sklearn-crfsuite

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

0.5.0 (2024-06-18)

• The CRF.predict() and CRF.predict_marginals() methods now return a numpy array, as expected by newer versions of scikit-learn.
• Fixed the parameters of a call to the sklearn.metrics.classification_report() function from the flat_classification_report() function.
• sequence_accuracy_score now works with numpy arrays.

0.4.0 (2024-06-18)

• Dropped official support for Python 3.7 and lower, and added official support for Python 3.8 and higher.
• Added support for scikit-learn 0.24.0 and higher.
• Increased minimum versions of dependencies as follows:
  • python-crfsuite: 0.8.3 → 0.9.7
  • scikit-learn: 0.24.0
  • tabulate: 0.4.2
• Internal changes: enabled GitHub Actions for CI, added a tox environment for minimum supported versions of dependencies, applied automatic code cleanups.

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.