- see fastNLp/saver/logger.py to know how to create and use a logger
- a log file named "train_test.log" will be created in the same dir as the main file where the program starts
- this file records all important events happened in Trainer & Tester's methods

- 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

- fastNLP interface for sequence labeling works
- fastNLP interface for text classification works

- 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

- change parameter <seq_length-->mask> in loss function defined in seq model
- Trainer & Tester have Action as default parameter, shared static methods like make_batch
- add seq_len in make_batch of Inference
- add SeqLabelInfer, a subclass of Inference
- seq_labeling.py works

- Action collects shared operations: data_forward, mode, pad, make_batch
- Trainer and Tester receives Action as a parameter
- seq_labeling works in such setting