diff --git a/wit/doc/meaning_dataset.md b/wit/doc/meaning_dataset.md
index 6d1fdf8..b6a47ba 100644
--- a/wit/doc/meaning_dataset.md
+++ b/wit/doc/meaning_dataset.md
@@ -14,7 +14,7 @@ meaning数据集是一个模仿自然语言，以及抽象表达的数据集。
 8. rank_idx表示当前token在不同层的排序编号，每4位表示在一层里面的编号，低4位表示最低层级的rank_idx，高位无用的位用1填充
 9. rank_all表示当前token所在的不同层的总的分支个数，每4位表示在一层里面的个数，低4位表示最低层级的rank_all，高位无用的位用1填充
 10. tree用于存储每个meaning的拆解的数据，使用字典表达一个树形结构
-11. get_seq_mask返回一个sequence每个token在对应level是不是对应的index,level=0:最底层，index=-1:最后一个，index=0:第一个
+11. get_rank_mask返回一个sequence每个token在对应level是不是对应的index,level=0:最底层，index=-1:最后一个，index=0:第一个
 12. meaning_height 当前meaning的总高度
 13. meaning_weight 当前meaning的总宽度
 14. val_mask_level val_mask_idx: 表示用于训练的token的mask,val_mask_level=[0, 1, 2] val_mask_idx=[0, 0, -1]表示只有是第0层第0个,而且是第1层第0个,第2层最后一个的token,才参与训练
diff --git a/wit/meaning/meaning_dataset.py b/wit/meaning/meaning_dataset.py
index 0059d32..8c6f501 100644
--- a/wit/meaning/meaning_dataset.py
+++ b/wit/meaning/meaning_dataset.py
@@ -429,9 +429,12 @@ class MeaningDataset(Dataset):
         output = {}
         data = torch.tensor(np.stack(data, axis=0)).long()
         output["input_ids"] = data
-        output["labels"] = data.clone()
         output["token_type_ids"] = torch.zeros(data.shape)
-        output["val_mask"] = self.get_seq_mask_tensor(idx_list)
+        val_mask, stride_mask = self.get_seq_mask_tensor(idx_list)
+        output["val_mask"] = val_mask
+        labels = data.clone()
+        labels[~stride_mask] = self.vocab_size  # set to vocab_size will be masked in label
+        output["labels"] = labels
         output["meaning"] = [self.seq_meaning[i] for i in idx_list]
         return output
 
@@ -457,27 +460,22 @@ class MeaningDataset(Dataset):
         middle = int(l * ratio)
         return self.copy(0, middle), self.copy(middle, l)
 
-    def get_seq_mask(self, idx, level, index):
+    def get_rank_mask(self, idx, level, index):
         # assert index < 15, "index must < 15"
         # assert level < 8, "level must < 8"
         rank_idx = (self.rank_idx[idx] >> (4 * level)).astype(np.int32) & 0xF
         rank_all = (self.rank_all[idx] >> (4 * level)).astype(np.int32) & 0xF
-        return (rank_idx == (rank_all + index if index < 0 else index)) & (self.level[idx] < 255)
+        return rank_idx == (rank_all + index if index < 0 else index)
 
     def get_seq_mask_tensor(self, idx_list):
-        if self.val_mask_level is not None and self.val_mask_idx is not None:
-            mask = torch.tensor(
-                np.stack(
-                    [self.get_seq_mask(idx, self.val_mask_level[0], self.val_mask_idx[0]) for idx in idx_list], axis=0
+        stride_mask = torch.tensor(np.stack([(self.level[idx] < 255) for idx in idx_list], axis=0))
+        val_mask = stride_mask.clone()
+        if self.val_mask_level and self.val_mask_idx:
+            for i, l in enumerate(self.val_mask_level):
+                val_mask = val_mask & torch.tensor(
+                    np.stack([self.get_rank_mask(idx, l, self.val_mask_idx[i]) for idx in idx_list], axis=0)
                 )
-            )
-            for i, l in enumerate(self.val_mask_level[1:]):
-                mask = mask & torch.tensor(
-                    np.stack([self.get_seq_mask(idx, l, self.val_mask_idx[i + 1]) for idx in idx_list], axis=0)
-                )
-            return mask
-        else:
-            return torch.tensor(np.stack([(self.level[idx] < 255) for idx in idx_list], axis=0))
+        return (val_mask, stride_mask)
 
 
 class BatchGroupMeaningDataloader(Dataset):
@@ -586,13 +584,13 @@ if __name__ == "__main__":
 
     print(md.rank_idx[920])
     print(md.rank_all[920])
-    mask = md.get_seq_mask(920, 0, -1)
+    mask = md.get_rank_mask(920, 0, -1)
     print(mask)
-    mask = md.get_seq_mask(920, 1, 0)
+    mask = md.get_rank_mask(920, 1, 0)
     print(mask)
-    mask = md.get_seq_mask(920, 1, -1)
+    mask = md.get_rank_mask(920, 1, -1)
     print(mask)
-    mask = md.get_seq_mask(920, 1, 1)
+    mask = md.get_rank_mask(920, 1, 1)
     print(mask)
 
     md = MeaningDataset(100000, 115200, vocab_size=1024, size=1024, min_subitem=2, use_cache=False)