revise

scripts/pretraining/run_electra.py

@@ -72,9 +72,10 @@ def parse_args():
                         help='If set, both training and dev samples are generated on-the-fly '
                              'from raw texts instead of pre-processed npz files. ')
     parser.add_argument("--short_seq_prob", type=float, default=0.05,
-                        help="The probability of sampling sequences shorter than the max_seq_length.")
+                        help='The probability of sampling sequences '
+                             'shorter than the max_seq_length.')
     parser.add_argument("--cached_file_path", default=None,
-                        help="Directory for saving preprocessed features")
+                        help='Directory for saving preprocessed features')
     parser.add_argument('--circle_length', type=int, default=2,
                         help='Number of files to be read for a single GPU at the same time.')
     parser.add_argument('--repeat', type=int, default=8,

scripts/question_answering/run_squad.py

@@ -33,8 +33,9 @@
 
 
 def parse_args():
-    parser = argparse.ArgumentParser(description='Question Answering example.'
-                                                 ' We fine-tune the pretrained model on SQuAD dataset.')
+    parser = argparse.ArgumentParser(
+        description='Question Answering example. '
+                    'We fine-tune the pretrained model on SQuAD dataset.')
     parser.add_argument('--model_name', type=str, default='google_albert_base_v2',
                         help='Name of the pretrained model.')
     parser.add_argument('--do_train', action='store_true',
@@ -80,7 +81,8 @@ def parse_args():
                         help='warmup steps. Note that either warmup_steps or warmup_ratio is set.')
     parser.add_argument('--wd', type=float, default=0.01, help='weight decay')
     parser.add_argument('--layerwise_decay', type=float, default=-1, help='Layer-wise lr decay')
-    parser.add_argument('--untunable_depth', type=float, default=-1, help='Depth of untunable parameters')
+    parser.add_argument('--untunable_depth', type=float, default=-1,
+                        help='Depth of untunable parameters')
     parser.add_argument('--classifier_dropout', type=float, default=0.1,
                         help='dropout of classifier')
     # Data pre/post processing
@@ -108,15 +110,16 @@ def parse_args():
                              'to a start position')
     parser.add_argument('--n_best_size', type=int, default=20, help='Top N results written to file')
     parser.add_argument('--max_answer_length', type=int, default=30,
-                        help='The maximum length of an answer that can be generated. This is needed '
-                             'because the start and end predictions are not conditioned on one another.'
-                             ' default is 30')
+                        help='The maximum length of an answer that can be generated. This is '
+                             'needed because the start and end predictions are not conditioned '
+                             'on one another. default is 30')
     parser.add_argument('--param_checkpoint', type=str, default=None,
                         help='The parameter checkpoint for evaluating the model')
     parser.add_argument('--backbone_path', type=str, default=None,
                         help='The parameter checkpoint of backbone model')
     parser.add_argument('--all_evaluate', action='store_true',
-                        help='Whether to evaluate all intermediate checkpoints instead of only last one')
+                        help='Whether to evaluate all intermediate checkpoints '
+                             'instead of only last one')
     parser.add_argument('--max_saved_ckpt', type=int, default=10,
                         help='The maximum number of saved checkpoints')
     args = parser.parse_args()
@@ -326,7 +329,11 @@ def get_network(model_name,
     backbone_params_path = backbone_path if backbone_path else download_params_path
     if checkpoint_path is None:
         # TODO(zheyuye), be careful of allow_missing that used to pass the mlm parameters in roberta
-        backbone.load_parameters(backbone_params_path, ignore_extra=True, allow_missing=True, ctx=ctx_l)
+        backbone.load_parameters(
+            backbone_params_path,
+            ignore_extra=True,
+            allow_missing=True,
+            ctx=ctx_l)
         num_params, num_fixed_params = count_parameters(backbone.collect_params())
         logging.info(
             'Loading Backbone Model from {}, with total/fixd parameters={}/{}'.format(
@@ -710,7 +717,7 @@ def predict_extended(original_feature,
             # TODO investigate the impact
             token_max_context_score[i, j] = min(j - chunk_start,
                                                 chunk_start + chunk_length - 1 - j) \
-                                            + 0.01 * chunk_length
+                + 0.01 * chunk_length
     token_max_chunk_id = token_max_context_score.argmax(axis=0)
 
     for chunk_id, (result, chunk_feature) in enumerate(zip(results, chunked_features)):