crfsuite-0.12.Windows.x86.crfsuite.dll 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
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.?AVbad_alloc@std@@ 0? .?AVexception@std@@ 0? .?AVlogic_error@std@@ 0? .?AVinvalid_argument@std@@ 0? .?AVruntime_error@std@@ 0? .?AVTrainer@CRFSuite@@ 0? .?AVTagger@CRFSuite@@ 0? .?AV?$basic_stringbuf@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@ 0? .?AV?$basic_streambuf@DU?$char_traits@D@std@@@std@@ 0? .?AV?$basic_stringstream@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@ 0? .?AV?$basic_iostream@DU?$char_traits@D@std@@@std@@ 0? .?AV?$basic_istream@DU?$char_traits@D@std@@@std@@ 0? .?AV?$basic_ios@DU?$char_traits@D@std@@@std@@ 0? .?AVios_base@std@@ 0? .?AV?$_Iosb@H@std@@ 0? .?AV?$basic_ostream@DU?$char_traits@D@std@@@std@@ 0? .?AVDirector@Swig@@ 0? .?AVout_of_range@std@@ 0? .?AVSwigDirector_Trainer@@ T?\? ???0? .?AVtype_info@@ N?@???D ???????? Operation successful Invalid argument to function Insufficient memory to complete operation Entry does not exist No more entries in range Entry already exists UNKNOWN ERROR type type The strategy for updating feature weights: {
0: PA without slack variables,
1: PA type I,
2: PA type II
}.
type c c The aggressiveness parameter. c error_sensitive error_sensitive Consider the number of incorrect labels to the cost function. error_sensitive averaging averaging averaging Compute the average of feature weights (similarly to Averaged Perceptron). max_iterations max_iterations The maximum number of iterations. max_iterations epsilon epsilon The stopping criterion (the mean loss). epsilon Passive Aggressive
type: %d
c: %f
error_sensitive: %d
averaging: %d
max_iterations: %d
epsilon: %f
***** Iteration #%d *****
Loss: %f
Feature norm: %f
Seconds required for this iteration: %.3f
Terminated with the stopping criterion
Total seconds required for training: %.3f
Feature generation
type: CRF1d
feature.minfreq: %f
feature.possible_states: %d
feature.possible_transitions: %d
Number of features: %d
Seconds required: %.3f
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
Seconds required: %.3f
feature.minfreq feature.minfreq The minimum frequency of features. feature.minfreq feature.possible_states feature.possible_states Force to generate possible state features. feature.possible_states feature.possible_transitions feature.possible_transitions Force to generate possible transition features. feature.possible_transitions ***** Epoch #%d *****
ERROR: overflow loss
Loss: %f
Improvement ratio: %f
Feature L2-norm: %f
Learning rate (eta): %f
Total number of feature updates: %.0f
Seconds required for this iteration: %.3f
SGD terminated with the stopping criteria
SGD terminated with the maximum number of iterations
SGD terminated with error code (%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
%f (worse)
Best learning rate (eta): %f
Seconds required: %.3f
c2 c2 Coefficient for L2 regularization. c2 max_iterations max_iterations The maximum number of iterations (epochs) for SGD optimization. max_iterations period period The duration of iterations to test the stopping criterion. period delta delta calibration.eta calibration.rate 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. delta calibration.eta The initial value of learning rate (eta) used for calibration. calibration.eta calibration.rate The rate of increase/decrease of learning rate for calibration. calibration.rate calibration.samples calibration.samples The number of instances used for calibration. calibration.samples calibration.candidates calibration.candidates The number of candidates of learning rate. calibration.candidates calibration.max_trials calibration.max_trials Stochastic Gradient Descent (SGD)
The maximum number of trials of learning rates for calibration. calibration.max_trials c2: %f
max_iterations: %d
period: %d
delta: %f
Loss: %f
Total seconds required for training: %.3f
max_iterations max_iterations The maximum number of iterations. max_iterations epsilon epsilon The stopping criterion (the ratio of incorrect label predictions). epsilon Averaged perceptron
max_iterations: %d
epsilon: %f
***** Iteration #%d *****
Loss: %f
Feature norm: %f
Seconds required for this iteration: %.3f
Terminated with the stopping criterion
Total seconds required for training: %.3f
variance variance The initial variance of every feature weight. variance gamma gamma Tradeoff parameter. gamma max_iterations max_iterations The maximum number of iterations. max_iterations epsilon epsilon The stopping criterion (the mean loss). epsilon Adaptive Regularization of Weights (AROW)
variance: %f
gamma: %f
max_iterations: %d
epsilon: %f
***** Iteration #%d *****
Loss: %f
Feature norm: %f
Seconds required for this iteration: %.3f
Terminated with the stopping criterion
Total seconds required for training: %.3f
%d %f int float string unknown ***** Iteration #%d *****
Loss: %f
Feature norm: %f
Error norm: %f
Active features: %d
Line search trials: %d
Line search step: %f
Seconds required for this iteration: %.3f
c1 c1 Coefficient for L1 regularization. c1 c2 c2 Coefficient for L2 regularization. c2 max_iterations max_iterations The maximum number of iterations for L-BFGS optimization. max_iterations num_memories num_memories epsilon The duration of iterations to test the stopping criterion. The number of limited memories for approximating the inverse hessian matrix. num_memories epsilon Epsilon for testing the convergence of the objective. epsilon period period period delta delta 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. delta linesearch linesearch 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
}
MoreThuente linesearch max_linesearch max_linesearch The maximum number of trials for the line search algorithm. max_linesearch L-BFGS optimization
c1: %f
c2: %f
num_memories: %d
max_iterations: %d
epsilon: %f
stop: %d
delta: %f
linesearch: %s
linesearch.max_iterations: %d
Backtracking StrongBacktracking 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)
Total seconds required for training: %.3f
OK (%f)
FAIL: %f (%f)
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)... wb lCRF FOMC LFRF AFRF FEAT rb FILEHEADER = {
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 = {
%5d: %s
}
TRANSITIONS = {
(%d) %s --> %s: %f
}
STATE_FEATURES = {
(%d) %s --> %s: %f
}
Holdout group: %d
train/ crf1d/ lbfgs l2sgd averaged-perceptron passive-aggressive arow %Y-%m-%dT%H:%M:%SZ 0 %d .
dictionary [UNKNOWN] 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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