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Description
Interface Summary  

CacheStaleIndicator  Indicates when the value/gradient during training becomes stale. 
MaxLattice  The interface to classes implementing the Viterbi algorithm, finding the best sequence of states for a given input sequence. 
SumLattice  Interface to perform forwardbackward during training of a transducer. 
Transducer.Incrementor  Methods to be called by inference methods to indicate partial counts of sufficient statistics. 
TransducerTrainer.ByOptimization 
Class Summary  

CRF  Represents a CRF model. 
CRF.Factors  A simple, transparent container to hold the parameters or sufficient statistics for the CRF. 
CRF.State  
CRF.TransitionIterator  
CRFCacheStaleIndicator  Indicates when the value/gradient becomes stale based on updates to CRF's parameters. 
CRFOptimizableByBatchLabelLikelihood  Implements label likelihood gradient computations for batches of data, can be easily parallelized. 
CRFOptimizableByBatchLabelLikelihood.Factory  
CRFOptimizableByGradientValues  A CRF objective function that is the sum of multiple objective functions that implement Optimizable.ByGradientValue. 
CRFOptimizableByLabelLikelihood  An objective function for CRFs that is the label likelihood plus a Gaussian or hyperbolic prior on parameters. 
CRFOptimizableByLabelLikelihood.Factory  
CRFTrainerByL1LabelLikelihood  CRF trainer that implements L1regularization. 
CRFTrainerByLabelLikelihood  Unlike ClassifierTrainer, TransducerTrainer is not "stateless" between calls to train. 
CRFTrainerByStochasticGradient  Trains CRF by stochastic gradient. 
CRFTrainerByThreadedLabelLikelihood  
CRFTrainerByValueGradients  A CRF trainer that can combine multiple objective functions, each represented by a Optmizable.ByValueGradient. 
CRFWriter  Saves a trained model to specified filename. 
FeatureTransducer  
HMM  A Hidden Markov Model. 
HMM.State  
HMM.TransitionIterator  
HMMTrainerByLikelihood  
InstanceAccuracyEvaluator  Reports the percentage of instances for which the entire predicted sequence was correct. 
LabelDistributionEvaluator  Prints predicted and true label distribution. 
MaxLatticeDefault  Default, full dynamic programming version of the Viterbi "Max(Product)Lattice" algorithm. 
MaxLatticeDefault.Factory  
MaxLatticeFactory  
MEMM  A Maximum Entropy Markov Model. 
MEMM.State  
MEMM.TransitionIterator  
MEMMTrainer  Trains and evaluates a MEMM . 
MultiSegmentationEvaluator  Evaluates a transducer model, computes the precision, recall and F1 scores; considers segments that span across multiple tokens. 
NoopTransducerTrainer  A TransducerTrainer that does no training, but simply acts as a container for a Transducer; for use in situations that require a TransducerTrainer, such as the TransducerEvaluator methods. 
PerClassAccuracyEvaluator  Determines the precision, recall and F1 on a perclass basis. 
Segment  Represents a labelled chunk of a Sequence segmented by a
Transducer , usually corresponding to some object extracted
from an input Sequence . 
SegmentationEvaluator  
ShallowTransducerTrainer  Deprecated. Use NoopTransducerTrainer instead 
SimpleTagger  This class's main method trains, tests, or runs a generic CRFbased sequence tagger. 
SimpleTagger.SimpleTaggerSentence2FeatureVectorSequence  Converts an external encoding of a sequence of elements with binary
features to a FeatureVectorSequence . 
SumLatticeBeam  
SumLatticeBeam.Factory  
SumLatticeConstrained  
SumLatticeDefault  Default, full dynamic programming implementation of the ForwardBackward "Sum(Product)Lattice" algorithm 
SumLatticeDefault.Factory  
SumLatticeFactory  Provides factory methods to create inference engine for training a transducer. 
SumLatticeScaling  
SumLatticeScaling.Factory  
ThreadedOptimizable  An adaptor for optimizables based on batch values/gradients. 
TokenAccuracyEvaluator  Evaluates a transducer model based on predictions of individual tokens. 
Transducer  A base class for all sequence models, analogous to classify.Classifier . 
Transducer.State  An abstract class used to represent the states of the transducer. 
Transducer.TransitionIterator  An abstract class to iterate over the states of the transducer. 
TransducerEvaluator  An abstract class to evaluate a transducer model. 
TransducerTrainer  An abstract class to train and evaluate a transducer model. 
TransducerTrainer.ByIncrements  
TransducerTrainer.ByInstanceIncrements  
ViterbiWriter  Prints the input instances along with the features and the true and predicted labels to a file. 
Transducers, including Conditional Random Fields (CRFs).


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