CRFsuite (python-crfsuite) wrapper which provides interface simlar to scikit-learn
Project description
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.
Project details
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