diff --git a/fastNLP/embeddings/bert_embedding.py b/fastNLP/embeddings/bert_embedding.py
index 963ba04c..afba9d13 100644
--- a/fastNLP/embeddings/bert_embedding.py
+++ b/fastNLP/embeddings/bert_embedding.py
@@ -306,11 +306,8 @@ class _WordBertModel(nn.Module):
                 raise RuntimeError("After split words into word pieces, the lengths of word pieces are longer than the "
                                    f"maximum allowed sequence length:{self._max_position_embeddings} of bert.")
 
-
         # +2是由于需要加入[CLS]与[SEP]
         word_pieces = words.new_full((batch_size, max_word_piece_length+2), fill_value=self._wordpiece_pad_index)
-        word_pieces[:, 0].fill_(self._cls_index)
-        batch_indexes = torch.arange(batch_size).to(words)
         attn_masks = torch.zeros_like(word_pieces)
         # 1. 获取words的word_pieces的id，以及对应的span范围
         word_indexes = words.tolist()
@@ -319,8 +316,11 @@ class _WordBertModel(nn.Module):
             if self.auto_truncate and len(word_pieces_i)>self._max_position_embeddings-2:
                 word_pieces_i = word_pieces_i[:self._max_position_embeddings-2]
             word_pieces[i, 1:len(word_pieces_i)+1] = torch.LongTensor(word_pieces_i)
-            word_pieces[i, len(word_pieces_i)+1] = self._sep_index  # 补上sep
             attn_masks[i, :word_pieces_lengths[i]+2].fill_(1)
+        # 添加[cls]和[sep]
+        word_pieces[:, 0].fill_(self._cls_index)
+        batch_indexes = torch.arange(batch_size).to(words)
+        word_pieces[batch_indexes, word_pieces_lengths+1] = self._sep_index
         # 2. 获取hidden的结果，根据word_pieces进行对应的pool计算
         # all_outputs: [batch_size x max_len x hidden_size, batch_size x max_len x hidden_size, ...]
         bert_outputs, pooled_cls = self.encoder(word_pieces, token_type_ids=None, attention_mask=attn_masks,