$ git clone https://github.com/chantera/biaffineparser
$ cd biaffineparser
$ pip install -r requirements.txt

usage: main.py train [-h] [--batchsize NUM] [--cachedir DIR] [--devfile FILE]
                     [--device ID] [--dropout PROB] [--embedfile FILE]
                     [--epoch NUM] [--lr VALUE] [--refresh] [--savedir DIR]
                     [--seed VALUE] --trainfile FILE

optional arguments:
  -h, --help        show this help message and exit
  --batchsize NUM   Number of tokens in each mini-batch (default: 5000)
  --cachedir DIR    Cache directory (default: cache)
  --devfile FILE    Development data file (default: None)
  --device ID       Device ID (negative value indicates CPU) (default: -1)
  --dropout PROB    Dropout ratio (default: 0.33)
  --embedfile FILE  Pretrained word embedding file (default: None)
  --epoch NUM       Number of sweeps over the dataset to train (default: 20)
  --lr VALUE        Learning rate (default: 0.002)
  --refresh, -r     Refresh cache. (default: False)
  --savedir DIR     Directory to save the model (default: None)
  --seed VALUE      Random seed (default: None)
  --trainfile FILE  Training data file. (default: None)

usage: main.py test [-h] [--device ID] --modelfile FILE --testfile FILE

optional arguments:
  -h, --help        show this help message and exit
  --device ID       Device ID (negative value indicates CPU) (default: -1)
  --modelfile FILE  Trained model file (default: None)
  --testfile FILE   Development data file (default: None)

mkdir models
python3 src/main.py train --trainfile=$DATA/train.conll --devfile=$DATA/dev.conll --embedfile=$DATA/glove.6B.100d.txt --epoch=250 --device=0 --savedir=./models --seed=2017
python3 src/main.py test --testfile=$DATA/test.conll --modelfile=./models/[yyyymmdd]-[id].npz --device=0