Several models for POS Tagging
Already implemented models:
- BPNN+CRF
- BiLSTM+CRF
- CHAR+BiLSTM+CRF
python == 3.6.5
pytorch == 0.4.1$ git clone https://github.com/yzhangcs/post.git
$ cd post
# eg: BiLSTM+CHAR+CRF
$ python run.py --model=lstm_char --crf$ python run.py -h
usage: run.py [-h] [--model {bpnn_crf,lstm_crf,char_lstm_crf}] [--drop DROP]
[--batch_size BATCH_SIZE] [--epochs EPOCHS]
[--interval INTERVAL] [--eta ETA] [--threads THREADS]
[--seed SEED] [--file FILE]
Create several models for POS Tagging.
optional arguments:
-h, --help show this help message and exit
--model {bpnn_crf,lstm_crf,char_lstm_crf}, -m {bpnn_crf,lstm_crf,char_lstm_crf}
choose the model for POS Tagging
--drop DROP set the prob of dropout
--batch_size BATCH_SIZE
set the size of batch
--epochs EPOCHS set the max num of epochs
--interval INTERVAL set the max interval to stop
--eta ETA set the learning rate of training
--threads THREADS, -t THREADS
set the max num of threads
--seed SEED, -s SEED set the seed for generating random numbers
--file FILE, -f FILE set where to store the model# BPNN+CRF
BPNN_CRF(
(embed): Embedding(54304, 100)
(hid): Sequential(
(0): Linear(in_features=500, out_features=300, bias=True)
(1): ReLU()
)
(out): Linear(in_features=300, out_features=32, bias=True)
(crf): CRF()
(drop): Dropout(p=0.5)
)
# BiLSTM+CRF
LSTM_CRF(
(embed): Embedding(54304, 100)
(lstm): LSTM(100, 150, batch_first=True, bidirectional=True)
(out): Linear(in_features=300, out_features=32, bias=True)
(crf): CRF()
(drop): Dropout(p=0.5)
)
# CHAR+BiLSTM+CRF
CHAR_LSTM_CRF(
(embed): Embedding(54304, 100)
(char_lstm): CharLSTM(
(embed): Embedding(7478, 100)
(lstm): LSTM(100, 100, batch_first=True, bidirectional=True)
)
(word_lstm): LSTM(300, 150, batch_first=True, bidirectional=True)
(out): Linear(in_features=300, out_features=32, bias=True)
(crf): CRF()
(drop): Dropout(p=0.5)
)