Examples of opennlp.model.AbstractModel

opennlp.model.AbstractModel

    assertEquals("person", names1[2].getType());
    assertTrue(!hasOtherAsOutcome(nameFinderModel));
  }


  private boolean hasOtherAsOutcome(TokenNameFinderModel nameFinderModel) {
    AbstractModel model = nameFinderModel.getNameFinderModel();
    for (int i = 0; i < model.getNumOutcomes(); i++) {
        String outcome = model.getOutcome(i);
        if (outcome.equals(NameFinderME.OTHER)) {
          return true;
        }
      }
    return false;

View Full Code Here

    assertEquals("person", names1[2].getType());
    assertTrue(!hasOtherAsOutcome(nameFinderModel));
  }
  
  private boolean hasOtherAsOutcome(TokenNameFinderModel nameFinderModel) {
    AbstractModel model = nameFinderModel.getNameFinderModel();
    for (int i = 0; i < model.getNumOutcomes(); i++) {
        String outcome = model.getOutcome(i);
        if (outcome.equals(NameFinderME.OTHER)) {
          return true;
        }
      }
    return false;

View Full Code Here


      File outputFile = new File(modelFileName);


      AbstractModelWriter writer;


      AbstractModel model;
      if (type.equals("maxent")) {
  GIS.SMOOTHING_OBSERVATION = SMOOTHING_OBSERVATION;


        if (!real) {
          model = GIS.trainModel(es, maxit, cutoff, sigma);

View Full Code Here

  public static void main(String[] args) throws java.io.IOException {
    if (args.length == 0) {
      System.err.println("Usage: GISModel modelname < contexts");
      System.exit(1);
    }
    AbstractModel m = new opennlp.maxent.io.SuffixSensitiveGISModelReader(
        new File(args[0])).getModel();
    BufferedReader in = new BufferedReader(new InputStreamReader(System.in));
    DecimalFormat df = new java.text.DecimalFormat(".###");
    for (String line = in.readLine(); line != null; line = in.readLine()) {
      String[] context = line.split(" ");
      double[] dist = m.eval(context);
      for (int oi = 0; oi < dist.length; oi++) {
        System.out.print("[" + m.getOutcome(oi) + " " + df.format(dist[oi])
            + "] ");
      }
      System.out.println();
    }
  }

View Full Code Here

  public static void main(String[] args) throws java.io.IOException {
    if (args.length == 0) {
      System.err.println("Usage: PerceptronModel modelname < contexts");
      System.exit(1);
    }
    AbstractModel m = new PerceptronModelReader(new File(args[0])).getModel();
    BufferedReader in = new BufferedReader(new InputStreamReader(System.in));
    DecimalFormat df = new java.text.DecimalFormat(".###");
    for (String line = in.readLine(); line != null; line = in.readLine()) {
      String[] context = line.split(" ");
      double[] dist = m.eval(context);
      for (int oi=0;oi<dist.length;oi++) {
        System.out.print("["+m.getOutcome(oi)+" "+df.format(dist[oi])+"] ");
      }
      System.out.println();
    }
  }

View Full Code Here

  @Override
  protected void validateArtifactMap() throws InvalidFormatException {
    super.validateArtifactMap();
    
    if (artifactMap.get(MAXENT_MODEL_ENTRY_NAME) instanceof AbstractModel) {
      AbstractModel model = (AbstractModel) artifactMap.get(MAXENT_MODEL_ENTRY_NAME);
      isModelValid(model);
    }
    else {
      throw new InvalidFormatException("Token Name Finder model is incomplete!");
    }

View Full Code Here


    String languageCode = args[ai++];
    String packageName = args[ai++];
    String modelName = args[ai];


    AbstractModel model = new BinaryGISModelReader(new DataInputStream(
        new FileInputStream(modelName))).getModel();


    TokenizerModel packageModel = new TokenizerModel(languageCode, model,
        alphaNumericOptimization);

