FlexCRFs is a conditional random field toolkit for segmenting and labeling sequence data written in C/C++ using STL library. It was implemented based on the theoretic model presented in (Lafferty et al. 2001) and (Sha and Pereira 2003). The toolkit uses L-BFGS (Liu and Nocedal 1989) - an advanced convex optimization procedure - to train CRF models. FlexCRFs was designed to deal with hundreds of thousand data sequences and millions of features. FlexCRFs supports both first-order and second-order Markov CRFs. We have tested FlexCRFs on Linux (Red Hat, Fedora), Sun Solaris, and MS Windows with MS Visual C++.
PCRFs is a parallel version of FlexCRFs that allows us to train conditional random fields on massively parallel processing systems supporting Message Passing Interface (MPI). PCRFs helps to train conditional random fields on large-scale datasets containing up to millions of data sequences. We have tested PCRFs on large parallel systems, such as Cray XT3, SGI Altix, and IBM SP.
All comments, suggestions, and error detections are highly appreciated.
- Changes to previous version:
Initial Announcement on mloss.org.
No one has posted any comments yet. Perhaps you'd like to be the first?
Leave a comment
You must be logged in to post comments.