1a416088f4
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.
9 lines
546 B
Text
9 lines
546 B
Text
CRFSuite is an implementation of Conditional Random Fields (CRFs) for
|
|
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.
|