/** @return {@link Word2VecTrainerBuilder} for training a model */ public static Word2VecTrainerBuilder trainer() { return new Word2VecTrainerBuilder(); } }
.setMinVocabFrequency(100) .useNumThreads(20) .setWindowSize(7) .type(NeuralNetworkType.SKIP_GRAM) .useHierarchicalSoftmax() .setLayerSize(300) .useNegativeSamples(0) .setDownSamplingRate(1e-3) .setNumIterations(5) .setListener(new TrainingProgressListener() { @Override public void update(Stage stage, double progress) { System.out.println(String.format("%s is %.2f%% complete", Format.formatEnum(stage), progress * 100)); .train(partitioned);
.setMinVocabFrequency(100) .useNumThreads(20) .setWindowSize(7) .type(NeuralNetworkType.SKIP_GRAM) .useHierarchicalSoftmax() .setLayerSize(300) .useNegativeSamples(0) .setDownSamplingRate(1e-3) .setNumIterations(5) .setListener(new TrainingProgressListener() { @Override public void update(Stage stage, double progress) { System.out.println(String.format("%s is %.2f%% complete", Format.formatEnum(stage), progress * 100)); .train(partitioned);
.setMinVocabFrequency(5) .useNumThreads(20) .setWindowSize(8) .type(NeuralNetworkType.CBOW) .setLayerSize(200) .useNegativeSamples(25) .setDownSamplingRate(1e-4) .setNumIterations(5) .setListener(new TrainingProgressListener() { @Override public void update(Stage stage, double progress) { System.out.println(String.format("%s is %.2f%% complete", Format.formatEnum(stage), progress * 100)); .train(partitioned);
/** @return {@link Word2VecTrainerBuilder} for training a model */ public static Word2VecTrainerBuilder trainer() { return new Word2VecTrainerBuilder(); } }
.setMinVocabFrequency(5) .useNumThreads(20) .setWindowSize(8) .type(NeuralNetworkType.CBOW) .setLayerSize(200) .useNegativeSamples(25) .setDownSamplingRate(1e-4) .setNumIterations(5) .setListener(new TrainingProgressListener() { @Override public void update(Stage stage, double progress) { System.out.println(String.format("%s is %.2f%% complete", Format.formatEnum(stage), progress * 100)); .train(partitioned);