adding tests to examples - updating summary module - coverage update

pytorch_transformers/modeling_xlm.py

@@ -14,18 +14,14 @@
 # limitations under the License.
 """ PyTorch XLM model.
 """
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import json
 import logging
 import math
-import os
 import sys
 from io import open
 
-import math
 import itertools
 import numpy as np
 
@@ -34,9 +30,8 @@ from torch import nn
 from torch.nn import functional as F
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .file_utils import cached_path
-from .modeling_utils import (CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, PreTrainedModel,
-                          prune_linear_layer, SequenceSummary, SQuADHead)
+from .modeling_utils import (PretrainedConfig, PreTrainedModel,
+                             prune_linear_layer, SequenceSummary, SQuADHead)
 
 logger = logging.getLogger(__name__)
 
@@ -79,10 +74,11 @@ class XLMConfig(PretrainedConfig):
 
                  finetuning_task=None,
                  num_labels=2,
-                 summary_type='last',
+                 summary_type='first',
                  summary_use_proj=True,
-                 summary_activation='tanh',
-                 summary_dropout=0.1,
+                 summary_activation=None,
+                 summary_proj_to_labels=True,
+                 summary_first_dropout=0.1,
                  start_n_top=5,
                  end_n_top=5,
                  **kwargs):
@@ -164,7 +160,8 @@ class XLMConfig(PretrainedConfig):
             self.summary_type = summary_type
             self.summary_use_proj = summary_use_proj
             self.summary_activation = summary_activation
-            self.summary_dropout = summary_dropout
+            self.summary_proj_to_labels = summary_proj_to_labels
+            self.summary_first_dropout = summary_first_dropout
             self.start_n_top = start_n_top
             self.end_n_top = end_n_top
         else: