diff --git a/fastNLP/embeddings/bert_embedding.py b/fastNLP/embeddings/bert_embedding.py
index 17f6769d..05351cbd 100644
--- a/fastNLP/embeddings/bert_embedding.py
+++ b/fastNLP/embeddings/bert_embedding.py
@@ -420,11 +420,11 @@ class _WordBertModel(nn.Module):
         if self.pool_method == 'first':
             batch_word_pieces_cum_length = batch_word_pieces_cum_length[:, :seq_len.max()]
             batch_word_pieces_cum_length.masked_fill_(batch_word_pieces_cum_length.ge(word_piece_length), 0)
-            batch_indexes = batch_indexes[:, None].expand((batch_size, batch_word_pieces_cum_length.size(1)))
+            _batch_indexes = batch_indexes[:, None].expand((batch_size, batch_word_pieces_cum_length.size(1)))
         elif self.pool_method == 'last':
             batch_word_pieces_cum_length = batch_word_pieces_cum_length[:, 1:seq_len.max()+1] - 1
             batch_word_pieces_cum_length.masked_fill_(batch_word_pieces_cum_length.ge(word_piece_length), 0)
-            batch_indexes = batch_indexes[:, None].expand((batch_size, batch_word_pieces_cum_length.size(1)))
+            _batch_indexes = batch_indexes[:, None].expand((batch_size, batch_word_pieces_cum_length.size(1)))
 
         for l_index, l in enumerate(self.layers):
             output_layer = bert_outputs[l]
@@ -437,12 +437,12 @@ class _WordBertModel(nn.Module):
             # 从word_piece collapse到word的表示
             truncate_output_layer = output_layer[:, 1:-1]  # 删除[CLS]与[SEP] batch_size x len x hidden_size
             if self.pool_method == 'first':
-                tmp = truncate_output_layer[batch_indexes, batch_word_pieces_cum_length]
+                tmp = truncate_output_layer[_batch_indexes, batch_word_pieces_cum_length]
                 tmp = tmp.masked_fill(word_mask[:, :batch_word_pieces_cum_length.size(1), None].eq(0), 0)
                 outputs[l_index, :, s_shift:batch_word_pieces_cum_length.size(1)+s_shift] = tmp
 
             elif self.pool_method == 'last':
-                tmp = truncate_output_layer[batch_indexes, batch_word_pieces_cum_length]
+                tmp = truncate_output_layer[_batch_indexes, batch_word_pieces_cum_length]
                 tmp = tmp.masked_fill(word_mask[:, :batch_word_pieces_cum_length.size(1), None].eq(0), 0)
                 outputs[l_index, :, s_shift:batch_word_pieces_cum_length.size(1)+s_shift] = tmp
             elif self.pool_method == 'max':