Examples of SuffixSensitivePerceptronModelWriter


      } else if (type.equals("perceptron")) {
        //System.err.println("Perceptron training");
        model = new PerceptronTrainer().trainModel(maxit, new OnePassDataIndexer(es, cutoff), cutoff);


  writer = new SuffixSensitivePerceptronModelWriter(model, outputFile);


      } else {
        throw new RuntimeException("Unknown model type: " + type);
      }

Examples of opennlp.perceptron.SuffixSensitivePerceptronModelWriter

                new InputStreamReader(new FileInputStream(inFile), encoding))), cg);
          }
        }
        mod = new SimplePerceptronSequenceTrainer().trainModel(iterations, ss, cutoff, true);
        System.out.println("Saving the model as: " + outFile);
        new SuffixSensitivePerceptronModelWriter(mod, outFile).persist();
      }
      else {
        POSSampleEventStream es;
        if (encoding == null) {
          if (dict == null) {
            es = new POSSampleEventStream(new WordTagSampleStream((
                new InputStreamReader(new FileInputStream(inFile)))));
          }
          else {
            POSContextGenerator cg = new DefaultPOSContextGenerator(new Dictionary(new FileInputStream(dict)));


            es = new POSSampleEventStream(new WordTagSampleStream((
                new InputStreamReader(new FileInputStream(inFile)))),
                cg);
          }
        }
        else {
          if (dict == null) {


            es = new POSSampleEventStream(new WordTagSampleStream((
                new InputStreamReader(new FileInputStream(inFile), encoding))));
          }
          else {
            POSContextGenerator cg = new DefaultPOSContextGenerator(new Dictionary(new FileInputStream(dict)));


            es = new POSSampleEventStream(new WordTagSampleStream((
                new InputStreamReader(new FileInputStream(inFile), encoding))), cg);
          }
        }
        if (perceptron) {
          mod = trainPerceptronModel(es,iterations, cutoff);
          System.out.println("Saving the model as: " + outFile);
          new SuffixSensitivePerceptronModelWriter(mod, outFile).persist();
        }
        else {
          mod = trainMaxentModel(es, iterations, cutoff);


          System.out.println("Saving the model as: " + outFile);

Examples of opennlp.perceptron.SuffixSensitivePerceptronModelWriter

          writer =  new SuffixSensitiveGISModelWriter(model, outputFile);
        }
        else if (type.equals("perceptron")){ 
          System.err.println("Perceptron training");
          model = new PerceptronTrainer().trainModel(10, new OnePassDataIndexer(es,0),0);
          writer = new SuffixSensitivePerceptronModelWriter(model, outputFile);
        }
        else {
          System.err.println("Unknown model type: "+type);
          model = null;
        }

Examples of opennlp.perceptron.SuffixSensitivePerceptronModelWriter


      } else if (type.equals("perceptron")) {
        //System.err.println("Perceptron training");
        model = new PerceptronTrainer().trainModel(maxit, new OnePassDataIndexer(es, cutoff), cutoff);


  writer = new SuffixSensitivePerceptronModelWriter(model, outputFile);


      } else {
        throw new RuntimeException("Unknown model type: " + type);
      }

Examples of opennlp.perceptron.SuffixSensitivePerceptronModelWriter

                new InputStreamReader(new FileInputStream(inFile), encoding))), cg);
          }
        }
        mod = new SimplePerceptronSequenceTrainer().trainModel(iterations, ss, cutoff, true);
        System.out.println("Saving the model as: " + outFile);
        new SuffixSensitivePerceptronModelWriter(mod, outFile).persist();
      }
      else {
        POSSampleEventStream es;
        if (encoding == null) {
          if (dict == null) {
            es = new POSSampleEventStream(new WordTagSampleStream((
                new InputStreamReader(new FileInputStream(inFile)))));
          }
          else {
            POSContextGenerator cg = new DefaultPOSContextGenerator(new Dictionary(new FileInputStream(dict)));


            es = new POSSampleEventStream(new WordTagSampleStream((
                new InputStreamReader(new FileInputStream(inFile)))),
                cg);
          }
        }
        else {
          if (dict == null) {


            es = new POSSampleEventStream(new WordTagSampleStream((
                new InputStreamReader(new FileInputStream(inFile), encoding))));
          }
          else {
            POSContextGenerator cg = new DefaultPOSContextGenerator(new Dictionary(new FileInputStream(dict)));


            es = new POSSampleEventStream(new WordTagSampleStream((
                new InputStreamReader(new FileInputStream(inFile), encoding))), cg);
          }
        }
        if (perceptron) {
          mod = trainPerceptronModel(es,iterations, cutoff);
          System.out.println("Saving the model as: " + outFile);
          new SuffixSensitivePerceptronModelWriter(mod, outFile).persist();
        }
        else {
          mod = trainMaxentModel(es, iterations, cutoff);


          System.out.println("Saving the model as: " + outFile);