SequenceVectors.Builder<T> |
SequenceVectors.Builder.batchSize(int batchSize) |
This method defines batchSize option, viable only if iterations > 1
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.elementsLearningAlgorithm(@NonNull String algoName) |
* Sets specific LearningAlgorithm as Elements Learning Algorithm
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.elementsLearningAlgorithm(@NonNull ElementsLearningAlgorithm<T> algorithm) |
* Sets specific LearningAlgorithm as Elements Learning Algorithm
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.enableScavenger(boolean reallyEnable) |
This method ebables/disables periodical vocab truncation during construction
Default value: disabled
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.epochs(int numEpochs) |
This method defines how much iterations should be done over whole training corpus during modelling
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.intersectModel(@NonNull SequenceVectors<T> intersectVectors,
boolean lockFactor) |
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.iterate(@NonNull SequenceIterator<T> iterator) |
This method defines SequenceIterator to be used for model building
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.iterations(int iterations) |
This method defines how much iterations should be done over batched sequences.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.layerSize(int layerSize) |
This method defines number of dimensions for outcome vectors.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.learningRate(double learningRate) |
This method defines initial learning rate.
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SequenceVectors.Builder |
SequenceVectors.Builder.limitVocabularySize(int limit) |
This method sets vocabulary limit during construction.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.lookupTable(@NonNull WeightLookupTable<T> lookupTable) |
You can pass externally built WeightLookupTable, containing model weights and vocabulary.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.minLearningRate(double minLearningRate) |
This method defines minimum learning rate after decay being applied.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.minWordFrequency(int minWordFrequency) |
This method defines minimal element frequency for elements found in the training corpus.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.modelUtils(@NonNull ModelUtils<T> modelUtils) |
ModelUtils implementation, that will be used to access model.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.negativeSample(double negative) |
This method defines negative sampling value for skip-gram algorithm.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.resetModel(boolean reallyReset) |
This method defines, should all model be reset before training.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.sampling(double sampling) |
This method defines sub-sampling threshold.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.seed(long randomSeed) |
Sets seed for random numbers generator.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.sequenceLearningAlgorithm(@NonNull String algoName) |
Sets specific LearningAlgorithm as Sequence Learning Algorithm
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.sequenceLearningAlgorithm(@NonNull SequenceLearningAlgorithm<T> algorithm) |
Sets specific LearningAlgorithm as Sequence Learning Algorithm
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.setVectorsListeners(@NonNull Collection<VectorsListener<T>> listeners) |
This method sets VectorsListeners for this SequenceVectors model
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.stopWords(@NonNull Collection<T> stopList) |
You can provide collection of objects to be ignored, and excluded out of model
Please note: Object labels and hashCode will be used for filtering
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.stopWords(@NonNull List<String> stopList) |
You can provide collection of objects to be ignored, and excluded out of model
Please note: Object labels and hashCode will be used for filtering
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.trainElementsRepresentation(boolean trainElements) |
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.trainSequencesRepresentation(boolean trainSequences) |
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.unknownElement(T element) |
This method allows you to specify SequenceElement that will be used as UNK element, if UNK is used
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.useAdaGrad(boolean reallyUse) |
Deprecated.
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protected SequenceVectors.Builder<T> |
SequenceVectors.Builder.useExistingWordVectors(@NonNull WordVectors vec) |
This method allows you to use pre-built WordVectors model (e.g.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.useHierarchicSoftmax(boolean reallyUse) |
Enable/disable hierarchic softmax
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.usePreciseMode(boolean reallyUse) |
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.usePreciseWeightInit(boolean reallyUse) |
If set to true, initial weights for elements/sequences will be derived from elements themself.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.useUnknown(boolean reallyUse) |
This method allows you to specify, if UNK word should be used internally
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.useVariableWindow(int... windows) |
This method allows to use variable window size.
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.vocabCache(@NonNull VocabCache<T> vocabCache) |
You can pass externally built vocabCache object, containing vocabulary
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.windowSize(int windowSize) |
Sets window size for skip-Gram training
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SequenceVectors.Builder<T> |
SequenceVectors.Builder.workers(int numWorkers) |
Sets number of worker threads to be used in calculations
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