- 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

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