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Java example source code file (Glove.java)

This example Java source code file (Glove.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

builder, collection, deprecated, documentiterator, glove, modelutils, override, sentenceiterator, sentencetransformer, sequenceiterator, tokenizerfactory, util, vectorsconfiguration, weightlookuptable, wordvectors

The Glove.java Java example source code

package org.deeplearning4j.models.glove;

import lombok.NonNull;
import org.deeplearning4j.models.embeddings.learning.impl.elements.GloVe;
import org.deeplearning4j.models.embeddings.reader.ModelUtils;
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors;
import org.deeplearning4j.models.sequencevectors.SequenceVectors;
import org.deeplearning4j.models.sequencevectors.interfaces.SequenceIterator;
import org.deeplearning4j.models.embeddings.WeightLookupTable;
import org.deeplearning4j.models.embeddings.loader.VectorsConfiguration;
import org.deeplearning4j.models.sequencevectors.interfaces.VectorsListener;
import org.deeplearning4j.models.sequencevectors.iterators.AbstractSequenceIterator;
import org.deeplearning4j.models.sequencevectors.transformers.impl.SentenceTransformer;
import org.deeplearning4j.models.word2vec.VocabWord;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.deeplearning4j.text.documentiterator.DocumentIterator;
import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
import org.deeplearning4j.text.sentenceiterator.StreamLineIterator;
import org.deeplearning4j.text.tokenization.tokenizer.Tokenizer;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;

import java.util.Collection;
import java.util.List;

/**
 * GlobalVectors standalone implementation for DL4j.
 * Based on original Stanford GloVe
 * http://www-nlp.stanford.edu/pubs/glove.pdf
 *
 * @author raver119@gmail.com
 */
public class Glove extends SequenceVectors<VocabWord> {

    protected Glove() {

    }

    public static class Builder extends SequenceVectors.Builder<VocabWord> {
        private double xMax;
        private boolean shuffle;
        private boolean symmetric;
        protected double alpha = 0.75d;
        private int maxmemory = (int) (Runtime.getRuntime().totalMemory() / 1024 /1024 / 1024);

        protected TokenizerFactory tokenFactory;
        protected SentenceIterator sentenceIterator;
        protected DocumentIterator documentIterator;

        public Builder() {
            super();
        }


        public Builder(@NonNull VectorsConfiguration configuration) {
            super(configuration);
        }


        /**
         * This method has no effect for GloVe
         *
         * @param vec existing WordVectors model
         * @return
         */
        @Override
        public Builder useExistingWordVectors(@NonNull WordVectors vec) {
            return this;
        }

        @Override
        public Builder iterate(@NonNull SequenceIterator<VocabWord> iterator) {
            super.iterate(iterator);
            return this;
        }

        /**
         * Specifies minibatch size for training process.
         *
         * @param batchSize
         * @return
         */
        @Override
        public Builder batchSize(int batchSize) {
            super.batchSize(batchSize);
            return this;
        }

        /**
         * Ierations and epochs are the same in GloVe implementation.
         *
         * @param iterations
         * @return
         */
        @Override
        public Builder iterations(int iterations) {
            super.epochs(iterations);
            return this;
        }

        /**
         * Sets the number of iteration over training corpus during training
         *
         * @param numEpochs
         * @return
         */
        @Override
        public Builder epochs(int numEpochs) {
            super.epochs(numEpochs);
            return this;
        }

        @Override
        public Builder useAdaGrad(boolean reallyUse) {
            super.useAdaGrad(true);
            return this;
        }

        @Override
        public Builder layerSize(int layerSize) {
            super.layerSize(layerSize);
            return this;
        }

        @Override
        public Builder learningRate(double learningRate) {
            super.learningRate(learningRate);
            return this;
        }

        /**
         * Sets minimum word frequency during vocabulary mastering.
         * Please note: this option is ignored, if vocabulary is built outside of GloVe
         *
         * @param minWordFrequency
         * @return
         */
        @Override
        public Builder minWordFrequency(int minWordFrequency) {
            super.minWordFrequency(minWordFrequency);
            return this;
        }

        @Override
        public Builder minLearningRate(double minLearningRate) {
            super.minLearningRate(minLearningRate);
            return this;
        }

        @Override
        public Builder resetModel(boolean reallyReset) {
            super.resetModel(reallyReset);
            return this;
        }

        @Override
        public Builder vocabCache(@NonNull VocabCache<VocabWord> vocabCache) {
            super.vocabCache(vocabCache);
            return this;
        }

        @Override
        public Builder lookupTable(@NonNull WeightLookupTable<VocabWord> lookupTable) {
            super.lookupTable(lookupTable);
            return this;
        }

        @Override
        @Deprecated
        public Builder sampling(double sampling) {
            super.sampling(sampling);
            return this;
        }

        @Override
        @Deprecated
        public Builder negativeSample(double negative) {
            super.negativeSample(negative);
            return this;
        }

        @Override
        public Builder stopWords(@NonNull List<String> stopList) {
            super.stopWords(stopList);
            return this;
        }

        @Override
        public Builder trainElementsRepresentation(boolean trainElements) {
            super.trainElementsRepresentation(true);
            return this;
        }

        @Override
        @Deprecated
        public Builder trainSequencesRepresentation(boolean trainSequences) {
            super.trainSequencesRepresentation(false);
            return this;
        }

        @Override
        public Builder stopWords(@NonNull Collection<VocabWord> stopList) {
            super.stopWords(stopList);
            return this;
        }

        @Override
        public Builder windowSize(int windowSize) {
            super.windowSize(windowSize);
            return this;
        }

        @Override
        public Builder seed(long randomSeed) {
            super.seed(randomSeed);
            return this;
        }

