diff --git a/reproduction/seqence_labelling/ner/model/dilated_cnn.py b/reproduction/seqence_labelling/ner/model/dilated_cnn.py
new file mode 100644
index 00000000..cd2fa64b
--- /dev/null
+++ b/reproduction/seqence_labelling/ner/model/dilated_cnn.py
@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from fastNLP.modules.decoder import ConditionalRandomField
+from fastNLP.modules.encoder import Embedding
+from fastNLP.core.utils import seq_len_to_mask
+from fastNLP.core.const import Const as C
+
+
+class IDCNN(nn.Module):
+    def __init__(self, init_embed, char_embed,
+                 num_cls,
+                 repeats, num_layers, num_filters, kernel_size,
+                 use_crf=False, use_projection=False, block_loss=False,
+                 input_dropout=0.3, hidden_dropout=0.2, inner_dropout=0.0):
+        super(IDCNN, self).__init__()
+        self.word_embeddings = Embedding(init_embed)
+        self.char_embeddings = Embedding(char_embed)
+        embedding_size = self.word_embeddings.embedding_dim + \
+            self.char_embeddings.embedding_dim
+
+        self.conv0 = nn.Sequential(
+            nn.Conv1d(in_channels=embedding_size,
+                      out_channels=num_filters,
+                      kernel_size=kernel_size,
+                      stride=1, dilation=1,
+                      padding=kernel_size//2,
+                      bias=True),
+            nn.ReLU(),
+        )
+
+        block = []
+        for layer_i in range(num_layers):
+            dilated = 2 ** layer_i
+            block.append(nn.Conv1d(
+                in_channels=num_filters,
+                out_channels=num_filters,
+                kernel_size=kernel_size,
+                stride=1, dilation=dilated,
+                padding=(kernel_size//2) * dilated,
+                bias=True))
+            block.append(nn.ReLU())
+        self.block = nn.Sequential(*block)
+
+        if use_projection:
+            self.projection = nn.Sequential(
+                nn.Conv1d(
+                    in_channels=num_filters,
+                    out_channels=num_filters//2,
+                    kernel_size=1,
+                    bias=True),
+                nn.ReLU(),)
+            encode_dim = num_filters // 2
+        else:
+            self.projection = None
+            encode_dim = num_filters
+
+        self.input_drop = nn.Dropout(input_dropout)
+        self.hidden_drop = nn.Dropout(hidden_dropout)
+        self.inner_drop = nn.Dropout(inner_dropout)
+        self.repeats = repeats
+        self.out_fc = nn.Conv1d(
+            in_channels=encode_dim,
+            out_channels=num_cls,
+            kernel_size=1,
+            bias=True)
+        self.crf = ConditionalRandomField(
+            num_tags=num_cls) if use_crf else None
+        self.block_loss = block_loss
+
+    def forward(self, words, chars, seq_len, target=None):
+        e1 = self.word_embeddings(words)
+        e2 = self.char_embeddings(chars)
+        x = torch.cat((e1, e2), dim=-1) # b,l,h
+        mask = seq_len_to_mask(seq_len)
+
+        x = x.transpose(1, 2) # b,h,l
+        last_output = self.conv0(x)
+        output = []
+        for repeat in range(self.repeats):
+            last_output = self.block(last_output)
+            hidden = self.projection(last_output) if self.projection is not None else last_output
+            output.append(self.out_fc(hidden))
+
+        def compute_loss(y, t, mask):
+            if self.crf is not None and target is not None:
+                loss = self.crf(y, t, mask)
+            else:
+                t.masked_fill_(mask == 0, -100)
+                loss = F.cross_entropy(y, t, ignore_index=-100)
+            return loss
+
+        if self.block_loss:
+            losses = [compute_loss(o, target, mask) for o in output]
+            loss = sum(losses)
+        else:
+            loss = compute_loss(output[-1], target, mask)
+
+        scores = output[-1]
+        if self.crf is not None:
+            pred = self.crf.viterbi_decode(scores, target, mask)
+        else:
+            pred = scores.max(1)[1] * mask.long()
+
+        return {
+            C.LOSS: loss,
+            C.OUTPUT: pred,
+        }
+
+    def predict(self, words, chars, seq_len):
+        return self.forward(words, chars, seq_len)[C.OUTPUT]