crf4j: CRF model training and testing for Java

This is a pure Java port of taku's crfpp(also known as crf++), which is based on codes of crfpp-0.58.

Credits to komiya's for his Java double array trie implementation.

Features

pure Java, with least dependencies(only commons-cli as runtime deps)
compatible commandline options and template/input format with crfpp
load model from classpath
compatible text model format with crfpp
convert text model to (our)binary model and (our)binary model to text model
multi-threading support
CRF-L1/CRF-L2/MIRA algorithms supports
n-best outputs
CRF Model wrapper for API call
Tests and demo for usage demonstration

Usage

Building

mvn clean package

Run tests:

mvn test

Training

java -cp crf4j-<version>-jar-with-dependencies.jar com.github.zhifac.crf4j.CrfLearn <template file> <train datafile> <model path>

For more options, please run

java -cp crf4j-<version>-jar-with-dependencies.jar com.github.zhifac.crf4j.CrfLearn -h

For details on format of template file and train file, please refer to original page of crfpp.

Testing

to print output to console:

java -cp crf4j-<version>-jar-with-dependencies.jar com.github.zhifac.crf4j.CrfTest -m <model path> <test datafile>

to print output to file:

java -cp crf4j-<version>-jar-with-dependencies.jar com.github.zhifac.crf4j.CrfTest -m <model path> <test datafile> -o <outputfile>

API call

please refer to CrfDemo.java.

Performance

Concurrent Access

In an example of using crf4j model to recognize name entity, we used jmeter to test 400 concurrent access to the same Http interface, and here is the result.

#Samples	Average	Median	90% Line	Min	Max	Throughput
4000	41	4	60	0	746	1250/sec

The test environment is:

OS	CPU	MEM
Windows 7x64	Intel Core i5-4200U@1.60GHz	8GB

Notes

The binary model generated by CrfLearn is incompatible with crfpp, but the text model is. If you somehow want to reuse a crfpp model with crf4j, please generate a text model when you train with crfpp(add -t option), and then run java -cp crf4j.jar com.github.zhifac.crf4j.EncoderFeatureIndex <crfpp_text_model> <output_crf4j_binarymodel> to convert the crfpp text model to crf4j binary model. Or if you somehow can not retrain the same text model(e.g. missing train data), you can still convert an existing crfpp binary model to text model with modified version of crfpp from here.

TODO

Optimize memory usage when training(it currently consumes about 8GB heap memory for 24224128 features, whereas crfpp uses 2GB)

License

LGPL & Modified BSD

Chinese version:

crf4j: crfpp(crf++)的Java实现

（基于crfpp 0.58）

thankslife/crf4j