clean up

run_classifier.py

@@ -69,7 +69,7 @@ class InputFeatures(object):
         self.input_mask = input_mask
         self.segment_ids = segment_ids
         self.label_id = label_id
-        
+
 
 class DataProcessor(object):
     """Base class for data converters for sequence classification data sets."""
@@ -95,8 +95,8 @@ class DataProcessor(object):
             for line in reader:
                 lines.append(line)
             return lines
-    
-            
+
+
 class MrpcProcessor(DataProcessor):
     """Processor for the MRPC data set (GLUE version)."""
 
@@ -190,10 +190,9 @@ class ColaProcessor(DataProcessor):
             examples.append(
                 InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
         return examples
-        
-        
-def convert_examples_to_features(examples, label_list, max_seq_length,
-                                                                 tokenizer):
+
+
+def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
     """Loads a data file into a list of `InputBatch`s."""
 
     label_map = {}
@@ -380,7 +379,7 @@ def main():
     parser.add_argument("--do_lower_case",
                         default=False,
                         action='store_true',
-                        help="Whether to lower case the input text. Should be True for uncased models and False for cased models.")
+                        help="Whether to lower case the input text. True for uncased models, False for cased models.")
     parser.add_argument("--max_seq_length",
                         default=128,
                         type=int,
@@ -424,6 +423,10 @@ def main():
                         default=False,
                         action='store_true',
                         help="Whether not to use CUDA when available")
+    parser.add_argument("--accumulate_gradients",
+                        type=int,
+                        default=1,
+                        help="Number of steps to accumulate gradient on (divide the single step batch_size)")
     parser.add_argument("--local_rank",
                         type=int,
                         default=-1,
@@ -448,12 +451,12 @@ def main():
         n_gpu = 1
         # print("Initializing the distributed backend: NCCL")
     print("device", device, "n_gpu", n_gpu)
-    
+
     random.seed(args.seed)
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
     if n_gpu>0: torch.cuda.manual_seed_all(args.seed)
-    
+
     if not args.do_train and not args.do_eval:
         raise ValueError("At least one of `do_train` or `do_eval` must be True.")