sklearn-crfsuite 0.3.6

CRFsuite (python-crfsuite) wrapper which provides interface simlar to scikit-learn

sklearn-crfsuite is a thin CRFsuite (python-crfsuite) wrapper which provides interface simlar to scikit-learn. sklearn_crfsuite.CRF is a scikit-learn compatible estimator: you can use e.g. scikit-learn model selection utilities (cross-validation, hyperparameter optimization) with it, or save/load CRF models using joblib.

License is MIT.

Documentation can be found here.

Changes

0.3.6 (2017-06-22)

• added sklearn_crfsuite.metrics.flat_recall_score.

0.3.5 (2017-03-21)

• Properly close file descriptor in FileResource.cleanup;

• declare Python 3.6 support, stop testing on Python 3.3.

0.3.4 (2016-11-17)

• Small formatting fixes.

0.3.3 (2016-03-15)

• scikit-learn dependency is now optional for sklearn_crfsuite; it is required only when you use metrics and scorers;

• added metrics.flat_precision_score.

0.3.2 (2015-12-18)

• Ignore more errors in FileResource.__del__.

0.3.1 (2015-12-17)

• Ignore errors in FileResource.__del__.

0.3 (2015-12-17)

• Added sklearn_crfsuite.metrics.sequence_accuracy_score() function and related sklearn_crfsuite.scorers.sequence_accuracy;

• FileResource.__del__ method made more robust.

0.2 (2015-12-11)

• backwards-incompatible: crf.tagger attribute is renamed to crf.tagger_; when model is not trained accessing this attribute no longer raises an exception, its value is set to None instead.

• new CRF attributes available after training:

  • classes_

  • size_

  • num_attributes_

  • attributes_

  • state_features_

  • transition_features_

• Tutorial is added.

0.1 (2015-11-27)

Initial release.