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crfsuite.linux_x86_32.lib.libcrfsuite.so Maven / Gradle / Ivy

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gamma: %f
max_iterations: %d
epsilon: %f
***** Iteration #%d *****
Loss: %f
Feature norm: %f
The initial variance of every feature weight.The maximum number of iterations.The stopping criterion (the mean loss).Adaptive Regularization of Weights (AROW)
Seconds required for this iteration: %.3f
Terminated with the stopping criterion
Total seconds required for training: %.3f
???????ư>Averaged perceptron
The stopping criterion (the ratio of incorrect label predictions).c2perioddeltacalibration.etacalibration.ratecalibration.samplescalibration.candidatescalibration.max_trials***** Epoch #%d *****
ERROR: overflow loss
Improvement ratio: %f
Feature L2-norm: %f
Learning rate (eta): %f
c2: %f
period: %d
delta: %f
calibration.eta: %f
calibration.rate: %f
calibration.samples: %d
calibration.candidates: %d
calibration.max_trials: %d
Initial loss: %f
Trial #%d (eta = %f): Best learning rate (eta): %f
Seconds required: %.3f
%f (worse)
Coefficient for L2 regularization.The maximum number of iterations (epochs) for SGD optimization.The duration of iterations to test the stopping criterion.The threshold for the stopping criterion; an optimization process stops when
the improvement of the log likelihood over the last ${period} iterations is no
greater than this threshold.The initial value of learning rate (eta) used for calibration.The rate of increase/decrease of learning rate for calibration.The number of instances used for calibration.The number of candidates of learning rate.The maximum number of trials of learning rates for calibration.Total number of feature updates: %.0f
SGD terminated with the stopping criteria
SGD terminated with the maximum number of iterations
Stochastic Gradient Descent (SGD)
Calibrating the learning rate (eta)
????????@?????????c1num_memoriesmax_linesearchMoreThuenteError norm: %f
Active features: %d
Line search trials: %d
Line search step: %f
L-BFGS optimization
c1: %f
num_memories: %d
stop: %d
linesearch: %s
StrongBacktrackingCoefficient for L1 regularization.The maximum number of iterations for L-BFGS optimization.The number of limited memories for approximating the inverse hessian matrix.Epsilon for testing the convergence of the objective.The threshold for the stopping criterion; an L-BFGS iteration stops when the
improvement of the log likelihood over the last ${period} iterations is no
greater than this threshold.The line search algorithm used in L-BFGS updates:
{   'MoreThuente': More and Thuente's method,
    'Backtracking': Backtracking method with regular Wolfe condition,
    'StrongBacktracking': Backtracking method with strong Wolfe condition
}
The maximum number of trials for the line search algorithm.linesearch.max_iterations: %d
L-BFGS resulted in convergence
L-BFGS terminated with the stopping criteria
L-BFGS terminated with the maximum number of iterations
L-BFGS terminated with error code (%d)
?h㈵??>typecerror_sensitiveaveragingThe aggressiveness parameter.Passive Aggressive
type: %d
c: %f
error_sensitive: %d
averaging: %d
The strategy for updating feature weights: {
    0: PA without slack variables,
    1: PA type I,
    2: PA type II
}.
Consider the number of incorrect labels to the cost function.Compute the average of feature weights (similarly to Averaged Perceptron).Check for the partition factor... Check for the sequence %d-%d-%d... Check for the marginal probability (0,%d)... Check for the marginal probability (1,%d)... Check for the marginal probability (2,%d)... Check for the marginal probability (0,%d)-(1,%d)... Check for the marginal probability (1,%d)-(2,%d)... OK (%f)
FAIL: %f (%f)
????????Z???????Z????????$WUUU???$WUUU????TUU????TUU???????????I?j?V??I?j?V????
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*?99o??ݥL?>o??ݥL?>D?^b?"?>D?^b?"?>(?~?wc>(?~?wc>ʫy????>ʫy????>@.??@.??Ҽz?+#??Ҽz?+#???9??B.?@?9??B.?@??+eG????+eG???????????????????????333333??333333????????????&?.>??????????FILEHEADER = {
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  off_features: 0x%X
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LABELS = {
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ATTRIBUTES = {
TRANSITIONS = {
  (%d) %s --> %s: %f
STATE_FEATURES = {
rbwbStoring the model
Writing labels
Writing attributes
Feature generation
type: CRF1d
feature.minfreq: %f
feature.possible_states: %d
Number of features: %d
feature.minfreqfeature.possible_statesfeature.possible_transitionsNumber of active features: %d (%d)
Number of active attributes: %d (%d)
Number of active labels: %d (%d)
Writing feature references for transitions
Writing feature references for attributes
feature.possible_transitions: %d
The minimum frequency of features.Force to generate possible state features.Force to generate possible transition features.????????????????Holdout group: %d
train/crf1d/lbfgsl2sgdaveraged-perceptronpassive-aggressivearow???????t???????????4???Performance by label (#match, #model, #ref) (precision, recall, F1):
    %s: (%d, %d, %d) (******, ******, ******)
    %s: (%d, %d, %d) (%1.4f, %1.4f, %1.4f)
Macro-average precision, recall, F1: (%f, %f, %f)
Item accuracy: %d / %d (%1.4f)
Instance accuracy: %d / %d (%1.4f)
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