diff --git a/fastNLP/core/dataset.py b/fastNLP/core/dataset.py
index 53e9bb4c..2f7bd8a2 100644
--- a/fastNLP/core/dataset.py
+++ b/fastNLP/core/dataset.py
@@ -864,9 +864,10 @@ class DataSet(object):
 
     def add_seq_len(self, field_name: str, new_field_name=Const.INPUT_LEN):
         """
-        将使用len()直接对field_name中每个元素作用，将其结果作为seqence length, 并放入seq_len这个field。
+        将使用len()直接对field_name中每个元素作用，将其结果作为sequence length, 并放入seq_len这个field。
 
         :param field_name: str.
+        :param new_field_name: str. 新的field_name
         :return:
         """
         if self.has_field(field_name=field_name):
diff --git a/fastNLP/io/file_utils.py b/fastNLP/io/file_utils.py
index 2c447e87..db4ccc45 100644
--- a/fastNLP/io/file_utils.py
+++ b/fastNLP/io/file_utils.py
@@ -120,7 +120,7 @@ DATASET_DIR = {
     # Summarization, English
     "ext-cnndm": "ext-cnndm.zip",
 
-    # Question & answer
+    # Question & answer, Chinese
     "cmrc2018": "cmrc2018.zip"
 
 }
diff --git a/fastNLP/io/loader/classification.py b/fastNLP/io/loader/classification.py
index aee661c6..12b10541 100644
--- a/fastNLP/io/loader/classification.py
+++ b/fastNLP/io/loader/classification.py
@@ -225,7 +225,7 @@ class IMDBLoader(Loader):
             shutil.rmtree(data_dir)
             data_dir = self._get_dataset_path(dataset_name=dataset_name)
         
-        if not os.path.exists(os.path.join(data_dir, 'dev.csv')):
+        if not os.path.exists(os.path.join(data_dir, 'dev.txt')):
             if dev_ratio > 0:
                 assert 0 < dev_ratio < 1, "dev_ratio should be in range (0,1)."
                 try:
diff --git a/fastNLP/models/cnn_text_classification.py b/fastNLP/models/cnn_text_classification.py
index 863c4941..d18b1e7c 100644
--- a/fastNLP/models/cnn_text_classification.py
+++ b/fastNLP/models/cnn_text_classification.py
@@ -33,7 +33,6 @@ class CNNText(torch.nn.Module):
         :param tuple(int,int),torch.FloatTensor,nn.Embedding,numpy.ndarray embed: Embedding的大小(传入tuple(int, int),
             第一个int为vocab_zie, 第二个int为embed_dim); 如果为Tensor, Embedding, ndarray等则直接使用该值初始化Embedding
         :param int num_classes: 一共有多少类
-        :param int,tuple(int) out_channels: 输出channel的数量。如果为list，则需要与kernel_sizes的数量保持一致
         :param int,tuple(int) kernel_sizes: 输出channel的kernel大小。
         :param float dropout: Dropout的大小
         """
diff --git a/fastNLP/models/snli.py b/fastNLP/models/snli.py
index 1661d191..9a9f0c80 100644
--- a/fastNLP/models/snli.py
+++ b/fastNLP/models/snli.py
@@ -164,6 +164,7 @@ class BiRNN(nn.Module):
         if self.dropout_rate > 0:
             dropout_input = F.dropout(rnn_input.data, p=self.dropout_rate, training=self.training)
             rnn_input = nn.utils.rnn.PackedSequence(dropout_input, rnn_input.batch_sizes)
+        self.rnn.flatten_parameters()
         output = self.rnn(rnn_input)[0]
         # Unpack everything
         output = nn.utils.rnn.pad_packed_sequence(output, batch_first=True)[0]