基于OpenNLP 2.1.1 Java版本实现训练、使用MaxEntTrainer 的示例，提供完整代码

好的，以下是一个基于OpenNLP 2.1.1 Java版本实现训练和使用MaxEntTrainer的示例代码，包括训练数据和测试数据：

import java.io.FileInputStream;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.util.Collections;

import opennlp.tools.doccat.DoccatFactory;
import opennlp.tools.doccat.DoccatModel;
import opennlp.tools.doccat.DocumentCategorizerME;
import opennlp.tools.doccat.DocumentSample;
import opennlp.tools.doccat.DocumentSampleStream;
import opennlp.tools.ml.EventTrainer;
import opennlp.tools.ml.ModelTrainer;
import opennlp.tools.ml.TrainerFactory;
import opennlp.tools.ml.TrainerFactory.TrainerType;
import opennlp.tools.ml.model.Event;
import opennlp.tools.ml.model.MaxentModel;
import opennlp.tools.ml.model.TwoPassDataIndexer;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.PlainTextByLineStream;
import opennlp.tools.util.Span;
import opennlp.tools.util.TrainingParameters;

public class MaxEntTrainerExample {

    public static void main(String[] args) throws IOException {
        // 1. 创建训练器
        DoccatFactory factory = new DoccatFactory();
        ModelTrainer<DoccatModel> trainer = TrainerFactory.create(
                TrainerType.EVENT, "maxent", new TwoPassDataIndexer());
        trainer.setEventTrainer(TrainerFactory.createEventTrainer(
                100, new DoccatEventStreamFactory(factory)));
        
        // 2. 加载训练数据，并转换为OpenNLP格式
        ObjectStream<String> lineStream = new PlainTextByLineStream(
                () -> new FileInputStream("train.txt"), StandardCharsets.UTF_8);
        ObjectStream<DocumentSample> sampleStream = new DocumentSampleStream(lineStream);
        
        // 3. 训练模型
        DoccatModel model = trainer.train(sampleStream);
        
        // 4. 使用模型进行分类
        DocumentCategorizerME categorizer = new DocumentCategorizerME(model);
        double[] outcomes = categorizer.categorize("This is a test sentence.");
        System.out.println("Outcomes: " + outcomes[0] + ", " + outcomes[1]);
        String category = categorizer.getBestCategory(outcomes);
        System.out.println("Category: " + category);
        
        // 5. 加载测试数据，并转换为OpenNLP格式
        ObjectStream<String> testStream = new PlainTextByLineStream(
                () -> new FileInputStream("test.txt"), StandardCharsets.UTF_8);
        ObjectStream<DocumentSample> testSampleStream = new DocumentSampleStream(testStream);
        
        // 6. 对测试数据进行评估
        DocumentCategorizerME evaluator = new DocumentCategorizerME(model, Collections.emptyMap());
        ObjectStream<Event> events = new DoccatEventStreamFactory(factory).createEventStream(testSampleStream);
        EventTrainer trainer2 = TrainerFactory.createEventTrainer("maxent", null, Collections.emptyMap());
        MaxentModel maxentModel = trainer2.train(events);
        DocumentCategorizerME me = new DocumentCategorizerME(maxentModel);
        DocumentSample sample;
        while ((sample = testSampleStream.read()) != null) {
            String[] tokens = sample.getText().split(" ");
            double[] probs = me.categorize(tokens);
            String predictedCategory = me.getBestCategory(probs);
            String actualCategory = sample.getCategory();
            System.out.println("Predicted category: " + predictedCategory + ", Actual category: " + actualCategory);
            Span[] predictedSpans = me.getTokenSpans();
            System.out.println("Predicted spans: " + predictedSpans);
        }
    }
}

其中，train.txt和test.txt是训练和测试数据，每个文本实例都是一行。要注意，示例代码中使用的是文本分类模型，如果需要训练其他类型的模型，需要对代码进行相应的修改。