Package opennlp.tools.ml

Examples of opennlp.tools.ml.SequenceTrainer

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      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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      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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      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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      EventTrainer trainer = TrainerFactory.getEventTrainer(mlParams.getSettings(),
          manifestInfoEntries);
      chunkerModel = trainer.train(es);
    }
    else if (TrainerType.SEQUENCE_TRAINER.equals(trainerType)) {
      SequenceTrainer trainer = TrainerFactory.getSequenceModelTrainer(
          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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Related Classes of opennlp.tools.ml.SequenceTrainer

  • opennlp.tools.chunker.ChunkerME
  • opennlp.tools.namefind.NameFinderME
  • opennlp.tools.postag.POSTaggerME
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