logging + update copyright

convert_tf_checkpoint_to_pytorch.py

 # coding=utf-8
+# Copyright 2018 The HugginFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """Convert BERT checkpoint."""
 
 from __future__ import absolute_import

extract_features.py

 # coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors.
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Extract pre-computed feature vectors from BERT."""
+"""Extract pre-computed feature vectors from a PyTorch BERT model."""
 
 from __future__ import absolute_import
 from __future__ import division

modeling.py

 # coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors.
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Common utility functions related to TensorFlow."""
+"""PyTorch BERT model."""
 
 from __future__ import absolute_import
 from __future__ import division

optimization.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch optimization for BERT model."""
+
 import math
 import torch
 from torch.optim import Optimizer

run_classifier.py

 # coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors.
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

run_squad.py

 # coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors.
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -720,22 +720,6 @@ def main():
                         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.")
 
-    ### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
-    # parser.add_argument("--use_tpu", default=False, action='store_true', help="Whether to use TPU or GPU/CPU.")
-    # parser.add_argument("--tpu_name", default=None, type=str,
-    #                     help="The Cloud TPU to use for training. This should be either the name used when creating the "
-    #                          "Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")
-    # parser.add_argument("--tpu_zone", default=None, type=str,
-    #                     help="[Optional] GCE zone where the Cloud TPU is located in. If not specified, we will attempt "
-    #                          "to automatically detect the GCE project from metadata.")
-    # parser.add_argument("--gcp_project", default=None, type=str,
-    #                     help="[Optional] Project name for the Cloud TPU-enabled project. If not specified, we will attempt "
-    #                          "to automatically detect the GCE project from metadata.")
-    # parser.add_argument("--master", default=None, type=str, help="[Optional] TensorFlow master URL.")
-    # parser.add_argument("--num_tpu_cores", default=8, type=int, help="Only used if `use_tpu` is True. "
-    #                                                                  "Total number of TPU cores to use.")
-    ### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
-
     parser.add_argument("--verbose_logging", default=False, action='store_true',
                         help="If true, all of the warnings related to data processing will be printed. "
                              "A number of warnings are expected for a normal SQuAD evaluation.")
@@ -871,7 +855,6 @@ def main():
                 loss.backward()
                 optimizer.step()
                 global_step += 1
-                logger.info("Done %s steps", global_step)
 
     if args.do_predict:
         eval_examples = read_squad_examples(
@@ -892,10 +875,8 @@ def main():
         all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
         all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
         all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
-        #all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
         all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
 
-        #eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids, all_example_index)
         eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
         if args.local_rank == -1:
             eval_sampler = SequentialSampler(eval_data)
@@ -906,7 +887,6 @@ def main():
         model.eval()
         all_results = []
         logger.info("Start evaluating")
-        #for input_ids, input_mask, segment_ids, label_ids, example_index in eval_dataloader:
         for input_ids, input_mask, segment_ids, example_index in tqdm(eval_dataloader, descr="Evaluating"):
             if len(all_results) % 1000 == 0:
                 logger.info("Processing example: %d" % (len(all_results)))
@@ -918,9 +898,7 @@ def main():
             start_logits, end_logits = model(input_ids, segment_ids, input_mask)
 
             unique_id = [int(eval_features[e.item()].unique_id) for e in example_index]
-            #start_logits = [x.item() for x in start_logits]
             start_logits = [x.view(-1).detach().cpu().numpy() for x in start_logits]
-            #end_logits = [x.item() for x in end_logits]
             end_logits = [x.view(-1).detach().cpu().numpy() for x in end_logits]
             for idx, i in enumerate(unique_id):
                 s = [float(x) for x in start_logits[idx]]
@@ -932,11 +910,6 @@ def main():
                         end_logits=e
                     )
                 )
-            # all_results.append(
-            #     RawResult(
-            #         unique_id=unique_id,
-            #         start_logits=start_logits,
-            #         end_logits=end_logits))
 
         output_prediction_file = os.path.join(args.output_dir, "predictions.json")
         output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")

tokenization.py

 # coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors.
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.