diff --git a/fastNLP/core/loss.py b/fastNLP/core/loss.py
index ce388989..093b3b96 100644
--- a/fastNLP/core/loss.py
+++ b/fastNLP/core/loss.py
@@ -5,7 +5,7 @@ def squash(predict , truth , **kwargs):
 
 	:param predict	: Tensor, model output
 	:param truth	: Tensor, truth from dataset
-	:param **kwargs 	: extract arguments
+	:param **kwargs : extra arguments
 
 	:return predict , truth: predict & truth after processing
 	'''
@@ -18,8 +18,8 @@ def unpad(predict , truth , **kwargs):
 
 	:param predict	: Tensor, [batch_size , max_len , tag_size]
 	:param truth	: Tensor, [batch_size , max_len]
-	:param **kwargs 	: extract arguments, kwargs["lens"] is expected to be exsist
-		arg["lens"] : list or LongTensor, [batch_size]
+	:param **kwargs : extra arguments, kwargs["lens"] is expected to be exsist
+		kwargs["lens"] : list or LongTensor, [batch_size]
 					  the i-th element is true lengths of i-th sequence  
 	
 	:return predict , truth: predict & truth after processing
@@ -39,8 +39,8 @@ def unpad_mask(predict , truth , **kwargs):
 
 	:param predict	: Tensor, [batch_size , max_len , tag_size]
 	:param truth	: Tensor, [batch_size , max_len]
-	:param **kwargs 	: extract arguments, kwargs["lens"] is expected to be exsist
-		arg["lens"] : list or LongTensor, [batch_size]
+	:param **kwargs : extra arguments, kwargs["lens"] is expected to be exsist
+		kwargs["lens"] : list or LongTensor, [batch_size]
 					  the i-th element is true lengths of i-th sequence  
 	
 	:return predict , truth: predict & truth after processing
@@ -56,8 +56,8 @@ def mask(predict , truth , **kwargs):
 
 	:param predict	: Tensor, [batch_size , max_len , tag_size]
 	:param truth	: Tensor, [batch_size , max_len]
-	:param **kwargs 	: extract arguments, kwargs["mask"] is expected to be exsist
-		arg["mask"] : ByteTensor, [batch_size , max_len]
+	:param **kwargs : extra arguments, kwargs["mask"] is expected to be exsist
+		kwargs["mask"] : ByteTensor, [batch_size , max_len]
 					  the mask Tensor , the position that is 1 will be selected
 	
 	:return predict , truth: predict & truth after processing
@@ -112,7 +112,6 @@ loss_function_name = {
 	"MarginRankingLoss".lower() : torch.nn.MarginRankingLoss,
 	"TripletMarginLoss".lower() : torch.nn.TripletMarginLoss,
 	"HingeEmbeddingLoss".lower() : torch.nn.HingeEmbeddingLoss,
-	"HingeEmbeddingLoss".lower() : torch.nn.HingeEmbeddingLoss,
 	"CosineEmbeddingLoss".lower() : torch.nn.CosineEmbeddingLoss,
 	"MultiLabelMarginLoss".lower() : torch.nn.MultiLabelMarginLoss,
 	"MultiLabelSoftMarginLoss".lower() : torch.nn.MultiLabelSoftMarginLoss,
@@ -132,7 +131,7 @@ class Loss(object):
 
 		pre_pro funcsions should have three arguments: predict, truth, **arg
 			predict and truth is the necessary parameters in loss function
-			arg is the extra parameters passed-in when calling loss function
+			kwargs is the extra parameters passed-in when calling loss function
 		pre_pro functions should return two objects, respectively predict and truth that after processed
 
 		'''