---
id: sklearn-crfsuite
version: "0.5.0"
license: MIT license
license_treatment: permissive
maintenance: active
---

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

License: permissive · Maintenance: active · Downloads: 591.8K/mo

## 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 above — verify before relying.

sklearn-crfsuite wraps CRFsuite (Conditional Random Fields) as a scikit-learn compatible estimator, letting you train sequence labeling models using scikit-learn's standard tools like cross-validation and hyperparameter optimization.

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

pip install sklearn-crfsuite
uv add sklearn-crfsuite
poetry add sklearn-crfsuite

## Installing sklearn-crfsuite

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: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 591.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

conditional random fields scikit-learn, CRF sequence labeling, sklearn compatible CRF estimator, named entity recognition CRF, sequence tagging with scikit-learn, python-crfsuite wrapper, structured prediction sklearn, sequence-labeling, conditional-random-fields, nlp

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