- [trainer]rename "batchify" to "make_batch" in trainer
- [trainer]pack (batch_x_pad, seq_len) into batch_x in make_batch for seq labeling, because seq length before pad is needed to make masks
- [trainer]unpack it in data_forward
- [model]shorten model definition
- [inference]build inference class. test_POS_pipeline.py is OK to infer
- [preprocessor]handle pickles in a nicer manner
- [FastNLP] add fastNLP.py as high-level API, not finished yet

# Conflicts:
#	fastNLP/modules/encoder/embedding.py
#	fastNLP/modules/encoder/lstm.py

add default parameters for modules, in order to decrease required params; update POSPreprocessor to return multi-level lists; add metrics.py

Add classification model.

optimize evaluation output in Trainer;
keep POS pipeline (loader + trainer + tester + saver) OK;
add codes borrowed from FudanParser.

update config loader

add ConfigSection class to save config data

restructure module: 4 classes; add modules; move prototype and rename

update config loader

1

- restructure: move reproduction outside
- add evaluate in tester

- add people.txt in data_for_tests
- To do: incorrect CRF param in POS_pipeline

1

Add files via upload

rename and add kmax pooling module