1. Abstract keys of dicts needed by nlp metric classes into the init method
2. Add Preprocessor.save_pretrained to save preprocessor information
3. Abstract the config saving function, which can lead to normally saving in the direct call of from_pretrained, and the modification of cfg one by one when training.
4. Remove SbertTokenizer and VecoTokenizer, use transformers' tokenizers instead
5. Use model/preprocessor's from_pretrained in all nlp pipeline classes.
6. Add model_kwargs and preprocessor_kwargs in all nlp pipeline classes
7. Add base classes for fill-mask and text-classification preprocessor, as a demo for later changes
8. Fix user feedback: Re-train the model in continue training scenario
9. Fix user feedback: Too many checkpoint saved
10. Simplify the nlp-trainer
11. Fix user feedback: Split the default trainer's __init__ method, which makes user easier to override
12. Add safe_get to Config class

----------------------------  Another refactor from version 36 -------------------------

13. Name all nlp transformers' preprocessors from TaskNamePreprocessor to TaskNameTransformersPreprocessor, for example:
      TextClassificationPreprocessor -> TextClassificationTransformersPreprocessor
14. Add a base class per task for all nlp tasks' preprocessors which has at least two sub-preprocessors
15. Add output classes of nlp models
16. Refactor the logic for token-classification
17. Fix bug: checkpoint_hook does not support pytorch_model.pt
18. Fix bug: Pipeline name does not match with task name, so inference will not succeed after training
       NOTE: This is just a stop bleeding solution, the root cause is the uncertainty of the relationship between models and pipelines
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10723513

    * add save_pretrained to preprocessor

* save preprocessor config in hook

* refactor label-id mapping fetching logic

* test ok on sentence-similarity

* run on finetuning

* fix bug

* pre-commit passed

* fix bug

* Merge branch 'master' into feat/refactor_config

# Conflicts:
#	modelscope/preprocessors/nlp/nlp_base.py

* add params to init

* 1. support max ckpt num 2. support ignoring others but bin file in continue training 3. add arguments to some nlp metrics

* Split trainer init impls to overridable methods

* remove some obsolete tokenizers

* unfinished

* support input params in pipeline

* fix bugs

* fix ut bug

* fix bug

* fix ut bug

* fix ut bug

* fix ut bug

* add base class for some preprocessors

* Merge commit '379867739548f394d0fa349ba07afe04adf4c8b6' into feat/refactor_config

* compatible with old code

* fix ut bug

* fix ut bugs

* fix bug

* add some comments

* fix ut bug

* add a requirement

* fix pre-commit

* Merge commit '0451b3d3cb2bebfef92ec2c227b2a3dd8d01dc6a' into feat/refactor_config

* fixbug

* Support function type in registry

* fix ut bug

* fix bug

* Merge commit '5f719e542b963f0d35457e5359df879a5eb80b82' into feat/refactor_config

# Conflicts:
#	modelscope/pipelines/nlp/multilingual_word_segmentation_pipeline.py
#	modelscope/pipelines/nlp/named_entity_recognition_pipeline.py
#	modelscope/pipelines/nlp/word_segmentation_pipeline.py
#	modelscope/utils/hub.py

* remove obsolete file

* rename init args

* rename params

* fix merge bug

* add default preprocessor config for ner-model

* move a method a util file

* remove unused config

* Fix a bug in pbar

* bestckptsaver:change default ckpt numbers to 1

* 1. Add assert to max_epoch 2. split init_dist and get_device 3. change cmp func name

* Fix bug

* fix bug

* fix bug

* unfinished refactoring

* unfinished

* uw

* uw

* uw

* uw

* Merge branch 'feat/refactor_config' into feat/refactor_trainer

# Conflicts:
#	modelscope/preprocessors/nlp/document_segmentation_preprocessor.py
#	modelscope/preprocessors/nlp/faq_question_answering_preprocessor.py
#	modelscope/preprocessors/nlp/relation_extraction_preprocessor.py
#	modelscope/preprocessors/nlp/text_generation_preprocessor.py

