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

Project description

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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.

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