fix the flake8 bug

Author: karin0018

EduNLP/I2V/i2v.py

@@ -97,11 +97,9 @@ def __call__(self, items, *args, **kwargs):
     def tokenize(self, items, *args, key=lambda x: x, **kwargs) -> list:
         """
         tokenize item
-        
         Parameter
         ----------
         items: a list of questions
-        
         Return
         ----------
         tokens: list
@@ -111,7 +109,6 @@ def tokenize(self, items, *args, key=lambda x: x, **kwargs) -> list:
     def infer_vector(self, items, key=lambda x: x, **kwargs) -> tuple:
         """
         get question embedding
-        
         NotImplemented
         """
         raise NotImplementedError
@@ -127,7 +124,6 @@ def infer_token_vector(self, tokens, *args, **kwargs) -> ...:
     def save(self, config_path):
         """
         save model weights in config_path
-        
         Parameter:
         ----------
         config_path: str

EduNLP/Vector/disenqnet/disenqnet.py

@@ -46,7 +46,6 @@ def infer_tokens(self, items: dict, **kwargs) -> torch.Tensor:
         embeded, _, _ = self(items)
         """
         get tokens embedding with DisenQModel
-        
         Parameters
         ----------
         items: dict

EduNLP/Vector/elmo_vec.py

@@ -23,7 +23,6 @@ def __call__(self, items: dict):
     def infer_vector(self, items: dict, **kwargs) -> torch.Tensor:
         """
         get sentence vector embedding with ElmoModel
-        
         Parameters
         ----------
         items: dict, {'seq_idx': tensor(),'seq_len':tensor()}, the tokens about question after tokenizer processing
@@ -41,7 +40,6 @@ def infer_vector(self, items: dict, **kwargs) -> torch.Tensor:
     def infer_tokens(self, items, **kwargs) -> torch.Tensor:
         """
         get tokens embedding with ElmoModel
-        
         Parameters
         ----------
         items: dict, {'seq_idx': tensor()}, the tokens about question after tokenizer processing

EduNLP/Vector/gensim_vec.py

@@ -68,11 +68,9 @@ def __getitem__(self, item):
     def infer_vector(self, items, agg="mean", **kwargs) -> list:
         """
         get sentence embedding with word2vec model
-        
         Parameters
         ----------
         item: list, the tokens after tokenizer processing
-        
         Return
         ------
         vector: list
@@ -85,15 +83,13 @@ def infer_vector(self, items, agg="mean", **kwargs) -> list:
     def infer_tokens(self, items, **kwargs) -> list:
         """
         get token embedding with word2vec model
-        
         Parameters
         ----------
         item: list
             the tokens after tokenizer processing
-        
         Return
         ------
-        vector: list  
+        vector: list
             [[array(), ..., array()], [...], [...]]
         """
         return [list(self(*item)) for item in items]
@@ -122,17 +118,15 @@ def __init__(self, filepath):
     def infer_vector(self, item, return_vec=False):
         """
         get Bow vector
-        
         Parameters
         ----------
         item: list
             the tokens after tokenizer processing
-        
         Return
         ------
         vector: list
             [array(), ..., array()]
-        """        
+        """
         item = self.dictionary.doc2bow(item)
         if not return_vec:
             return item  # return dic as default
@@ -161,15 +155,13 @@ def __init__(self, filepath):
     def infer_vector(self, item, return_vec=False):
         """
         get Tf-idf vector
-        
         Parameters
         ----------
         item: list
             the tokens after tokenizer processing
-        
         Return
         ------
-        vector: list 
+        vector: list
             [array(), ..., array()]
         """
         dic_item = self.dictionary.doc2bow(item)
@@ -234,12 +226,10 @@ def vector_size(self):
     def infer_vector(self, items, *args, **kwargs) -> list:
         """
         get vector with D2V model
-        
         Parameters
         ----------
-        item: list  
+        item: list
             the tokens after tokenizer processing
-        
         Return
         ------
         vector: list
@@ -250,7 +240,6 @@ def infer_vector(self, items, *args, **kwargs) -> list:
     def infer_tokens(self, item, *args, **kwargs) -> ...:
         """
         get token embeddings with D2V
-        
         NotImplemented
         """
         raise NotImplementedError