['▁I', '▁saw', '▁a', '▁girl', '▁with', '▁a', '▁telescope.']
+    for i in dataset.create_dict_iterator():
+        ret = to_str(i["text"])
+        for key, value in enumerate(ret):
+            assert value == expect[key]
+
+
 def test_from_vocab_to_int():
     vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
     tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.INT)
@@ -85,7 +122,10 @@ def test_build_from_dataset():
 
 
 if __name__ == "__main__":
-    test_from_vocab_to_str()
+    test_from_vocab_to_str_UNIGRAM()
+    test_from_vocab_to_str_BPE()
+    test_from_vocab_to_str_CHAR()
+    test_from_vocab_to_str_WORD()
     test_from_vocab_to_int()
     test_from_file_to_str()
     test_from_file_to_int()