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    • crfsuite-0.12.Mac.x86_64.libcrfsuite.dylib Maven / Gradle / Ivy

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    Jcrfsuite is a Java interface for crfsuite, a fast implementation of Conditional Random Fields, using SWIG and class injection technique

    There is a newer version: 0.6.1
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:???L??$?H?T$8H??L?D$A?T$P??$?E1???~A1?H??$?HcNj?H??$?Hc?<0uIc?A????H??$????;?$?|?E??D??$??B1???H??$?Hc???Hc?H??$????$?9?|؅??H??$?H??$?1?fW?f.?Hc??H??9????Y??X?u?fD??$???$??T$h?*L$4??$?f(??Yȅ?~g1???H??$?Hc?H??$?f(???Hc??Y?H??I?L?X?f(?HD$@?Y??X?9?$???D$p?X?$??D$p?D$4A??D9|$P?????V???fW??5???H?Ѓ?H??H	??H*??X??C???H?Ѓ?H??H	??H*??X???????%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?~?%?%?%?%?%	?%?%
?%?%?%?%?%?%?%?%?%?%!?%#?%%?%'?%)?%+?%-?%/?%1?%3?%5?%7?%9?%;?%=?%??%A?%C?%E?%G?%I?%K?%M?%O?%Q?%S?%U?%W?%Y?%[L?~?2??L??}?2??L??}?2??L?u}?2??L??|?2??L?-|?x2??L??{?l2??L??}?`2??L?9|?T2??L??}?H2??L??{?<2??L??{?02??L??}?$2??L?5}?2??L?|?2??L?%}?2??L?y{??1??L?]{??1??L??{??1??L??{??1??L?Y{??1??L?e{?1??L?q{?1??L?~?1??L?1{?1??L?%~?1??L??|?|1??L?-{?p1??L??{?d1??L??|?X1??L??|?L1??L??{?@1??L?y{?41??L??|?(1??L??z?1??L??z?1??L??z?1??L?u{??0??L?Q|??0??L?]|??0??L?A|??0??L??{??0??L?y{?0??L??{?0??L?A{?0??L?u{?0??L?9{?0??L?{?0??L?{?t0??L??{?h0??L??y?\0??L??y?P0??L??y?D0??L?
z?80??L?	z?,0??L?-z? 0??L?){?0??L?
z?0??L?Yz??/??L?Ez??/??L?z??/??L??y??/??L??y??/??L??z??/??L??y?/??L?Mz?/??L?Qz?/??L??x?/??L??y?/??L??x?x/??L?Yz?l/??L??y?`/??L?z?T/??L?Ez?H/??L??y?::value_type const & reference is nullvector::reservestd::vector< CRFSuite::Item >::value_type const & reference is nullCQDBCheck for the partition factor... OK (%f)
FAIL: %f (%f)
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)... feature.minfreqfeature.possible_statesfeature.possible_transitionsThe minimum frequency of features.Force to generate possible state features.Force to generate possible transition features.Storing the model
Number of active features: %d (%d)
Number of active attributes: %d (%d)
Number of active labels: %d (%d)
Writing labels
Writing attributes
Writing feature references for transitions
Writing feature references for attributes
Feature generation
type: CRF1d
feature.minfreq: %f
feature.possible_states: %d
feature.possible_transitions: %d
Number of features: %d
wbrbFILEHEADER = {
  magic: %c%c%c%c
  size: %d
  type: %c%c%c%c
  version: %d
  num_features: %d
  num_labels: %d
  num_attrs: %d
  off_features: 0x%X
  off_labels: 0x%X
  off_attrs: 0x%X
  off_labelrefs: 0x%X
  off_attrrefs: 0x%X
}
LABELS = {
  %5d: %s
ATTRIBUTES = {
TRANSITIONS = {
  (%d) %s --> %s: %f
STATE_FEATURES = {
Performance by label (#match, #model, #ref) (precision, recall, F1):
[UNKNOWN]    %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)
Holdout group: %d
crf1d/lbfgsl2sgdaveraged-perceptronpassive-aggressivearow%Y-%m-%dT%H:%M:%SZ0%d.%fEntry already existsUNKNOWN ERROROperation successfulInvalid argument to functionInsufficient memory to complete operationEntry does not existNo more entries in rangevariancegammaThe initial variance of every feature weight.Tradeoff parameter.The maximum number of iterations.The stopping criterion (the mean loss).Adaptive Regularization of Weights (AROW)
variance: %f
gamma: %f
epsilon: %f
Loss: %f
Seconds required for this iteration: %.3f
Terminated with the stopping criterion
Total seconds required for training: %.3f
max_iterationsThe stopping criterion (the ratio of incorrect label predictions).Averaged perceptron
max_iterations: %d
Feature norm: %f
c2periodcalibration.etacalibration.ratecalibration.samplescalibration.candidatescalibration.max_trialsCoefficient 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.***** Epoch #%d *****
ERROR: overflow loss
Improvement ratio: %f
Feature L2-norm: %f
Learning rate (eta): %f
Total number of feature updates: %.0f
SGD terminated with the stopping criteria
SGD terminated with the maximum number of iterations
Stochastic Gradient Descent (SGD)
c2: %f
period: %d
Calibrating the learning rate (eta)
calibration.eta: %f
calibration.rate: %f
calibration.samples: %d
calibration.candidates: %d
calibration.max_trials: %d
Initial loss: %f
Trial #%d (eta = %f): %f
Best learning rate (eta): %f
Seconds required: %.3f
%f (worse)
***** Iteration #%d *****
Error norm: %f
Active features: %d
Line search trials: %d
Line search step: %f
c1num_memoriesdeltalinesearchmax_linesearchCoefficient 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
}
MoreThuenteThe maximum number of trials for the line search algorithm.L-BFGS optimization
c1: %f
num_memories: %d
stop: %d
delta: %f
linesearch: %s
linesearch.max_iterations: %d
BacktrackingStrongBacktrackingL-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)
typecerror_sensitiveaveragingepsilonThe strategy for updating feature weights: {
    0: PA without slack variables,
    1: PA type I,
    2: PA type II
}.
The aggressiveness parameter.Consider the number of incorrect labels to the cost function.Compute the average of feature weights (similarly to Averaged Perceptron).Passive Aggressive
type: %d
c: %f
error_sensitive: %d
averaging: %d

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