Sequor is a sequence labeler based on Collins's (2002) perceptron. Sequor has a flexible feature template language and is meant mainly for NLP applications such as Named Entity labeling, Part of Speech tagging or syntactic chunking. It includes the SemiNER named entity recognizer, with pre-trained models for German and English (see Named Entity Recognition (SemiNER)).
Sequor is especially useful if your dataset has a large label set. In this case it is likely to run faster and allow you to use much less RAM than a sequence labeler based on Conditional Random Fields. Additionally sequor implements options which allow you to control the size of model and tradeoff speed against accuracy:
size of the beam
See https://bitbucket.org/gchrupala/sequor/wiki/Options for details.