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

examples/README.md

@@ -246,7 +246,6 @@ and unpack it to some directory `$GLUE_DIR`.
 export GLUE_DIR=/path/to/glue
 
 python run_glue.py \
-  --model_type bert \
   --model_name_or_path bert-base-cased \
   --task_name MRPC \
   --do_train \
@@ -272,7 +271,6 @@ Using Apex and 16 bit precision, the fine-tuning on MRPC only takes 27 seconds.
 export GLUE_DIR=/path/to/glue
 
 python run_glue.py \
-  --model_type bert \
   --model_name_or_path bert-base-cased \
   --task_name MRPC \
   --do_train \
@@ -296,7 +294,6 @@ export GLUE_DIR=/path/to/glue
 
 python -m torch.distributed.launch \
     --nproc_per_node 8 run_glue.py \
-    --model_type bert \
     --model_name_or_path bert-base-cased \
     --task_name MRPC \
     --do_train \
@@ -329,7 +326,6 @@ export GLUE_DIR=/path/to/glue
 
 python -m torch.distributed.launch \
     --nproc_per_node 8 run_glue.py \
-    --model_type bert \
     --model_name_or_path bert-base-cased \
     --task_name mnli \
     --do_train \
@@ -369,7 +365,6 @@ Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
 #training on 4 tesla V100(16GB) GPUS
 export SWAG_DIR=/path/to/swag_data_dir
 python ./examples/run_multiple_choice.py \
---model_type roberta \
 --task_name swag \
 --model_name_or_path roberta-base \
 --do_train \