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

Dev/classify

- 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

add metrics

- 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

- [model] optimize cuda support in seq labeling model
- [test] add test data "pku" for chinese word seg
- test_tester.py and test_cws.py is OK to run!

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

- [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

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

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