Examples of opennlp.tools.ml.SequenceTrainer.train()

opennlp.tools.ml.SequenceTrainer.train()

    } 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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    } 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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          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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          mlParams.getSettings(), manifestInfoEntries);


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


      ChunkSampleSequenceStream ss = new ChunkSampleSequenceStream(in, factory.getContextGenerator());
      seqChunkerModel = trainer.train(ss);
    }
    else {
      throw new IllegalArgumentException("Trainer type is not supported: " + trainerType);
    }

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