        @Override
        public Builder workers(int numWorkers) {
            super.workers(numWorkers);
            return this;
        }

        /**
         * Sets TokenizerFactory to be used for training
         *
         * @param tokenizerFactory
         * @return
         */
        public Builder tokenizerFactory(@NonNull TokenizerFactory tokenizerFactory) {
            this.tokenFactory = tokenizerFactory;
            return this;
        }

        /**
         * Parameter specifying cutoff in weighting function; default 100.0
         *
         * @param xMax
         * @return
         */
        public Builder xMax(double xMax) {
            this.xMax = xMax;
            return this;
        }

        /**
         * Parameters specifying, if cooccurrences list should be build into both directions from any current word.
         *
         * @param reallySymmetric
         * @return
         */
        public Builder symmetric(boolean reallySymmetric) {
            this.symmetric = reallySymmetric;
            return this;
        }

        /**
         * Parameter specifying, if cooccurrences list should be shuffled between training epochs
         *
         * @param reallyShuffle
         * @return
         */
        public Builder shuffle(boolean reallyShuffle) {
            this.shuffle = reallyShuffle;
            return this;
        }

        /**
         * Parameter in exponent of weighting function; default 0.75
         *
         * @param alpha
         * @return
         */
        public Builder alpha(double alpha) {
            this.alpha = alpha;
            return this;
        }

        public Builder iterate(@NonNull SentenceIterator iterator) {
            this.sentenceIterator = iterator;
            return this;
        }

        public Builder iterate(@NonNull DocumentIterator iterator) {
            this.sentenceIterator = new StreamLineIterator.Builder(iterator)
                    .setFetchSize(100)
                    .build();
            return this;
        }

        /**
         * Sets ModelUtils that gonna be used as provider for utility methods: similarity(), wordsNearest(), accuracy(), etc
         *
         * @param modelUtils model utils to be used
         * @return
         */
        @Override
        public Builder modelUtils(@NonNull ModelUtils<VocabWord> modelUtils) {
            super.modelUtils(modelUtils);
            return this;
        }

        /**
         * This method sets VectorsListeners for this SequenceVectors model
         *
         * @param vectorsListeners
         * @return
         */
        @Override
        public Builder setVectorsListeners(@NonNull Collection<VectorsListener vectorsListeners) {
            super.setVectorsListeners(vectorsListeners);
            return this;
        }

        /**
         * This method allows you to specify maximum memory available for CoOccurrence map builder.
         *
         * Please note: this option can be considered a debugging method. In most cases setting proper -Xmx argument set to JVM is enough to limit this algorithm.
         * Please note: this option won't override -Xmx JVM value.
         *
         * @param gbytes memory limit, in gigabytes
         * @return
         */
        public Builder maxMemory(int gbytes) {
            this.maxmemory = gbytes;
            return this;
        }

        /**
         * This method allows you to specify SequenceElement that will be used as UNK element, if UNK is used
         *
         * @param element
         * @return
         */
        @Override
        public Builder unknownElement(VocabWord element) {
            super.unknownElement(element);
            return this;
        }

        /**
         * This method allows you to specify, if UNK word should be used internally
         *
         * @param reallyUse
         * @return
         */
        @Override
        public Builder useUnknown(boolean reallyUse) {
            super.useUnknown(reallyUse);
            if (this.unknownElement == null) {
                this.unknownElement(new VocabWord(1.0, Glove.DEFAULT_UNK));
            }
            return this;
        }

        public Glove build() {
            presetTables();

            Glove ret = new Glove();


            // hardcoded value for glove

            if (sentenceIterator != null) {
                SentenceTransformer transformer = new SentenceTransformer.Builder()
                        .iterator(sentenceIterator)
                        .tokenizerFactory(tokenFactory)
                        .build();
                this.iterator = new AbstractSequenceIterator.Builder<VocabWord>(transformer).build();
            }


            ret.trainElementsVectors = true;
            ret.trainSequenceVectors = false;
            ret.useAdeGrad = true;
            this.useAdaGrad = true;

            ret.learningRate.set(this.learningRate);
            ret.resetModel = this.resetModel;
            ret.batchSize = this.batchSize;
            ret.iterator = this.iterator;
            ret.numEpochs = this.numEpochs;
            ret.numIterations = this.iterations;
            ret.layerSize = this.layerSize;

            ret.useUnknown = this.useUnknown;
            ret.unknownElement = this.unknownElement;



            this.configuration.setLearningRate(this.learningRate);
            this.configuration.setLayersSize(layerSize);
            this.configuration.setHugeModelExpected(hugeModelExpected);
            this.configuration.setWindow(window);
            this.configuration.setMinWordFrequency(minWordFrequency);
            this.configuration.setIterations(iterations);
            this.configuration.setSeed(seed);
            this.configuration.setBatchSize(batchSize);
            this.configuration.setLearningRateDecayWords(learningRateDecayWords);
            this.configuration.setMinLearningRate(minLearningRate);
            this.configuration.setSampling(this.sampling);
            this.configuration.setUseAdaGrad(useAdaGrad);
            this.configuration.setNegative(negative);
            this.configuration.setEpochs(this.numEpochs);


            ret.configuration = this.configuration;

            ret.lookupTable = this.lookupTable;
            ret.vocab = this.vocabCache;
            ret.modelUtils = this.modelUtils;
            ret.eventListeners = this.vectorsListeners;


            ret.elementsLearningAlgorithm = new GloVe.Builder<VocabWord>()
                    .learningRate(this.learningRate)
                    .shuffle(this.shuffle)
                    .symmetric(this.symmetric)
                    .xMax(this.xMax)
                    .alpha(this.alpha)
                    .maxMemory(maxmemory)
                    .build();

            return ret;
        }
    }
}

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