diff --git a/test/embeddings/test_static_embedding.py b/test/embeddings/test_static_embedding.py
index 0c8fc739..6fd33072 100644
--- a/test/embeddings/test_static_embedding.py
+++ b/test/embeddings/test_static_embedding.py
@@ -3,13 +3,90 @@ import unittest
 from fastNLP.embeddings import StaticEmbedding
 from fastNLP import Vocabulary
 import torch
+import os
 
 class TestRandomSameEntry(unittest.TestCase):
     def test_same_vector(self):
-        vocab = Vocabulary().add_word_lst(["The", "the", "THE"])
+        vocab = Vocabulary().add_word_lst(["The", "the", "THE", 'a', "A"])
         embed = StaticEmbedding(vocab, model_dir_or_name=None, embedding_dim=5, lower=True)
-        words = torch.LongTensor([[vocab.to_index(word) for word in ["The", "the", "THE"]]])
+        words = torch.LongTensor([[vocab.to_index(word) for word in ["The", "the", "THE", 'a', 'A']]])
         words = embed(words)
         embed_0 = words[0, 0]
-        for i in range(1, words.size(1)):
+        for i in range(1, 3):
             assert torch.sum(embed_0==words[0, i]).eq(len(embed_0))
+        embed_0 = words[0, 3]
+        for i in range(3, 5):
+            assert torch.sum(embed_0 == words[0, i]).eq(len(embed_0))
+
+    @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
+    def test_same_vector2(self):
+        vocab = Vocabulary().add_word_lst(["The", 'a', 'b', "the", "THE", "B", 'a', "A"])
+        embed = StaticEmbedding(vocab, model_dir_or_name='/remote-home/source/fastnlp_caches/glove.6B.100d/glove.6B.100d.txt',
+                                lower=True)
+        words = torch.LongTensor([[vocab.to_index(word) for word in ["The", "the", "THE", 'b', "B", 'a', 'A']]])
+        words = embed(words)
+        embed_0 = words[0, 0]
+        for i in range(1, 3):
+            assert torch.sum(embed_0==words[0, i]).eq(len(embed_0))
+        embed_0 = words[0, 3]
+        for i in range(3, 5):
+            assert torch.sum(embed_0 == words[0, i]).eq(len(embed_0))
+
+    @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
+    def test_same_vector3(self):
+        word_lst = ["The", "the"]
+        no_create_word_lst = ['of', 'Of', 'With', 'with']
+        vocab = Vocabulary().add_word_lst(word_lst)
+        vocab.add_word_lst(no_create_word_lst, no_create_entry=True)
+        embed = StaticEmbedding(vocab, model_dir_or_name='/remote-home/source/fastnlp_caches/glove.6B.100d/glove.demo.txt',
+                                lower=True)
+        words = torch.LongTensor([[vocab.to_index(word) for word in word_lst+no_create_word_lst]])
+        words = embed(words)
+
+        lowered_word_lst = [word.lower() for word in word_lst]
+        lowered_no_create_word_lst = [word.lower() for word in no_create_word_lst]
+        lowered_vocab = Vocabulary().add_word_lst(lowered_word_lst)
+        lowered_vocab.add_word_lst(lowered_no_create_word_lst, no_create_entry=True)
+        lowered_embed = StaticEmbedding(lowered_vocab, model_dir_or_name='/remote-home/source/fastnlp_caches/glove.6B.100d/glove.demo.txt',
+                                lower=False)
+        lowered_words = torch.LongTensor([[lowered_vocab.to_index(word) for word in lowered_word_lst+lowered_no_create_word_lst]])
+        lowered_words = lowered_embed(lowered_words)
+
+        all_words = word_lst + no_create_word_lst
+
+        for idx, (word_i, word_j) in enumerate(zip(words[0], lowered_words[0])):
+            with self.subTest(idx=idx, word=all_words[idx]):
+                assert torch.sum(word_i == word_j).eq(lowered_embed.embed_size)
+
+    @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
+    def test_same_vector4(self):
+        # words = []
+        # create_word_lst = []  # 需要创建
+        # no_create_word_lst = []
+        # ignore_word_lst = []
+        # with open('/remote-home/source/fastnlp_caches/glove.6B.100d/glove.demo.txt', 'r', encoding='utf-8') as f:
+        #     for line in f:
+        #         words
+        word_lst = ["The", "the", "the", "The", "a", "A"]
+        no_create_word_lst = ['of', 'Of', "Of", "of", 'With', 'with']
+        all_words = word_lst[:-2] + no_create_word_lst[:-2]
+        vocab = Vocabulary(min_freq=2).add_word_lst(word_lst)
+        vocab.add_word_lst(no_create_word_lst, no_create_entry=True)
+        embed = StaticEmbedding(vocab, model_dir_or_name='/remote-home/source/fastnlp_caches/glove.6B.100d/glove.demo.txt',
+                                lower=True)
+        words = torch.LongTensor([[vocab.to_index(word) for word in all_words]])
+        words = embed(words)
+
+        lowered_word_lst = [word.lower() for word in word_lst]
+        lowered_no_create_word_lst = [word.lower() for word in no_create_word_lst]
+        lowered_vocab = Vocabulary().add_word_lst(lowered_word_lst)
+        lowered_vocab.add_word_lst(lowered_no_create_word_lst, no_create_entry=True)
+        lowered_embed = StaticEmbedding(lowered_vocab, model_dir_or_name='/remote-home/source/fastnlp_caches/glove.6B.100d/glove.demo.txt',
+                                lower=False)
+        lowered_words = torch.LongTensor([[lowered_vocab.to_index(word.lower()) for word in all_words]])
+        lowered_words = lowered_embed(lowered_words)
+
+        for idx in range(len(all_words)):
+            word_i, word_j = words[0, idx], lowered_words[0, idx]
+            with self.subTest(idx=idx, word=all_words[idx]):
+                assert torch.sum(word_i == word_j).eq(lowered_embed.embed_size)
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