nlp - 使用 Mallet 加载模型并对输入进行分类

Question

我已经有了一个使用 SimpleTagger 训练过的 CRF 训练模型。

        SimpleTagger.main(new String[] {
                "--train", "true",
                "--model-file", "/Desktop/crfmodel",
                "--threads",  "8",
                "--training-proportion", "0.8",
                "--weights", "dense",
                "--test", "lab",
//                "--orders", "2",
                "/Desktop/annotations.txt"
        });

我计划加载此模型并将其用于标记。我正在使用这段代码。

    public static void main(String[] args) throws Exception {

        //DOCS http://mallet.cs.umass.edu/classifier-devel.php

        Instance instance = getMyInstance();

        Classifier classifier = loadClassifier(Paths.get("/Desktop/crfmodel").toFile());

        Labeling labeling = classifier.classify(instance).getLabeling();
        Label l = labeling.getBestLabel();
        System.out.print(instance);
        System.out.println(l);
    }


    private static Classifier loadClassifier(File serializedFile)
            throws FileNotFoundException, IOException, ClassNotFoundException {
        ObjectInputStream ois = new ObjectInputStream (new FileInputStream(serializedFile));
        Classifier crf = (Classifier) ois.readObject();
        ois.close();

        return crf;
    }

当我尝试执行上述操作时，出现以下错误

Exception in thread "main" java.lang.ClassCastException: cc.mallet.fst.CRF cannot be cast to cc.mallet.classify.Classifier
    at TagClassifier.loadClassifier(TagClassifier.java:77)
    at TagClassifier.main(TagClassifier.java:64)

错误发生在行中

Classifier crf = (Classifier) ois.readObject();

我可以知道为什么会发生这种情况吗？另外，如果有正确的记录方法使用经过训练的模型来标记输入，您可以分享任何链接/文档吗？预先非常感谢您!!!

Answer 1

我想我通过查看 SimpleTagger 代码找到了答案。

        crfModel = loadClassifier(Paths.get("/Desktop/crfmodel").toFile());
        pipe = crfModel.getInputPipe();
        pipe.setTargetProcessing(false);
        String formatted = getFormattedQuery(q);

        Instance instance = pipe.pipe(new Instance(formatted, null, null, null));
        Sequence sequence = (Sequence) instance.getData();
        Sequence[] tags = tag(sequence, 3);

    private static Sequence[] tag(Sequence input, int bestK) {
        Sequence[] answers;
        if (bestK == 1) {
            answers = new Sequence[1];
            answers[0] = crfModel.transduce(input);
        } else {
            MaxLatticeDefault lattice = new MaxLatticeDefault(crfModel, input, null);
            answers = lattice.bestOutputSequences(bestK).toArray(new Sequence[0]);
        }
        return answers;
    }