Examples of opennlp.tools.ml.EventModelSequenceTrainer

opennlp.tools.ml.EventModelSequenceTrainer

    } // TODO: Maybe it is not a good idea, that these two don't use the context generator ?!
    // These also don't use the sequence codec ?!
    else if (TrainerType.EVENT_MODEL_SEQUENCE_TRAINER.equals(trainerType)) {
      NameSampleSequenceStream ss = new NameSampleSequenceStream(samples, factory.createContextGenerator());


      EventModelSequenceTrainer trainer = TrainerFactory.getEventModelSequenceTrainer(
              trainParams.getSettings(), manifestInfoEntries);
      nameFinderModel = trainer.train(ss);
    } else if (TrainerType.SEQUENCE_TRAINER.equals(trainerType)) {
      SequenceTrainer trainer = TrainerFactory.getSequenceModelTrainer(
              trainParams.getSettings(), manifestInfoEntries);


      NameSampleSequenceStream ss = new NameSampleSequenceStream(samples, factory.createContextGenerator(), false);
      seqModel = trainer.train(ss);
    } else {
      throw new IllegalStateException("Unexpected trainer type!");
    }


    if (seqModel != null) {

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      EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams.getSettings(), manifestInfoEntries);
      nameFinderModel = trainer.train(eventStream);
    } else if (TrainerType.EVENT_MODEL_SEQUENCE_TRAINER.equals(trainerType)) {
      NameSampleSequenceStream ss = new NameSampleSequenceStream(samples, featureGenerator);


      EventModelSequenceTrainer trainer = TrainerFactory.getEventModelSequenceTrainer(
              trainParams.getSettings(), manifestInfoEntries);
      nameFinderModel = trainer.train(ss);
    } else if (TrainerType.SEQUENCE_TRAINER.equals(trainerType)) {
      SequenceTrainer trainer = TrainerFactory.getSequenceModelTrainer(
              trainParams.getSettings(), manifestInfoEntries);


      NameSampleSequenceStream ss = new NameSampleSequenceStream(samples, featureGenerator, false);
      seqModel = trainer.train(ss);
    } else {
      throw new IllegalStateException("Unexpected trainer type!");
    }


     // TODO: Pass the sequence codec down to the model! We will just store the class

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          manifestInfoEntries);
      posModel = trainer.train(es);
    }
    else if (TrainerType.EVENT_MODEL_SEQUENCE_TRAINER.equals(trainerType)) {
      POSSampleSequenceStream ss = new POSSampleSequenceStream(samples, contextGenerator);
      EventModelSequenceTrainer trainer = TrainerFactory.getEventModelSequenceTrainer(trainParams.getSettings(),
          manifestInfoEntries);
      posModel = trainer.train(ss);
    }
    else if (TrainerType.SEQUENCE_TRAINER.equals(trainerType)) {
      SequenceTrainer trainer = TrainerFactory.getSequenceModelTrainer(
          trainParams.getSettings(), manifestInfoEntries);


      // TODO: This will probably cause issue, since the feature generator uses the outcomes array


      POSSampleSequenceStream ss = new POSSampleSequenceStream(samples, contextGenerator);
      seqPosModel = trainer.train(ss);
    }
    else {
      throw new IllegalArgumentException("Trainer type is not supported: " + trainerType);
    }

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      Map<String, String> reportMap) throws IOException {


    if(!TrainerFactory.isSupportSequence(trainParams)) {
      throw new IllegalArgumentException("EventTrain is not supported");
    }
    EventModelSequenceTrainer trainer = TrainerFactory.getEventModelSequenceTrainer(trainParams, reportMap);


    return trainer.train(events);
  }

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Related Classes of opennlp.tools.ml.EventModelSequenceTrainer

opennlp.tools.ml.model.TrainUtil

opennlp.tools.namefind.NameFinderME

opennlp.tools.postag.POSTaggerME

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