diff --git a/README.md b/README.md
index 6b2254b5..a38771ee 100644
--- a/README.md
+++ b/README.md
@@ -30,6 +30,7 @@ A typical fastNLP routine is composed of four phases: loading dataset, pre-proce
 from fastNLP.models.base_model import BaseModel
 from fastNLP.modules import encoder
 from fastNLP.modules import aggregation
+from fastNLP.modules import decoder
 
 from fastNLP.loader.dataset_loader import ClassDatasetLoader
 from fastNLP.loader.preprocess import ClassPreprocess
@@ -42,20 +43,20 @@ class ClassificationModel(BaseModel):
     Simple text classification model based on CNN.
     """
 
-    def __init__(self, class_num, vocab_size):
+    def __init__(self, num_classes, vocab_size):
         super(ClassificationModel, self).__init__()
 
-        self.embed = encoder.Embedding(nums=vocab_size, dims=300)
-        self.conv = encoder.Conv(
+        self.emb = encoder.Embedding(nums=vocab_size, dims=300)
+        self.enc = encoder.Conv(
             in_channels=300, out_channels=100, kernel_size=3)
-        self.pool = aggregation.MaxPool()
-        self.output = encoder.Linear(input_size=100, output_size=class_num)
+        self.agg = aggregation.MaxPool()
+        self.dec = decoder.MLP(100, num_classes=num_classes)
 
     def forward(self, x):
-        x = self.embed(x)  # [N,L] -> [N,L,C]
-        x = self.conv(x)  # [N,L,C_in] -> [N,L,C_out]
-        x = self.pool(x)  # [N,L,C] -> [N,C]
-        x = self.output(x)  # [N,C] -> [N, N_class]
+        x = self.emb(x)  # [N,L] -> [N,L,C]
+        x = self.enc(x)  # [N,L,C_in] -> [N,L,C_out]
+        x = self.agg(x)  # [N,L,C] -> [N,C]
+        x = self.dec(x)  # [N,C] -> [N, N_class]
         return x
 
 
@@ -75,7 +76,7 @@ model_args = {
     'num_classes': n_classes,
     'vocab_size': vocab_size
 }
-model = ClassificationModel(class_num=n_classes, vocab_size=vocab_size)
+model = ClassificationModel(num_classes=n_classes, vocab_size=vocab_size)
 
 # train model
 train_args = {
diff --git a/test/readme_example.py b/test/readme_example.py
index 03cae2e6..17ac92c2 100644
--- a/test/readme_example.py
+++ b/test/readme_example.py
@@ -13,6 +13,7 @@ from fastNLP.loader.dataset_loader import ClassDatasetLoader
 from fastNLP.models.base_model import BaseModel
 from fastNLP.modules import aggregation
 from fastNLP.modules import encoder
+from fastNLP.modules import decoder
 
 
 class ClassificationModel(BaseModel):
@@ -20,20 +21,20 @@ class ClassificationModel(BaseModel):
     Simple text classification model based on CNN.
     """
 
-    def __init__(self, class_num, vocab_size):
+    def __init__(self, num_classes, vocab_size):
         super(ClassificationModel, self).__init__()
 
-        self.embed = encoder.Embedding(nums=vocab_size, dims=300)
-        self.conv = encoder.Conv(
+        self.emb = encoder.Embedding(nums=vocab_size, dims=300)
+        self.enc = encoder.Conv(
             in_channels=300, out_channels=100, kernel_size=3)
-        self.pool = aggregation.MaxPool()
-        self.output = encoder.Linear(input_size=100, output_size=class_num)
+        self.agg = aggregation.MaxPool()
+        self.dec = decoder.MLP(100, num_classes=num_classes)
 
     def forward(self, x):
-        x = self.embed(x)  # [N,L] -> [N,L,C]
-        x = self.conv(x)  # [N,L,C_in] -> [N,L,C_out]
-        x = self.pool(x)  # [N,L,C] -> [N,C]
-        x = self.output(x)  # [N,C] -> [N, N_class]
+        x = self.emb(x)  # [N,L] -> [N,L,C]
+        x = self.enc(x)  # [N,L,C_in] -> [N,L,C_out]
+        x = self.agg(x)  # [N,L,C] -> [N,C]
+        x = self.dec(x)  # [N,C] -> [N, N_class]
         return x
 
 
@@ -55,7 +56,7 @@ model_args = {
     'num_classes': n_classes,
     'vocab_size': vocab_size
 }
-model = ClassificationModel(class_num=n_classes, vocab_size=vocab_size)
+model = ClassificationModel(num_classes=n_classes, vocab_size=vocab_size)
 
 # train model
 train_args = {
@@ -75,4 +76,4 @@ trainer.cross_validate(model)
 # predict using model
 data_infer = [x[0] for x in data]
 infer = ClassificationInfer(data_dir)
-labels_pred = infer.predict(model, data_infer)
+labels_pred = infer.predict(model, data_infer)
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