diff --git a/fastNLP/models/bert.py b/fastNLP/models/bert.py
index 02227c0d..4846c7fa 100644
--- a/fastNLP/models/bert.py
+++ b/fastNLP/models/bert.py
@@ -30,7 +30,7 @@ class BertConfig:
         self.hidden_size = hidden_size
         self.num_hidden_layers = num_hidden_layers
         self.num_attention_heads = num_attention_heads
-        self.intermediate = intermediate_size
+        self.intermediate_size = intermediate_size
         self.hidden_act = hidden_act
         self.hidden_dropout_prob = hidden_dropout_prob
         self.attention_probs_dropout_prob = attention_probs_dropout_prob
diff --git a/fastNLP/modules/encoder/embedding.py b/fastNLP/modules/encoder/embedding.py
index 6e7406b2..5360826a 100644
--- a/fastNLP/modules/encoder/embedding.py
+++ b/fastNLP/modules/encoder/embedding.py
@@ -157,7 +157,9 @@ class StaticEmbedding(TokenEmbedding):
             'en-glove-840b-300': 'glove.840B.300d-cc1ad5e1.tar.gz',
             'en-glove-6b-50': "glove.6B.50d-a6028c70.tar.gz",
             'en-word2vec-300': "GoogleNews-vectors-negative300-be166d9d.tar.gz",
-            'cn': "tencent_cn-dab24577.tar.gz"
+            'en-fasttext': "cc.en.300.vec-d53187b2.gz",
+            'cn': "tencent_cn-dab24577.tar.gz",
+            'cn-fasttext': "cc.zh.300.vec-d68a9bcf.gz",
         }
 
         # 得到cache_path
diff --git a/test/core/test_vocabulary.py b/test/core/test_vocabulary.py
index df0ebb1a..c161ad9d 100644
--- a/test/core/test_vocabulary.py
+++ b/test/core/test_vocabulary.py
@@ -100,13 +100,14 @@ class TestIndexing(unittest.TestCase):
         self.assertEqual(text, [vocab.to_word(idx) for idx in [vocab[w] for w in text]])
     
     def test_iteration(self):
-        vocab = Vocabulary()
+        vocab = Vocabulary(padding=None, unknown=None)
         text = ["FastNLP", "works", "well", "in", "most", "cases", "and", "scales", "well", "in",
                 "works", "well", "in", "most", "cases", "scales", "well"]
         vocab.update(text)
         text = set(text)
-        for word in vocab:
+        for word, idx in vocab:
             self.assertTrue(word in text)
+            self.assertTrue(idx < len(vocab))
 
 
 class TestOther(unittest.TestCase):
diff --git a/test/models/test_cnn_text_classification.py b/test/models/test_cnn_text_classification.py
index b83b7bad..2ea48220 100644
--- a/test/models/test_cnn_text_classification.py
+++ b/test/models/test_cnn_text_classification.py
@@ -12,7 +12,6 @@ class TestCNNText(unittest.TestCase):
         model = CNNText(init_emb,
                         NUM_CLS,
                         kernel_nums=(1, 3, 5),
-                        kernel_sizes=(2, 2, 2),
-                        padding=0,
+                        kernel_sizes=(1, 3, 5),
                         dropout=0.5)
         RUNNER.run_model_with_task(TEXT_CLS, model)
diff --git a/test/test_tutorials.py b/test/test_tutorials.py
index 128e4235..a38d5ae1 100644
--- a/test/test_tutorials.py
+++ b/test/test_tutorials.py
@@ -70,7 +70,7 @@ class TestTutorial(unittest.TestCase):
             break
 
         from fastNLP.models import CNNText
-        model = CNNText((len(vocab), 50), num_classes=5, padding=2, dropout=0.1)
+        model = CNNText((len(vocab), 50), num_classes=5, dropout=0.1)
 
         from fastNLP import Trainer
         from copy import deepcopy
@@ -143,7 +143,7 @@ class TestTutorial(unittest.TestCase):
                        is_input=True)
 
         from fastNLP.models import CNNText
-        model = CNNText((len(vocab), 50), num_classes=5, padding=2, dropout=0.1)
+        model = CNNText((len(vocab), 50), num_classes=5, dropout=0.1)
 
         from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric, Adam