# Conflicts:
#	fastNLP/api/pipeline.py
#	fastNLP/api/pos_tagger.py
#	fastNLP/api/processor.py
#	fastNLP/modules/decoder/CRF.py

- 在api中创建converter.py
- Pipeline添加初始化方法，方便一次性添加processors
- 删除pos_tagger.py
- 优化整体code style

- update predictor.py to remove unused methods
- update model_loader.py & model_saver.py to support entire model saving & loading
- update pos tagging training script

- improve metrics codes
- fix validator bugs in trainer; remove early saving
- run CWS codes
- improve README.md

- add character vocab in preprocessor
- add dataset loader for language model dataset
- other minor adjustments
- preserve only a little example data for language model

2. changes of names:
  aggregation ----> aggregator
  interaction ----> interactor
  action.py ----> sampler.py
  BasePreprocess ---> Preprocessor
  BaseTester ----> Tester
  BaseTrainer ----> Trainer
3. add more code comments
4. fix bugs in predictor's data_forward
5. in sampler.py, remove Bachifier, fix some codes. but not test
6. remove unused codes in other_modules.py & utils.py
7. update fastnlp.py with new config file names and code comments
8. add data examples in data_for_tests/

- improve code comments
- BaseLoader & its subclasses does not need a data name any more
- update file tree
- add setup.py

[add] PeopleDailyCorpusLoader, to parse PeopleDaily Corpus
[update] add CWS + POS_tag interface at FastNLP, see example in test_fastNLP.py
[update] modify README.md and readme_example.py to the latest version.

- see test_fastNLP.py
- update interpret_word_seg_results in fastnlp.py
- delete useless data to increase git clone speed

1. Tester has a parameter "print_every_step" to control printing. print_every_step == 0 means NO print.
2. Tester's evaluate return (list of) floats, rather than torch.cuda.tensor
3. Trainer also has a parameter "print_every_step". The same usage.
4. In training, validation steps are not shown.
5. Updates to code comments.
6. fastnlp.py is ready for CWS. test_fastNLP.py works.

- specify the name of the config file and the name of corresponding section where model init params store.
- fastnlp.py needs load_pickle to get dictionary size and the number of labels
- other minor adjustments

New Trainer Initialization Interface

- add Loss, Optimizer
- change Trainer & Tester initialization interface: two styles of definition provided
- handle Optimizer construction and loss function definition in a hard manner
- add argparse in task-specific scripts. (seq_labeling.py & text_classify.py)
- seq_labeling.py & text_classify.py work

New updates

- move preprocess.py from loader/ to core/
- changes to interface of preprocess: 1. add run method, to run the main processing 2. add cross validation split 3. add return value 4. merge subclasses
- Trainer supports cross validation
- add data as arguments in Trainer.train & Tester.test
- add readme.example.py, to run the example program shown in README.md
- other corresponding changes

Updates to cores, loader, saver

- rename Inference to Predictor
- rename Trainer.prepare_input to Trainer.load_train_data, load data_train.pkl only
- add __contains__ method to config Section class
- more code comments
- more elegant make_batch & data_iterator: Samplers return batch samples instead of batch indices

- rename "POSTrainer", "POSTester" to "SeqLabelTrainer", "SeqLabelTester"
- Trainer & Tester have NO relation with Action
- Inference owns independent "make_batch" & "data_forward"
- Conversion to Tensor & go into cuda are done in "make_batch"
- "make_batch" support maximum/minimum length

- rename "POSTrainer" to "SeqLabelTrainer"
- add text classification test data
- update make_batch in  Trainer and Tester

- [tester][trainer] add cuda support
- [preprocess] fix label2index for padding label seq
- update README.md
- [test] add test_tester.py
- rename "action" to "core"

- restructure: move reproduction outside
- add evaluate in tester