Trainer (#3800)

* doc

* [tests] Add sample files for a regression task

* [HUGE] Trainer

* Feedback from @sshleifer

* Feedback from @thomwolf + logging tweak

* [file_utils] when downloading concurrently, get_from_cache will use the cached file for subsequent processes

* [glue] Use default max_seq_length of 128 like before

* [glue] move DataTrainingArguments around

* [ner] Change interface of InputExample, and align run_{tf,pl}

* Re-align the pl scripts a little bit

* ner

* [ner] Add integration test

* Fix language_modeling with API tweak

* [ci] Tweak loss target

* Don't break console output

* amp.initialize: model must be on right device before

* [multiple-choice] update for Trainer

* Re-align to 827d6d6ef0

src/transformers/__init__.py

@@ -31,6 +31,8 @@ from .benchmark_utils import (
     start_memory_tracing,
     stop_memory_tracing,
 )
+
+# Configurations
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
 from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, AutoConfig
 from .configuration_bart import BartConfig
@@ -46,8 +48,6 @@ from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, Open
 from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
 from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
-
-# Configurations
 from .configuration_utils import PretrainedConfig
 from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
 from .configuration_xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
@@ -121,6 +121,8 @@ from .pipelines import (
     TranslationPipeline,
     pipeline,
 )
+
+# Tokenizers
 from .tokenization_albert import AlbertTokenizer
 from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
 from .tokenization_bart import BartTokenizer, MBartTokenizer
@@ -136,8 +138,6 @@ from .tokenization_openai import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
 from .tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
 from .tokenization_t5 import T5Tokenizer
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer, TransfoXLTokenizerFast
-
-# Tokenizers
 from .tokenization_utils import PreTrainedTokenizer
 from .tokenization_xlm import XLMTokenizer
 from .tokenization_xlm_roberta import XLMRobertaTokenizer
@@ -162,6 +162,7 @@ if is_torch_available():
         AutoModelForQuestionAnswering,
         AutoModelWithLMHead,
         AutoModelForTokenClassification,
+        AutoModelForMultipleChoice,
         ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
         MODEL_MAPPING,
         MODEL_FOR_PRETRAINING_MAPPING,
@@ -169,6 +170,7 @@ if is_torch_available():
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
+        MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
     )
 
     from .modeling_bert import (
@@ -320,6 +322,10 @@ if is_torch_available():
         get_linear_schedule_with_warmup,
     )
 
+    # Trainer
+    from .trainer import Trainer, set_seed, torch_distributed_zero_first, EvalPrediction
+    from .data.data_collator import DefaultDataCollator, DataCollator, DataCollatorForLanguageModeling
+    from .data.datasets import GlueDataset, TextDataset, LineByLineTextDataset, GlueDataTrainingArguments
 
 # TensorFlow
 if is_tf_available():