org.deeplearning4j.text.tokenization.tokenizer.preprocessor.EndingPreProcessor Java Examples

The following examples show how to use org.deeplearning4j.text.tokenization.tokenizer.preprocessor.EndingPreProcessor. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: Word2VecModelExample.java    From Java-Deep-Learning-Cookbook with MIT License 5 votes vote down vote up 
public static void main(String[] args) throws Exception {
    final SentenceIterator iterator = new LineSentenceIterator(new ClassPathResource("raw_sentences_large.txt").getFile());
    SentenceDataPreProcessor.setPreprocessor(iterator);
    final TokenizerFactory tokenizerFactory = new DefaultTokenizerFactory();
    tokenizerFactory.setTokenPreProcessor(new EndingPreProcessor());

    final Word2Vec model = new Word2Vec.Builder()
                                    .iterate(iterator)
                                    .tokenizerFactory(tokenizerFactory)
                                    .minWordFrequency(5)
                                    .layerSize(100)
                                    .seed(42)
                                    .epochs(50)
                                    .windowSize(5)
                                    .build();
    log.info("Fitting Word2Vec model....");
    model.fit();

    final Collection<String> words = model.wordsNearest("season",10);
    for(final String word: words){
        System.out.println(word+ " ");
    }
    final double cosSimilarity = model.similarity("season","program");
    System.out.println(cosSimilarity);

    BarnesHutTsne tsne = new BarnesHutTsne.Builder()
            .setMaxIter(100)
            .theta(0.5)
            .normalize(false)
            .learningRate(500)
            .useAdaGrad(false)
            .build();


    //save word vectors for tSNE visualization.
    WordVectorSerializer.writeWordVectors(model.lookupTable(),new File("words.txt"));
    WordVectorSerializer.writeWord2VecModel(model, "model.zip");

}
Example #2
Source File: Word2VecModelExample.java    From Java-Deep-Learning-Cookbook with MIT License 5 votes vote down vote up 
public static void main(String[] args) throws Exception {
    final SentenceIterator iterator = new LineSentenceIterator(new ClassPathResource("raw_sentences_large.txt").getFile());
    SentenceDataPreProcessor.setPreprocessor(iterator);
    final TokenizerFactory tokenizerFactory = new DefaultTokenizerFactory();
    tokenizerFactory.setTokenPreProcessor(new EndingPreProcessor());

    final Word2Vec model = new Word2Vec.Builder()
                                    .iterate(iterator)
                                    .tokenizerFactory(tokenizerFactory)
                                    .minWordFrequency(5)
                                    .layerSize(100)
                                    .seed(42)
                                    .epochs(50)
                                    .windowSize(5)
                                    .build();
    log.info("Fitting Word2Vec model....");
    model.fit();

    final Collection<String> words = model.wordsNearest("season",10);
    for(final String word: words){
        System.out.println(word+ " ");
    }
    final double cosSimilarity = model.similarity("season","program");
    System.out.println(cosSimilarity);

    BarnesHutTsne tsne = new BarnesHutTsne.Builder()
            .setMaxIter(100)
            .theta(0.5)
            .normalize(false)
            .learningRate(500)
            .useAdaGrad(false)
            .build();


    //save word vectors for tSNE visualization.
    WordVectorSerializer.writeWordVectors(model.lookupTable(),new File("words.txt"));
    WordVectorSerializer.writeWord2VecModel(model, "model.zip");

}
Example #3
Source File: EndingPreProcessorTest.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testPreProcessor() {
    TokenPreProcess preProcess = new EndingPreProcessor();
    String endingTest = "ending";
    assertEquals("end", preProcess.preProcess(endingTest));

}