rename Bert2Bert -> Bert2Rnd

transformers/__init__.py

@@ -64,7 +64,7 @@ if is_torch_available():
                                 BertForMaskedLM, BertForNextSentencePrediction,
                                 BertForSequenceClassification, BertForMultipleChoice,
                                 BertForTokenClassification, BertForQuestionAnswering,
-                                load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, Bert2Bert)
+                                load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, Bert2Rnd)
     from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
                                 OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
                                 load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)

transformers/modeling_bert.py

@@ -1419,7 +1419,7 @@ class BertForQuestionAnswering(BertPreTrainedModel):
 @add_start_docstrings("Bert encoder-decoder model for sequence generation.",
                       BERT_START_DOCSTRING,
                       BERT_INPUTS_DOCSTRING)
-class Bert2Bert(BertPreTrainedModel):
+class Bert2Rnd(BertPreTrainedModel):
     r"""
 
     Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
@@ -1434,7 +1434,8 @@ class Bert2Bert(BertPreTrainedModel):
     Examples::
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        model = Bert2Bert.from_pretrained('bert-base-uncased')
+        model = Bert2Rnd.from_pretrained('bert-base-uncased')
+        # fine-tuning magic happens here
         input = tokenizer.encode("Hello, how are you?")
         outputs = model(input)
         output_text = tokenize.decode(outputs[0])
@@ -1468,4 +1469,4 @@ class Bert2Bert(BertPreTrainedModel):
                                        token_type_ids=token_type_ids,
                                        position_ids=position_ids,
                                        head_mask=head_mask)
-        return decoder_outputs
+        return decoder_outputs[0]

transformers/tests/modeling_bert_test.py

@@ -29,7 +29,7 @@ if is_torch_available():
     from transformers import (BertConfig, BertModel, BertForMaskedLM,
                               BertForNextSentencePrediction, BertForPreTraining,
                               BertForQuestionAnswering, BertForSequenceClassification,
-                              BertForTokenClassification, BertForMultipleChoice, Bert2Bert)
+                              BertForTokenClassification, BertForMultipleChoice, Bert2Rnd)
     from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
 else:
     pytestmark = pytest.mark.skip("Require Torch")
@@ -257,7 +257,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_bert2bert(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
             config.num_choices = self.num_choices
-            model = Bert2Bert(config=config)
+            model = Bert2Rnd(config=config)
             model.eval()
             bert2bert_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
             bert2bert_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()