Course Project
review the pytorch framework and sequence-labeling task
practice using the transformers of Huggingface

A train set, a test set and a validation set in the data file

-DOCSTART- -X- O O
-sentnce- -pos- -Chuck- -Entity-

-data  # source data
-emb # BERT model files

-util
    -dataTool.py  # data interface
    -model.py
    -trainer.py  # train and evaluate

config.py  # parameters in the project
run.py
requirement.txt

EDA.ipynb # exploratory data analasis, 
          # which aims to confirm the hyper-params in the trials

For keeping the convenience and simplicity of experiments,
decouple the model into two units: encoder and tagger

model ==> encoder + tagger

In such a way, encoder extracts the context and linguistit features,
which will be received by tagger to output BIO tags.

chmod 755 deploy
./deploy

./gpu n  # monitor the GPU (refresh every n seconds)
./run  # start