Package opennlp.model

Examples of opennlp.model.AbstractModel

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    // TODO: Fix the EventStream to throw exceptions when training goes wrong
    EventStream eventStream = new SDEventStream(samples,
        factory.createSentenceContextGenerator(languageCode, getAbbreviations(abbreviations)),
        factory.createEndOfSentenceScanner(languageCode));
   
    AbstractModel sentModel = TrainUtil.train(eventStream, mlParams.getSettings(), manifestInfoEntries);
   
    return new SentenceModel(languageCode, sentModel,
        useTokenEnd, abbreviations, manifestInfoEntries);
  }
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*/
public class PerceptronPrepAttachTest {

  @Test
  public void testPerceptronOnPrepAttachData() throws IOException {
    AbstractModel model =
        new PerceptronTrainer().trainModel(400,
        new TwoPassDataIndexer(createTrainingStream(), 1, false), 1);

    testModel(model, 0.7650408516959644);
  }
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    Map<String, String> trainParams = new HashMap<String, String>();
    trainParams.put(TrainUtil.ALGORITHM_PARAM, TrainUtil.PERCEPTRON_VALUE);
    trainParams.put(TrainUtil.CUTOFF_PARAM, Integer.toString(1));
    trainParams.put("UseSkippedAveraging", Boolean.toString(true));
   
    AbstractModel model = TrainUtil.train(createTrainingStream(), trainParams, null);
   
    testModel(model, 0.773706362961129);
  }
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    trainParams.put(TrainUtil.ALGORITHM_PARAM, TrainUtil.PERCEPTRON_VALUE);
    trainParams.put(TrainUtil.CUTOFF_PARAM, Integer.toString(1));
    trainParams.put(TrainUtil.ITERATIONS_PARAM, Integer.toString(500));
    trainParams.put("Tolerance", Double.toString(0.0001d));
   
    AbstractModel model = TrainUtil.train(createTrainingStream(), trainParams, null);
   
    testModel(model, 0.7677642980935875);
  }
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    trainParams.put(TrainUtil.ALGORITHM_PARAM, TrainUtil.PERCEPTRON_VALUE);
    trainParams.put(TrainUtil.CUTOFF_PARAM, Integer.toString(1));
    trainParams.put(TrainUtil.ITERATIONS_PARAM, Integer.toString(500));
    trainParams.put("StepSizeDecrease", Double.toString(0.06d));
   
    AbstractModel model = TrainUtil.train(createTrainingStream(), trainParams, null);
   
    testModel(model, 0.7756870512503095);
  }
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public class MaxentPrepAttachTest {

  @Test
  public void testMaxentOnPrepAttachData() throws IOException {
    AbstractModel model =
        new GISTrainer(true).trainModel(100,
        new TwoPassDataIndexer(createTrainingStream(), 1), 1);

    testModel(model, 0.7997028967566229);
  }
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    testModel(model, 0.7997028967566229);
  }
 
  @Test
  public void testMaxentOnPrepAttachData2Threads() throws IOException {
    AbstractModel model =
        new GISTrainer(true).trainModel(100,
            new TwoPassDataIndexer(createTrainingStream(), 1),
            new UniformPrior(), 1, 2);
   
    testModel(model, 0.7997028967566229);
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    trainParams.put(TrainUtil.ALGORITHM_PARAM, TrainUtil.MAXENT_VALUE);
    trainParams.put(TrainUtil.DATA_INDEXER_PARAM,
        TrainUtil.DATA_INDEXER_TWO_PASS_VALUE);
    trainParams.put(TrainUtil.CUTOFF_PARAM, Integer.toString(1));
   
    AbstractModel model = TrainUtil.train(createTrainingStream(), trainParams, null);
   
    testModel(model, 0.7997028967566229);
  }
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  public void testMaxentOnPrepAttachDataWithParamsDefault() throws IOException {
   
    Map<String, String> trainParams = new HashMap<String, String>();
    trainParams.put(TrainUtil.ALGORITHM_PARAM, TrainUtil.MAXENT_VALUE);
   
    AbstractModel model = TrainUtil.train(createTrainingStream(), trainParams, null);
   
    testModel(model, 0.8086159940579352 );
  }
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    EventStream eventStream = new TokSpanEventStream(samples,
        useAlphaNumericOptimization, factory.getAlphanumeric(languageCode),
        factory.createTokenContextGenerator(languageCode,
            getAbbreviations(abbreviations)));

    AbstractModel maxentModel = TrainUtil.train(eventStream,
        mlParams.getSettings(), manifestInfoEntries);

    return new TokenizerModel(languageCode, maxentModel, abbreviations,
        useAlphaNumericOptimization, manifestInfoEntries);
  }
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Related Classes of opennlp.model.AbstractModel

  • CreateModel
  • opennlp.maxent.GISModel
  • opennlp.maxent.io.SuffixSensitiveGISModelReader
  • opennlp.maxent.MaxentPrepAttachTest
  • opennlp.maxent.ModelTrainer
  • opennlp.maxent.quasinewton.QNTrainerTest
  • opennlp.perceptron.PerceptronModel
  • opennlp.perceptron.PerceptronPrepAttachTest
  • opennlp.tools.chunker.ChunkerME
  • opennlp.tools.chunker.ChunkerModel
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