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Started In
December 2013


The CRF package implements linear-chain Conditional Random Fields. CRFs are a probabilistic framework for labeling sequential data.


julia> using CRF
julia> crf = Sequence(x, y, features)
julia> loglikelihood(crf)
julia> loglikelihood_gradient(crf)
julia> label(crf)

The example directory contains a detailed documentation.

Further Reading

  • Charles Sutton, Andrew McCallum. An Introduction to Conditional Random Fields for Relational Learning. Introduction to Statistical Relational Learning, Vol. 93, pp. 142-146, 2007.

  • John Lafferty, Andrew McCallum, Fernando Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML-2001), 2001.

  • Hanna M. Wallach. Conditional Random Fields: An Introduction. Technical Report MS-CIS-04-21. Department of Computer and Information Science, University of Pennsylvania, 2004.

  • Thomas G. Dietterich. Machine Learning for Sequential Data: A Review. In Structural, Syntactic, and Statistical Pattern Recognition; Lecture Notes in Computer Science, Vol. 2396, T. Caelli (Ed.), pp. 15–30, Springer-Verlag, 2002.

More material on CRFs can be found here.