An implementation of Conditional Random Fields (CRFs) with Deep Learning Method
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.

README.md

DeepCRF: Neural Networks and CRFs for Sequence Labeling

A implementation of Conditional Random Fields (CRFs) with Deep Learning Method.

DeepCRF is a sequence labeling library that uses neural networks and CRFs in Python using Chainer, a flexible deep learning framework.

Which version of Python is supported?

  • Python 2.7
  • Python 3.4

Which version of Chainer is supported?

  • Chainer v1.24.0
  • Chainer v2.1.0

How to install?

# if you use Ubuntu
sudo apt install libhdf5-dev

git clone https://github.com/aonotas/deep-crf.git
cd deep-crf
python setup.py install

# if you want to use Chainer v1.24.0
pip install 'chainer==1.24.0'

# if you want to use Chainer v2.1.0
pip install 'chainer==2.1.0'
pip install cupy # if you want to use CUDA

How to train?

train Ma and Hovy (2016) model

$ deep-crf train input_file.txt --delimiter=' ' --dev_file input_file_dev.txt --save_dir save_model_dir --save_name bilstm-cnn-crf_adam --optimizer adam

Note that --dev_file means path of development file to use early stopping.

$ cat input_file.txt
Barack  B−PERSON 
Hussein I−PERSON 
Obama   E−PERSON
is      O 
a       O 
man     O 
.       O

Yuji   B−PERSON 
Matsumoto E−PERSON 
is     O 
a      O 
man    O 
.      O

Each line is word and gold tag. One line is represented by word [ ](space) gold tag. Note that you should put empty line (\n) between sentences. This format is called CoNLL format.

Deep BiLSTM-CNN-CRF model (three layers)

$ deep-crf train input_file.txt --delimiter=' ' --n_layer 3  --dev_file input_file_dev.txt --save_dir save_model_dir --save_name bilstm-cnn-crf_adam --optimizer adam

Deep BiLSTM-CNN-CRF model (three layers) with Multiple Input files

If input file is multiple due to large input files or many lines, please following commands. Please add this arg : --use_list_files 1

$ deep-crf train input_file_list.txt --delimiter=' ' --n_layer 3  --dev_file input_file_dev.txt --save_dir save_model_dir --save_name bilstm-cnn-crf_adam --optimizer adam --use_list_files 1
$ cat input_file_list.txt
./path_to_file/input_file_1.txt
./path_to_file/input_file_2.txt
./path_to_file/input_file_3.txt

set Pretrained Word Embeddings

$ deep-crf train input_file.txt --delimiter=' ' --n_layer 3 --word_emb_file ./glove.6B.100d.txt --word_emb_vocab_type replace_all --dev_file input_file_dev.txt

We prepare some vocab mode.

  • --word_emb_vocab_type: select from [replace_all, replace_only, additional]
  • replace_all : Replace training vocab by Glove embeddings's vocab.
  • replace_only : Replace word embedding exists in training vocab.
  • additional : Concatenate training vocab and Glove embeddings's vocab.

If you want to use word2vec embeddings, please convert Glove format.

$ head glove.6B.100d.txt
the -0.038194 -0.24487 0.72812 -0.39961 0.083172
dog -0.10767 0.11053 0.59812 -0.54361 0.67396
cat -0.33979 0.20941 0.46348 -0.64792 -0.38377
of -0.1529 -0.24279 0.89837 0.16996 0.53516
to -0.1897 0.050024 0.19084 -0.049184 -0.089737
and -0.071953 0.23127 0.023731 -0.50638 0.33923
in 0.085703 -0.22201 0.16569 0.13373 0.38239

Additional Feature Support

$ deep-crf train input_file_multi.txt --delimiter=' ' --input_idx 0,1 --output_idx 2 --dev_file input_file_dev.txt --save_dir save_model_dir --save_name bilstm-cnn-crf_adam_additional --optimizer adam
$ cat input_file_multi.txt
Barack  NN B−PERSON 
Hussein NN I−PERSON 
Obama   NN E−PERSON
is      VBZ O 
a       DT  O 
man     NN  O 
.       .   O

Yuji  NN B−PERSON 
Matsumoto NN E−PERSON 
is      VBZ O 
a       DT  O 
man     NN  O 
.       .   O

Note that --input_idx means that input features (but word feature must be 0-index) like this example.

Multi-Task Learning Support

(Now developing this multi-task learning mode...)

$ deep-crf train input_file_multi.txt --delimiter ' ' --model_name bilstm-cnn-crf --input idx 0 --output idx 1,2 

How to predict?

$ deep-crf predict input_raw_file.txt --delimiter=' ' --model_filename ./save_model_dir/bilstm-cnn-crf_adam_epoch3.model --save_dir save_model_dir --save_name bilstm-cnn-crf_adam  --predicted_output predicted.txt

Please use following format when predict.

$ cat input_raw_file.txt
Barack Hussein Obama is a man .
Yuji Matsumoto is a man .

Note that --model_filename means saved model file path. Please set same --save_name in training step.

How to predict? (Additional Feature)

$ deep-crf predict input_file_multi.txt --delimiter=' ' --input_idx 0,1 --output_idx 2 --model_filename ./save_model_dir/bilstm-cnn-crf_multi_epoch3.model --save_dir save_model_dir --save_name bilstm-cnn-crf_multi  --predicted_output predicted.txt

Note that you must prepare CoNLL format input file when you use additional feature mode in training step.

$ cat input_file_multi.txt
Barack  NN B−PERSON 
Hussein NN I−PERSON 
Obama   NN E−PERSON
is      VBZ O 
a       DT  O 
man     NN  O 
.       .   O

Yuji  NN B−PERSON 
Matsumoto NN E−PERSON 
is      VBZ O 
a       DT  O 
man     NN  O 
.       .   O

How to evaluate?

$ deep-crf eval gold.txt predicted.txt
$ head gold.txt
O
O
B-LOC
O
O

B-PERSON

How to update?

cd deep-crf
git pull
python setup.py install

Help (how to use)

deep-crf train --help

If CUDNN ERROR

if you got CUDNN ERROR, please let me know in issues.

You can cudnn-off mode with --use_cudnn=0

Features

DeepCRF provides following features.

  • Bi-LSTM / Bi-GRU / Bi-RNN
  • CNN for character-level representation
  • Pre-trained word embedding
  • Pre-trained character embedding
  • CRFs at output layer
  • CoNLL format input/output
  • Raw text data input/output
  • Training : Your variable files
  • Test : Raw text file at command-line
  • Evaluation : F-measure, Accuracy

Experiment

POS Tagging

Model Accuracy
CRFsuite 96.39
deep-crf 97.45
dos Santos and Zadrozny (2014) 97.32
Ma and Hovy (2016) 97.55

Named Entity Recognition (NER)

Model Prec. Recall F1
CRFsuite 84.43 83.60 84.01
deep-crf 90.82 91.11 90.96
Ma and Hovy (2016) 91.35 91.06 91.21

Chunking

Model Prec. Recall F1
CRFsuite 93.77 93.45 93.61
deep-crf 94.67 94.43 94.55
Huang et al. (2015) - - 94.46