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## Package cc.mallet.fst

Transducers, including Conditional Random Fields (CRFs).

See:
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 forward-backward 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 L1-regularization.
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 per-class 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 CRF-based 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 Forward-Backward "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.

## Package cc.mallet.fst Description

Transducers, including Conditional Random Fields (CRFs).

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