1.新增支持原始bert模型（非easynlp的 backbone prefix版本）
2.支持bert的在sequence classification/fill mask /token classification上的backbone head形式
3.统一了sequence classification几个任务的pipeline到一个类
4.fill mask 支持backbone head形式
5.token classification的几个子任务（ner，word seg， part of speech）的preprocessor 统一到了一起TokenClassificationPreprocessor
6. sequence classification的几个子任务（single classification， pair classification）的preprocessor 统一到了一起SequenceClassificationPreprocessor
7. 改动register中 cls的group_key 赋值位置，之前的group_key在多个decorators的情况下，会被覆盖，obj_cls的group_key信息不正确
8. 基于backbone head形式将 原本group_key和 module同名的情况尝试做调整，如下在modelscope/pipelines/nlp/sequence_classification_pipeline.py 中 
原本
 @PIPELINES.register_module(
    Tasks.sentiment_classification, module_name=Pipelines.sentiment_classification)
改成
@PIPELINES.register_module(
    Tasks.text_classification, module_name=Pipelines.sentiment_classification)
相应的configuration.json也有改动，这样的改动更符合任务和pipline（子任务）的关系。
8. 其他相应改动为支持上述功能
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10041463

1.  refactor maaslib to modelscope
2.  fix UT error
3.  support pipeline which does not register default model

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8988388

* add preprocessor module
 * add model base and builder
 * update task constant
 * add load image preprocessor and its dependency
 * add pipeline interface and UT covered
 * support default pipeline for task
 * add image matting pipeline
 * refine nlp tokenize interface
 * add nlp pipeline 
 * fix UT failed
 * add test for Compose

Link: https://code.aone.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8769235

* add preprocessor module

* add test for Compose

* fix citest error

* fix abs class error

* add model base and builder

* update task constant

* add load image preprocessor and its dependency

* add pipeline interface and UT covered

* support default pipeline for task

* refine models and pipeline interface

* add pipeline folder structure

* add image matting pipeline

* refine nlp tokenize interface

* add nlp pipeline 

1.add preprossor model pipeline for nlp text classification
2. add corresponding test

Link: https://code.aone.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8757371

* new nlp pipeline

* format pre-commit code

* update easynlp pipeline

* update model_name for easynlp pipeline; add test for maas_lib/utils/typeassert.py

* update test_typeassert.py

* refactor code

1. rename typeassert to type_assert
2. use lazy import to make easynlp dependency optional
3. refine image matting UT

* fix linter test failed

* update requirements.txt

* fix UT failed

* fix citest script to update requirements