diff --git a/ernie/modeling_ernie.py b/ernie/modeling_ernie.py
index c7655c280184536e4b637a5f437da9fdc4c1e44b..b3b3bbe21c5b81899a9843beca96019262a38271 100644
--- a/ernie/modeling_ernie.py
+++ b/ernie/modeling_ernie.py
@@ -407,7 +407,7 @@ class ErnieModelForTokenClassification(ErnieModel):
         self.dropout = lambda i: L.dropout(i, dropout_prob=prob, dropout_implementation="upscale_in_train",) if self.training else i
 
     @add_docstring(ErnieModel.forward.__doc__)
-    def forward(self, *args, ignore_index=-100, labels=None, loss_weights=None, **kwargs, ):
+    def forward(self, *args, **kwargs):
         """
         Args:
             labels (optional, `Variable` of shape [batch_size, seq_len]): 
@@ -418,8 +418,14 @@ class ErnieModelForTokenClassification(ErnieModel):
                 if labels not set, returns None
             logits (`Variable` of shape [batch_size, seq_len, hidden_size]):
                 output logits of classifier
+            loss_weights (`Variable` of shape [batch_size, seq_len]):
+                weigths of loss for each tokens.
+            ignore_index (int):
+                when label == `ignore_index`, this token will not contribute to loss
         """
-
+        ignore_index = kwargs.pop('ignore_index', -100)
+        labels = kwargs.pop('labels', None)
+        loss_weights = kwargs.pop('loss_weights', None)
         pooled, encoded = super(ErnieModelForTokenClassification, self).forward(*args, **kwargs)
         hidden = self.dropout(encoded) # maybe not?
         logits = self.classifier(hidden)