diff --git a/fastNLP/core/dataset.py b/fastNLP/core/dataset.py
index e626ff26..c6f0de35 100644
--- a/fastNLP/core/dataset.py
+++ b/fastNLP/core/dataset.py
@@ -27,7 +27,7 @@ class DataSet(object):
             for ins in ins_list:
                 self.append(ins)
         else:
-            self.append(ins)
+            self.append(ins_list)
 
     def append(self, ins):
         # no field
@@ -40,6 +40,10 @@ class DataSet(object):
                 assert name in self.field_arrays
                 self.field_arrays[name].append(field)
 
+    def add_field(self, name, fields):
+        assert len(self) == len(fields)
+        self.field_arrays[name] = fields
+
     def get_fields(self):
         return self.field_arrays
 
diff --git a/fastNLP/core/field.py b/fastNLP/core/field.py
index 5b9c1b63..cf34abf8 100644
--- a/fastNLP/core/field.py
+++ b/fastNLP/core/field.py
@@ -39,35 +39,6 @@ class TextField(Field):
         super(TextField, self).__init__(text, is_target)
 
 
-class IndexField(Field):
-    def __init__(self, name, content, vocab, is_target):
-        super(IndexField, self).__init__(name, is_target)
-        self.content = []
-        self.padding_idx = vocab.padding_idx
-        for sent in content:
-            idx = vocab.index_sent(sent)
-            if isinstance(idx, list):
-                idx = torch.Tensor(idx)
-            elif isinstance(idx, np.array):
-                idx = torch.from_numpy(idx)
-            elif not isinstance(idx, torch.Tensor):
-                raise ValueError
-            self.content.append(idx)
-
-    def to_tensor(self, id_list, sort_within_batch=False):
-        max_len = max(id_list)
-        batch_size = len(id_list)
-        tensor = torch.full((batch_size, max_len), self.padding_idx, dtype=torch.long)
-        len_list = [(i, self.content[i].size(0)) for i in id_list]
-        if sort_within_batch:
-            len_list = sorted(len_list, key=lambda x: x[1], reverse=True)
-        for i, (idx, length) in enumerate(len_list):
-            if length == max_len:
-                tensor[i] = self.content[idx]
-            else:
-                tensor[i][:length] = self.content[idx]
-        return tensor
-
 class LabelField(Field):
     """The Field representing a single label. Can be a string or integer.
 
diff --git a/fastNLP/core/fieldarray.py b/fastNLP/core/fieldarray.py
index 9710f991..9d0f8e9e 100644
--- a/fastNLP/core/fieldarray.py
+++ b/fastNLP/core/fieldarray.py
@@ -4,36 +4,24 @@ import numpy as np
 class FieldArray(object):
     def __init__(self, name, content, padding_val=0, is_target=True, need_tensor=True):
         self.name = name
-        self.data = [self._convert_np(val) for val in content]
+        self.content = content
         self.padding_val = padding_val
         self.is_target = is_target
         self.need_tensor = need_tensor
 
-    def _convert_np(self, val):
-        if not isinstance(val, np.array):
-            return np.array(val)
-        else:
-            return val
-
     def append(self, val):
-        self.data.append(self._convert_np(val))
+        self.content.append(val)
 
     def get(self, idxes):
         if isinstance(idxes, int):
-            return self.data[idxes]
-        elif isinstance(idxes, list):
-            id_list = np.array(idxes)
-        batch_size = len(id_list)
-        len_list = [(i, self.data[i].shape[0]) for i in id_list]
-        _, max_len = max(len_list, key=lambda x: x[1])
+            return self.content[idxes]
+        batch_size = len(idxes)
+        max_len = max([len(self.content[i]) for i in idxes])
         array = np.full((batch_size, max_len), self.padding_val, dtype=np.int32)
 
-        for i, (idx, length) in enumerate(len_list):
-            if length == max_len:
-                array[i] = self.data[idx]
-            else:
-                array[i][:length] = self.data[idx]
+        for i, idx in enumerate(idxes):
+            array[i][:len(self.content[idx])] = self.content[idx]
         return array
 
     def __len__(self):
-        return len(self.data)
+        return len(self.content)