diff --git a/wit/meaning_dataset.py b/wit/meaning_dataset.py
index ada1531..4eb658a 100644
--- a/wit/meaning_dataset.py
+++ b/wit/meaning_dataset.py
@@ -40,7 +40,7 @@ class MeaningMap:
         else:
             print("Disk cache miss, build new one.")
 
-            map = np.empty((size, max_subitem), dtype=np.uint32)
+            map = np.empty((size, max_subitem), dtype=np.int32)
 
             index = np.arange(0, size)
             map = np.random.random((size, max_subitem))
@@ -53,13 +53,12 @@ class MeaningMap:
 
             item_sum = map.sum(axis=1)
             scale = (index / item_sum).reshape(-1, 1).repeat(max_subitem, axis=1)
-            map = map * scale
+            map = (map * scale).astype(np.int32)
 
-            map[mask_zero] = 0
+            map[mask_zero] = -1
 
             map[:vocab_size, 0] = np.arange(0, vocab_size)
-            map[:vocab_size, 1:] = 0
-            map = map.astype(np.uint32)
+            map[:vocab_size, 1:] = -1
 
             ms_data = []  # meaning sequence
             ms_level = []  # meaning level, vocab's level is 0
@@ -70,33 +69,42 @@ class MeaningMap:
             ms_weight = []  # meaning tree weight
             index = 0
             for i in range(self.vocab_size):
-                ms_data.append([i])
-                ms_level.append([0])
-                ms_idx.append([0])
+                ms_data.append(np.array([i]))
+                ms_level.append(np.array([0]))
+                ms_idx.append(np.array([0]))
                 ms_start.append(index)
                 ms_len.append(1)
-                index = index + 1
                 ms_height.append(0)
                 ms_weight.append(1)
+                index = index + 1
 
             for i in range(self.vocab_size, size):
                 m = map[i]
-                m = m[m > 0]
-                ma = []
-                ml = []
-                mi = []
-                for i, newm in enumerate(m.tolist()):
-                    ma = ma + ms_data[newm]
-                    ml = ml + [x + 1 for x in ms_level[newm]]
-                    mi = mi + ([0xFFFFFFF0 + i] if newm < self.vocab_size else [n * 16 + i for n in ms_idx[newm]])
+                m = m[m >= 0]
+
+                m_list = m.tolist()
+                assert m_list, "map list can not be empty list"
+
+                ma = np.concatenate([ms_data[newm] for newm in m_list])
+                ml = np.concatenate([ms_level[newm] + 1 for newm in m_list])
+                mi = np.concatenate(
+                    [
+                        ([0xFFFFFFF0 + i] if newm < self.vocab_size else ms_idx[newm] * 16 + i)
+                        for i, newm in enumerate(m_list)
+                    ]
+                )
+                ml = ml[ma > 0]
+                mi = mi[ma > 0]
+                ma = ma[ma > 0]
+
                 ms_data.append(ma)
-                ms_start.append(index)
-                ms_len.append(len(ma))
                 ms_level.append(ml)
                 ms_idx.append(mi)
+                ms_start.append(index)
+                ms_len.append(len(ma))
+                ms_height.append(max([ms_height[sub_m] for sub_m in m_list]) + 1)
+                ms_weight.append(sum(ms_weight[sub_m] for sub_m in m_list))
                 index = index + len(ma)
-                ms_height.append(max([-1] + [ms_height[sub_m] for sub_m in m.tolist()]) + 1)
-                ms_weight.append(sum(ms_weight[sub_m] for sub_m in m.tolist()))
 
             # offsets = [0, 0, 4, 8, 12, 16, 20, 24, 28]
             # for idxmi, mi in enumerate(ms_idx):
@@ -126,11 +134,10 @@ class MeaningMap:
             )
             ms_idx = ((ms_idx >> ((8 - ms_level) * 4)) + d).astype(np.uint32)
 
-            ms_start = np.array(ms_start).astype(np.uint32)
-            ms_height = np.array(ms_height).astype(np.uint32)
-            ms_weight = np.array(ms_weight).astype(np.uint32)
-            ms_len = np.array(ms_len).astype(np.uint32)
-            ms_map = map.astype(np.uint32)
+            ms_start = np.array(ms_start).astype(np.int32)
+            ms_height = np.array(ms_height).astype(np.int32)
+            ms_weight = np.array(ms_weight).astype(np.int32)
+            ms_len = np.array(ms_len).astype(np.int32)
 
             slhwm = np.concatenate(
                 (
@@ -138,7 +145,7 @@ class MeaningMap:
                     ms_len.reshape((-1, 1)),
                     ms_height.reshape((-1, 1)),
                     ms_weight.reshape((-1, 1)),
-                    ms_map,
+                    map,
                 ),
                 axis=1,
             )
@@ -383,7 +390,7 @@ class BatchGroupMeaningDataloader(Dataset):
 
 if __name__ == "__main__":
 
-    md = MeaningDataset(100000, 115200, vocab_size=1024, size=1024, use_cache=False)
+    md = MeaningDataset(100000, 115200, vocab_size=128, size=1024, use_cache=False)
     train, val = md.split(0.95)
     fdaf = md.__getitem__(920)
     print(md.print_tree(920))