sklearn-crfsuite
CRFsuite (python-crfsuite) wrapper which provides interface simlar to scikit-learn
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, low install friction, and solves a real problem—making CRFs accessible within scikit-learn workflows. Use it if you need sequence labeling and want to leverage scikit-learn's ecosystem for model selection and evaluation. The MIT license adds no restrictions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with four straightforward runtime dependencies (python-crfsuite, scikit-learn, tabulate, tqdm).
- Actively maintained as of April 2026 with recent fixes to numpy array compatibility.
License · maintenance · safety
MIT license (permissive) — MIT license is permissive; you can use this package in commercial and proprietary projects without restriction, though you must include the license notice.
last release 2024-06-18 (787 days) · last repo commit 2026-04-08 · 440 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 591,755 downloads/mo, #5,855 on PyPI
Alternatives
Verify before relying
pip install sklearn-crfsuite
from sklearn_crfsuite import CRF
crf = CRF()
crf.fit(X_train, y_train)
predictions = crf.predict(X_test)- Whether the package's 'Alpha' status reflects actual stability concerns or is simply outdated classifier metadata.
- Performance characteristics and typical model training time for large sequence datasets.
What it is and what it does
sklearn-crfsuite is a thin wrapper around python-crfsuite that exposes Conditional Random Fields (CRFs) through a scikit-learn compatible interface. CRFs are a machine learning model designed for sequence labeling tasks—predicting a label for each element in a sequence, where the prediction for one element can depend on surrounding elements. This is useful for problems like named entity recognition, part-of-speech tagging, or any task where you need to label sequences of data.
Because it follows scikit-learn's estimator API, you can use all of scikit-learn's standard tools with it: cross-validation, grid search for hyperparameter tuning, pipeline integration, and model serialization with joblib. The package includes utility functions for sequence-level metrics (like sequence_accuracy_score) and flat metrics (precision, recall) tailored to sequence labeling evaluation. Recent updates (0.5.0) ensure compatibility with modern scikit-learn versions by returning numpy arrays from predict methods.
Use it for
- Named entity recognition: label person names, organizations, and locations in text using CRF with scikit-learn's cross-validation.
- Part-of-speech tagging: predict grammatical tags for words in sentences, leveraging scikit-learn's hyperparameter tuning.
- Information extraction: extract structured fields from documents by labeling token sequences with CRF models.
- Biomedical text mining: identify gene and protein mentions in scientific abstracts using sequence labeling.
- Slot filling in dialogue systems: label user utterance tokens to extract intent slots for conversational AI.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, low install friction, and solves a real problem—making CRFs accessible within scikit-learn workflows. Use it if you need sequence labeling and want to leverage scikit-learn's ecosystem for model selection and evaluation. The MIT license adds no restrictions.
Install
sklearn-crfsuite on PyPI
Before you install
Low friction: pure Python wheel with four straightforward runtime dependencies (python-crfsuite, scikit-learn, tabulate, tqdm). Actively maintained as of April 2026 with recent fixes to numpy array compatibility.
License in practice
MIT license is permissive; you can use this package in commercial and proprietary projects without restriction, though you must include the license notice.
Quickstart
pip install sklearn-crfsuite
from sklearn_crfsuite import CRF
crf = CRF()
crf.fit(X_train, y_train)
predictions = crf.predict(X_test)Verify before relying
- Whether the package's 'Alpha' status reflects actual stability concerns or is simply outdated classifier metadata.
- Performance characteristics and typical model training time for large sequence datasets.
Package facts
| License | MIT license permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagespython-crfsuitescikit-learntabulatetqdm |
| Maintenance | Actively maintained 787 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 591,755 / month, #5,855 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: sklearn_crfsuite-0.5.0-py2.py3-none-any.whl
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