我一直在使用propety文件训练我的ner模型,如本教程LINK所示。我正在使用相同的prop文件,但是当我无法理解如何以编程方式执行它时。
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, sentiment, regexner");
props.setProperty("ner.model", "resources/NER.prop");
prop文件如下所示:
# location of the training file
trainFile = nerTEST.tsv
# location where you would like to save (serialize) your
# classifier; adding .gz at the end automatically gzips the file,
# making it smaller, and faster to load
serializeTo = resources/ner-model.ser.gz
# structure of your training file; this tells the classifier that
# the word is in column 0 and the correct answer is in column 1
map = word=0,answer=1
# This specifies the order of the CRF: order 1 means that features
# apply at most to a class pair of previous class and current class
# or current class and next class.
maxLeft=1
# these are the features we'd like to train with
# some are discussed below, the rest can be
# understood by looking at NERFeatureFactory
useClassFeature=true
useWord=true
# word character ngrams will be included up to length 6 as prefixes
# and suffixes only
useNGrams=true
noMidNGrams=true
maxNGramLeng=6
usePrev=true
useNext=true
useDisjunctive=true
useSequences=true
usePrevSequences=true
# the last 4 properties deal with word shape features
useTypeSeqs=true
useTypeSeqs2=true
useTypeySequences=true
wordShape=chris2useLC
错误:
java.io.StreamCorruptedException: invalid stream header: 23206C6F
....
..
Caused by: java.io.IOException: Couldn't load classifier from resources/NER.prop
从SO上的另一个问题,我了解到您直接提供了模型文件。但是,我们如何借助属性文件来做到这一点?
答案 0 :(得分:2)
您应该从命令行运行此命令:
java -cp "*" edu.stanford.nlp.ie.crf.CRFClassifier -prop NER.prop
如果你想在Java代码中运行它,你可以这样做:
String[] args = new String[]{"-props", "NER.prop"};
CRFClassifier.main(args);
.prop文件是指定训练模型的设置的文件。您的代码正在尝试将.prop文件作为模型本身加载,这会导致错误。
执行任一操作将在resources / ner-model.ser.gz
生成最终模型答案 1 :(得分:1)
public class TrainModel {
private void trainCrf(String serializeFile, String prop) {
Properties props = StringUtils.propFileToProperties(prop);
props.setProperty("serializeTo", serializeFile);
SeqClassifierFlags flags = new SeqClassifierFlags(props);
CRFClassifier<CoreLabel> crf = new CRFClassifier<>(flags);
crf.train();
crf.serializeClassifier(serializeFile);
}
public static void main(String[] args) {
String serializeFile = "skill/ner-model.ser.gz";
String prop = "ner.props";
TrainModel trainModel = new TrainModel();
trainModel.trainCrf(serializeFile, prop);
}
}