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.
- added sklearn_crfsuite.metrics.flat_recall_score.
- Properly close file descriptor in FileResource.cleanup;
- declare Python 3.6 support, stop testing on Python 3.3.
- Small formatting fixes.
- scikit-learn dependency is now optional for sklearn_crfsuite; it is required only when you use metrics and scorers;
- added metrics.flat_precision_score.
- Ignore more errors in FileResource.__del__.
- Ignore errors in FileResource.__del__.
- Added sklearn_crfsuite.metrics.sequence_accuracy_score() function and related sklearn_crfsuite.scorers.sequence_accuracy;
- FileResource.__del__ method made more robust.
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:
Tutorial is added.
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