New BartModel (#2745)

* Results same as fairseq
* Wrote a ton of tests
* Struggled with api signatures
* added some docs

src/transformers/__init__.py

@@ -21,6 +21,7 @@ import logging
 
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
 from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, AutoConfig
+from .configuration_bart import BartConfig
 from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
 from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
@@ -106,6 +107,7 @@ from .pipelines import (
 )
 from .tokenization_albert import AlbertTokenizer
 from .tokenization_auto import AutoTokenizer
+from .tokenization_bart import BartTokenizer
 from .tokenization_bert import BasicTokenizer, BertTokenizer, BertTokenizerFast, WordpieceTokenizer
 from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
 from .tokenization_camembert import CamembertTokenizer
@@ -204,6 +206,7 @@ if is_torch_available():
         XLMForQuestionAnsweringSimple,
         XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
     )
+    from .modeling_bart import BartForSequenceClassification, BartModel, BartForMaskedLM
     from .modeling_roberta import (
         RobertaForMaskedLM,
         RobertaModel,