View Full Code Here

    // build
    System.err.println("Training builder");
    opennlp.model.EventStream bes = new ParserEventStream(parseSamples, rules,
        ParserEventTypeEnum.BUILD, mdict);
    Map<String, String> buildReportMap = new HashMap<String, String>();
    AbstractModel buildModel = TrainUtil.train(bes, mlParams.getSettings("build"), buildReportMap);
    opennlp.tools.parser.chunking.Parser.mergeReportIntoManifest(manifestInfoEntries, buildReportMap, "build");
    
    parseSamples.reset();
    
    // check
    System.err.println("Training checker");
    opennlp.model.EventStream kes = new ParserEventStream(parseSamples, rules,
        ParserEventTypeEnum.CHECK);
    Map<String, String> checkReportMap = new HashMap<String, String>();
    AbstractModel checkModel = TrainUtil.train(kes, mlParams.getSettings("check"), checkReportMap);
    opennlp.tools.parser.chunking.Parser.mergeReportIntoManifest(manifestInfoEntries, checkReportMap, "check");
    
    parseSamples.reset();
    
    // attach 
    System.err.println("Training attacher");
    opennlp.model.EventStream attachEvents = new ParserEventStream(parseSamples, rules,
        ParserEventTypeEnum.ATTACH);
    Map<String, String> attachReportMap = new HashMap<String, String>();
    AbstractModel attachModel = TrainUtil.train(attachEvents, mlParams.getSettings("attach"), attachReportMap);
    opennlp.tools.parser.chunking.Parser.mergeReportIntoManifest(manifestInfoEntries, attachReportMap, "attach");
    
    // TODO: Remove cast for HeadRules
    return new ParserModel(languageCode, buildModel, checkModel,
        attachModel, posModel, chunkModel,

View Full Code Here


    String languageCode = args[ai++];
    String packageName = args[ai++];
    String modelName = args[ai];


    AbstractModel model = new GenericModelReader(new File(modelName)).getModel();
    SentenceModel packageModel = new SentenceModel(languageCode, model, useTokenEnd, abbreviations);
    packageModel.serialize(new FileOutputStream(packageName));
  }

View Full Code Here

    
    // build
    System.err.println("Training builder");
    opennlp.model.EventStream bes = new ParserEventStream(parseSamples, rules, ParserEventTypeEnum.BUILD, mdict);
    Map<String, String> buildReportMap = new HashMap<String, String>();
    AbstractModel buildModel = TrainUtil.train(bes, mlParams.getSettings("build"), buildReportMap);
    mergeReportIntoManifest(manifestInfoEntries, buildReportMap, "build");
    
    parseSamples.reset();
    
    // tag
    POSModel posModel = POSTaggerME.train(languageCode, new PosSampleStream(parseSamples), 
        mlParams.getParameters("tagger"), null, null);
    
    parseSamples.reset();
    
    // chunk
    ChunkerModel chunkModel = ChunkerME.train(languageCode, 
        new ChunkSampleStream(parseSamples),
        new ChunkContextGenerator(), mlParams.getParameters("chunker"));
    
    parseSamples.reset();
    
    // check
    System.err.println("Training checker");
    opennlp.model.EventStream kes = new ParserEventStream(parseSamples, rules, ParserEventTypeEnum.CHECK);
    Map<String, String> checkReportMap = new HashMap<String, String>();
    AbstractModel checkModel = TrainUtil.train(kes, mlParams.getSettings("check"), checkReportMap);
    mergeReportIntoManifest(manifestInfoEntries, checkReportMap, "check");


    // TODO: Remove cast for HeadRules
    return new ParserModel(languageCode, buildModel, checkModel,
        posModel, chunkModel, (opennlp.tools.parser.lang.en.HeadRules) rules,

View Full Code Here

0 1 2 3 4 5 6

TOP

Related Classes of opennlp.model.AbstractModel

CreateModel

opennlp.maxent.GISModel

opennlp.maxent.io.SuffixSensitiveGISModelReader

opennlp.maxent.MaxentPrepAttachTest

opennlp.maxent.ModelTrainer

opennlp.maxent.quasinewton.QNTrainerTest

opennlp.perceptron.PerceptronModel

opennlp.perceptron.PerceptronPrepAttachTest

opennlp.tools.chunker.ChunkerME

opennlp.tools.chunker.ChunkerModel

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.