labeling sequential data. The first priority of this software is to
train and use CRF models as fast as possible even at the expense of
its memory space and code generality. CRFsuite runs 5.4 - 61.8 times
faster than C++ implementations for training. CRFsuite supports
parameter estimation with L1 regularization (Laplacian prior) using
Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2
regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS)
method.