* uw

* uw

* unify nlp task outputs

* uw

* uw

* uw

* uw

* change the order of text cls pipeline

* refactor t5

* refactor tg task preprocessor

* fix

* unfinished

* temp

* refactor code

* unfinished

* unfinished

* unfinished

* unfinished

* uw

* Merge branch 'feat/refactor_config' into feat/refactor_trainer

* smoke test pass

* ut testing

* pre-commit passed

* Merge branch 'master' into feat/refactor_config

# Conflicts:
#	modelscope/models/nlp/bert/document_segmentation.py
#	modelscope/pipelines/nlp/__init__.py
#	modelscope/pipelines/nlp/document_segmentation_pipeline.py

* merge master

* unifnished

* Merge branch 'feat/fix_bug_pipeline_name' into feat/refactor_config

* fix bug

* fix ut bug

* support ner batch inference

* fix ut bug

* fix bug

* support batch inference on three nlp tasks

* unfinished

* fix bug

* fix bug

* Merge branch 'master' into feat/refactor_config

# Conflicts:
#	modelscope/models/base/base_model.py
#	modelscope/pipelines/nlp/conversational_text_to_sql_pipeline.py
#	modelscope/pipelines/nlp/dialog_intent_prediction_pipeline.py
#	modelscope/pipelines/nlp/dialog_modeling_pipeline.py
#	modelscope/pipelines/nlp/dialog_state_tracking_pipeline.py
#	modelscope/pipelines/nlp/document_segmentation_pipeline.py
#	modelscope/pipelines/nlp/faq_question_answering_pipeline.py
#	modelscope/pipelines/nlp/feature_extraction_pipeline.py
#	modelscope/pipelines/nlp/fill_mask_pipeline.py
#	modelscope/pipelines/nlp/information_extraction_pipeline.py
#	modelscope/pipelines/nlp/named_entity_recognition_pipeline.py
#	modelscope/pipelines/nlp/sentence_embedding_pipeline.py
#	modelscope/pipelines/nlp/summarization_pipeline.py
#	modelscope/pipelines/nlp/table_question_answering_pipeline.py
#	modelscope/pipelines/nlp/text2text_generation_pipeline.py
#	modelscope/pipelines/nlp/text_classification_pipeline.py
#	modelscope/pipelines/nlp/text_error_correction_pipeline.py
#	modelscope/pipelines/nlp/text_generation_pipeline.py
#	modelscope/pipelines/nlp/text_ranking_pipeline.py
#	modelscope/pipelines/nlp/token_classification_pipeline.py
#	modelscope/pipelines/nlp/word_segmentation_pipeline.py
#	modelscope/pipelines/nlp/zero_shot_classification_pipeline.py
#	modelscope/trainers/nlp_trainer.py

* pre-commit passed

* fix bug

* Merge branch 'master' into feat/refactor_config

# Conflicts:
#	modelscope/preprocessors/__init__.py

* fix bug

* fix bug

* fix bug

* fix bug

* fix bug

* fixbug

* pre-commit passed

* fix bug

* fixbug

* fix bug

* fix bug

* fix bug

* fix bug

* self review done

* fixbug

* fix bug

* fix bug

* fix bugs

* remove sub-token offset mapping

* fix name bug

* add some tests

* 1. support batch inference of text-generation,text2text-generation,token-classification,text-classification 2. add corresponding UTs

* add old logic back

* tmp save

* add tokenize by words logic back

* move outputs file back

* revert veco token-classification back

* fix typo

* Fix description

* Merge commit '4dd99b8f6e4e7aefe047c68a1bedd95d3ec596d6' into feat/refactor_config

* Merge branch 'master' into feat/refactor_config

# Conflicts:
#	modelscope/pipelines/builder.py

添加东南亚小语种NLP支持，包括：
1. 针对泰语，越南语NER的预处理
2. 基于XLMR-CRF架构的分词模型和pipeline
3. 针对泰语分词的预处理

添加了相应pipeline的unittest
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10492404