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.

SimpleTagger 
This class's main method trains, tests, or runs a generic CRFbased
sequence tagger.

SimpleTagger.SimpleTaggerSentence2FeatureVectorSequence 

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.
