* update rouge-l, move default_trans_func to utils

* fix error info bugs

* add score method for metric bleu and rouge-l

* update bleu doc

* Add Express Example

* Add Express Data

* Add Ernie for Express Example

* add the express for the paddlenlp

* remove the ernie_crf file
Co-authored-by: Nwanghuijuan03 <wanghuijuan03@baidu.com>

* Add Express Example

* Add Express Data

* Add Ernie for Express Example

* add the express for the paddlenlp
Co-authored-by: Nwanghuijuan03 <wanghuijuan03@baidu.com>

Co-authored-by: Nwawltor <fanzeyang0904@hotmail.com>

* update docs

* add sbert

* add readme

* update readme

* update codes

* update lrscheduler

* minor fix

* add pre-commit

* minor fix

* Add __len__ to squad dataset

* minor fix

* Add dureader robust prototype

* dataset implement

* minor fix

* fix var name

* add dureader-yesno train script and dataset

* add readme and fix md5sum

* integrete dureader datasets

* change var names: segment to mode, root to data_file

* minor fix

* update var name

* Fix api bugs

* update the readme for the benchmark

* udpate README file for the bert benchmark

* uddate the readme format for the benchmark bert
Co-authored-by: Nwawltor <fanzeyang0904@hotmail.com>
Co-authored-by: Nfangzeyang <fangzeyang@baidu.com>

* optimize lac

* formatted

* optimize lac

* optimize lac

* update lrscheduler

* minor fix

* add pre-commit

* minor fix

* Add __len__ to squad dataset

* minor fix

* Add dureader robust prototype

* dataset implement

* minor fix

* fix var name

* add dureader-yesno train script and dataset

* add readme and fix md5sum

* integrete dureader datasets

* change var names: segment to mode, root to data_file

* minor fix

* update var name

* update lrscheduler

* minor fix

* add pre-commit

* minor fix

* Add __len__ to squad dataset

* minor fix

* Add dureader robust prototype

* dataset implement

* minor fix

* fix var name

* add dureader-yesno train script and dataset

* add readme and fix md5sum

* integrete dureader datasets

* Unified the task name with paddle1.8

* fixed bug.

* update ppl, inherited from Metric, suitable for common mt and lm task.

* fix ppl bugs, delete useless argument

* fix ppl bugs, return a num not a numpy array.

* Add TokenEmbedding

* download corpus embedding data
* load embedding data by specifying corpus name
* extend the vocab of tokenizer from corpus embedding data

* add unk token setting

* modify tokenizer

* add extend voacb

* move jieba tokenizer and rename corpus_name->embedding_name

* use bos url instead of localhost

* add log when loading data

* add token dot computation; add __repr__ of TokenEmbedding

* add color logging

* use paddlenlp.utils.log

* adjust repr

* update pretrained embedding table

* fix padding idx

* Update paddlenlp.metrics README

* Add info for Mcc

* delete DuReader metric

* update task name, add API col

* add custom op gelu

* refine run_pretrain

* add electra pretrain and modify style of electra modeling

* add electra pretrain, modify style of electra modeling and fix problems of review

* delete predict_classifer

* modify accu to acc

* add paddlenlp.metrics.glue

use paddlenlp downloader for the tokenizer

* add model api

* update codes

* update codes

* update codes

* add amp support for model SE_ResNet50_vd

* optimize code, add model ResNet50 and SE_ResNet50_vd to list

* merge develop

* add the support of amp for the fleet train
* remove the attribute for the amp

* update lrscheduler

* minor fix

* add pre-commit

* minor fix

* Add __len__ to squad dataset

* minor fix

* update lrscheduler

* minor fix

* add pre-commit

Co-authored-by: Nfangzeyang <fangzeyang@baidu.com>

* update api 1.8

* fix paddlerec readme

* fix

* fix

* fix 2.0

* fix 2.0

* fix 2.0

* fix 2.0

* fix 2.0

* fix

* add pure fp16 training.

* update the usage of cast_parameters_to_fp16.

* use fp32 to initilize parameter values and then cast to fp16.

* enable multi_precision when training with fp16.

* add static loss scaling.

* add the rescale_grad function in the pure fp16 training.

* use cast to complete the pure fp16 training.

* use sum loss for fp16 trainging and mean loss for fp32 training.

* refine codes according reviewers' suggestions