Example usage for org.deeplearning4j.models.word2vec VocabWord getWord

Introduction

In this page you can find the example usage for org.deeplearning4j.models.word2vec VocabWord getWord.

Prototype

public String getWord() 

Source Link

Usage

From source file:org.knime.ext.textprocessing.dl4j.nodes.embeddings.extract.VocabularyExtractorNodeModel2.java

License:Open Source License

@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    final WordVectorFileStorePortObject portObject = (WordVectorFileStorePortObject) inObjects[0];
    WordVectors wordVec = portObject.getWordVectors();
    WordVectorsImpl<VocabWord> wvImpl = null;

    if (wordVec instanceof WordVectorsImpl) {
        wvImpl = (WordVectorsImpl) wordVec;
    } else {//from w w  w . ja v a 2s  .co  m
        throw new IllegalStateException(
                "Word Vector model expected to be at least of type: " + WordVectorsImpl.class);
    }

    final List<String> vocabulary = new ArrayList<>();
    final List<String> labels = new ArrayList<>();

    for (VocabWord vw : wvImpl.getVocab().tokens()) {
        if (vw.isLabel()) {
            labels.add(vw.getWord());
        } else {
            vocabulary.add(vw.getWord());
        }
    }

    m_maxProgress = vocabulary.size() + labels.size();

    PortObject[] outputTables = new PortObject[2];
    outputTables[0] = createWordVectorTableFromWordList((DataTableSpec) m_outputSpec[0], vocabulary, wordVec,
            exec);

    if (labels.isEmpty()) {
        outputTables[1] = InactiveBranchPortObject.INSTANCE;
    } else {
        outputTables[1] = createWordVectorTableFromWordList((DataTableSpec) m_outputSpec[1], labels, wordVec,
                exec);
    }

    return outputTables;
}

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