structural_svm

Usage: ssvm_tool [OPTIONS]... [FILES]...

-h, --help Print help and exit
-V, --version Print version and exit

Training options:

-c, --cost=FLOAT set cost of SSVMs (default=`1')
-m, --model=STRING set model file name
-b, --binary save/load model in binary format (default=off)
 --source=STRING source model file for domain adaptation
 --skip_eval skip test set evaluation in the middle of training (default=off)
 --owps_format use One Word Per Sentence (OWPS) format (default=off)
 --hash=INT use hash feature and set number of predicates (default=`0')
-s, --support use support feature (default is all feature) (default=off)
 --general general feature mode (feature:value format) (default is binary feature) (default=off)
-r, --random=INT use random_shuffle in train_data (disabled if use 0) (default=`0')
 --train_num=INT set number of sentence in train_data for training (for experiments) (disabled if use 0) (default=`0')
-v, --verbose verbose mode (default=off)

Structural SVM options:

-e, --epsilon=FLOAT set epsilon (fsmo, fsmo_joint) (default=`0.01')
 --buf=INT set the number of new constraints to accumulated before recomputing the QP (fsmo, pegasos) (default=`100')
 --rm_inactive=INT inactive constraints are removed (iteration) (fsmo, fsmo_joint) (default=`50')
 --final_opt do final optimal check in shrinking (fsmo, fsmo_joint) (default=off)
 --comment use comment info in save_slack() (fsmo) (default=off)

Pegasos options:

-i, --iter=INT iterations for training algorithm (pegasos) (default=`100')
 --period=INT save model periodically (pegasos) (default=`0')

Latent Strucutral SVM options:

--latent use CCCP-based Latent SSVM (doesn't support sequence labeling) (default=off)
--latent_SPL use Self-Paced Learning for Latent SSVM (doesn't support sequence labeling) (default=off)

Joint Strucutral SVM options:

--joint use Joint model (y+z) using modified Latent SSVM (with --y_data, --z_data, --y_cost, and --z_cost options) (default=off)
--joint_SPL use Joint model (y+z) using Self-Paced Learning for modified Latent SSVM (with --y_data, --z_data, --y_cost, and --z_cost options) (default=off)
--y_data=STRING set file name for y_train_data (y is visible and z is hidden)
--z_data=STRING set file name for z_train_data (y is hidden and z is visible)
--y_cost=FLOAT set cost of y_train_data in joint model (default=`1')
--z_cost=FLOAT set cost of z_train_data in joint model (default=`1')
--y_train_num=INT set number of sentences of y_train_data for training (for experiments) (disabled if use 0) (default=`0')
--z_train_num=INT set number of sentences of y_train_data for training (for experiments) (disabled if use 0) (default=`0')
--init_iter=INT initial iterations for Joint SSVM training algorithm (default=`10')

Predict options:

-o, --output=STRING prediction output filename
 --nbest=INT print N-best result (default=`1')
 --beam=INT set number of beam in search (disabled if use 0) (default=`0')

Convert option:

-t, --threshold=FLOAT set threshold (convert mode) (default=`1e-04')

Group: MODE:

-p, --predict prediction mode, default is training mode
 --show show-feature mode
 --convert convert mode ('txt model to bin model' or 'bin model to txt model (with -b)') and remove zero features (with --threshold option)
 --convert2 convert2 mode (all_feaure model to support_feature model) and remove zero features (with --threshold option)
 --convert3 convert3 mode (support_feature model to all_feature model) and remove zero features (with --threshold option)
 --modify=STRING modify mode (modify feature weight), the option file is a list of feature weight
 --domain domain adaptation (Prior model) for structural SVM (with fsmo/fsmo_joint/pegasos algorithms and source option)

Group: Parameter Estimate Method for structural SVM:

--fsmo use Fixed-threshold SMO for structural SVM (shared slack)
--fsmo_joint use FSMO + joint constraint (1-slack) using Gram matrix
--fsmo_joint2 use FSMO + joint constraint (1-slack) without Gram matrix (slow version)
--pegasos use Pegasos in primal optimization (random shuffled train_data) (default method)