diff --git a/fastNLP/core/fieldarray.py b/fastNLP/core/fieldarray.py
index 20d7e5e0..96854e72 100644
--- a/fastNLP/core/fieldarray.py
+++ b/fastNLP/core/fieldarray.py
@@ -105,6 +105,7 @@ class FieldArray(object):
                 1.1) 二维list  DataSet({"x": [[1, 2], [3, 4]]})
                 1.2) 二维array  DataSet({"x": np.array([[1, 2], [3, 4]])})
                 1.3) 三维list  DataSet({"x": [[[1, 2], [3, 4]], [[1, 2], [3, 4]]]})
+                1.4) list of array: DataSet({"x": [np.array([1,2,3]), np.array([1,2,3])]})
             2） 如果DataSet使用list of Instance 初始化,那么在append中会先对第一个样本初始化FieldArray；
             然后后面的样本使用FieldArray.append进行添加。
                 2.1) 一维list DataSet([Instance(x=[1, 2, 3, 4])])
@@ -119,10 +120,12 @@ class FieldArray(object):
         if isinstance(content, list):
             # 如果DataSet使用dict初始化, content 可能是二维list/二维array/三维list
             # 如果DataSet使用list of Instance 初始化, content可能是 [list]/[array]/[2D list]
-            if len(content) == 1 and isinstance(content[0], np.ndarray):
+            for idx, item in enumerate(content):
                 # 这是使用list of Instance 初始化时第一个样本：FieldArray(name, [field])
                 # 将[np.array] 转化为 list of list
-                content[0] = content[0].tolist()
+                # 也可以支持[array, array, array]的情况
+                if isinstance(item, np.ndarray):
+                    content[idx] = content[idx].tolist()
         elif isinstance(content, np.ndarray):
             content = content.tolist()  # convert np.ndarray into 2-D list
         else:
diff --git a/reproduction/POS_tagging/train_pos_tag.py b/reproduction/POS_tagging/train_pos_tag.py
index 4bdc23c7..6448c32b 100644
--- a/reproduction/POS_tagging/train_pos_tag.py
+++ b/reproduction/POS_tagging/train_pos_tag.py
@@ -93,7 +93,7 @@ def train(train_data_path, dev_data_path, checkpoint=None):
                                                                            target="truth",
                                                                            seq_lens="word_seq_origin_len"),
                       dev_data=dev_data, metric_key="f",
-                      use_tqdm=True, use_cuda=True, print_every=5, n_epochs=6, save_path="./save_0")
+                      use_tqdm=True, use_cuda=True, print_every=10, n_epochs=20, save_path="./save_0117")
     trainer.train(load_best_model=True)
 
     # save model & pipeline
@@ -102,14 +102,14 @@ def train(train_data_path, dev_data_path, checkpoint=None):
 
     pp = Pipeline([vocab_proc, seq_len_proc, set_input_proc, model_proc, id2tag])
     save_dict = {"pipeline": pp, "model": model, "tag_vocab": tag_proc.vocab}
-    torch.save(save_dict, "model_pp.pkl")
+    torch.save(save_dict, "model_pp_0117.pkl")
     print("pipeline saved")
 
 
 def run_test(test_path):
     test_data = ZhConllPOSReader().load(test_path)
 
-    with open("model_pp.pkl", "rb") as f:
+    with open("model_pp_0117.pkl", "rb") as f:
         save_dict = torch.load(f)
     tag_vocab = save_dict["tag_vocab"]
     pipeline = save_dict["pipeline"]
diff --git a/test/core/test_fieldarray.py b/test/core/test_fieldarray.py
index 834545c0..151d9335 100644
--- a/test/core/test_fieldarray.py
+++ b/test/core/test_fieldarray.py
@@ -31,6 +31,12 @@ class TestFieldArrayInit(unittest.TestCase):
         # 三维list
         fa = FieldArray("x", [[[1, 2], [3, 4]], [[1, 2], [3, 4]]], is_input=True)
 
+    def test_init_v7(self):
+        # list of array
+        fa = FieldArray("x", [np.array([[1, 2], [3, 4]]), np.array([[1, 2], [3, 4]])], is_input=True)
+        self.assertEqual(fa.pytype, int)
+        self.assertEqual(fa.dtype, np.int)
+
     def test_init_v4(self):
         # 一维list
         val = [1, 2, 3, 4]