Example usage for opennlp.tools.util TrainingParameters ITERATIONS_PARAM

Introduction

In this page you can find the example usage for opennlp.tools.util TrainingParameters ITERATIONS_PARAM.

Prototype

String ITERATIONS_PARAM

To view the source code for opennlp.tools.util TrainingParameters ITERATIONS_PARAM.

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Usage

From source file:io.learningbox.controller.APIController.java

@RequestMapping(value = "/categorize/{area}", method = RequestMethod.POST)
public SortedMap<Double, Set<String>> categorize(@PathVariable final String area, @RequestBody String input)
        throws IOException {
    List<LearningSet> l = repository.findByArea(area);
    final Iterator<LearningSet> sets = l.iterator();

    ObjectStream<DocumentSample> stream = new ObjectStream<DocumentSample>() {

        @Override//  w  w  w.j  a va  2s .  c  o m
        public DocumentSample read() throws IOException {
            if (sets.hasNext()) {
                LearningSet s = sets.next();

                return new DocumentSample(s.getCategory(), s.getText());
            }
            return null;
        }

        @Override
        public void reset() throws IOException, UnsupportedOperationException {
            throw new UnsupportedOperationException();
        }

        @Override
        public void close() throws IOException {
            //Do nothing
        }
    };

    TrainingParameters trainingParameters = TrainingParameters.defaultParams();
    trainingParameters.put(TrainingParameters.ITERATIONS_PARAM, Integer.toString(1000));
    trainingParameters.put(TrainingParameters.CUTOFF_PARAM, Integer.toString(1));

    DoccatModel model = DocumentCategorizerME.train("en", stream, trainingParameters, new DoccatFactory());
    DocumentCategorizerME myCategorizer = new DocumentCategorizerME(model);
    return myCategorizer.sortedScoreMap(input);
}

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From source file:de.tudarmstadt.ukp.dkpro.core.opennlp.OpenNlpNamedEntityRecognizerTrainer.java

@Override
public void initialize(UimaContext aContext) throws ResourceInitializationException {
    super.initialize(aContext);

    stream = new CasNameSampleStream();

    TrainingParameters params = new TrainingParameters();
    params.put(TrainingParameters.ALGORITHM_PARAM, algorithm);
    //        params.put(TrainingParameters.TRAINER_TYPE_PARAM,
    //                TrainerFactory.getTrainerType(params.getSettings()).name());
    params.put(TrainingParameters.ITERATIONS_PARAM, Integer.toString(iterations));
    params.put(TrainingParameters.CUTOFF_PARAM, Integer.toString(cutoff));
    params.put(BeamSearch.BEAM_SIZE_PARAMETER, Integer.toString(beamSize));

    byte featureGenCfg[] = loadFeatureGen(featureGen);

    Callable<TokenNameFinderModel> trainTask = () -> {
        try {//from   w  w w .j  a v a 2  s.  c o m
            return NameFinderME.train(language, null, stream, params, new TokenNameFinderFactory(featureGenCfg,
                    Collections.<String, Object>emptyMap(), sequenceEncoding.getCodec()));
        } catch (Throwable e) {
            stream.close();
            throw e;
        }
    };

    future = executor.submit(trainTask);
}

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