references/python_crfsuite.md

---
title: "python-crfsuite"
type: "resource"
url: "https://github.com/scrapinghub/python-crfsuite"
---

## Overview

python-crfsuite provides Python bindings for the CRFsuite conditional random field toolkit, enabling efficient sequence labeling in Python.

## Key Information

| Field | Value |
|-------|-------|
| **GitHub** | https://github.com/scrapinghub/python-crfsuite |
| **PyPI** | https://pypi.org/project/python-crfsuite/ |
| **Documentation** | https://python-crfsuite.readthedocs.io/ |
| **License** | MIT (python-crfsuite), BSD (CRFsuite) |
| **Latest Version** | 0.9.12 (December 2025) |
| **Stars** | 771 |

## Features

- **Fast Performance**: Faster than official SWIG wrapper
- **No External Dependencies**: CRFsuite bundled; NumPy/SciPy not required
- **Python 2 & 3 Support**: Works with both Python versions
- **Cython-based**: High-performance C++ bindings

## Installation

```bash
# Using pip
pip install python-crfsuite

# Using conda
conda install -c conda-forge python-crfsuite
```

## Usage

### Training

```python
import pycrfsuite

# Create trainer
trainer = pycrfsuite.Trainer(verbose=True)

# Add training data
for xseq, yseq in zip(X_train, y_train):
    trainer.append(xseq, yseq)

# Set parameters
trainer.set_params({
    'c1': 1.0,           # L1 regularization
    'c2': 0.001,         # L2 regularization
    'max_iterations': 100,
    'feature.possible_transitions': True
})

# Train model
trainer.train('model.crfsuite')
```

### Inference

```python
import pycrfsuite

# Load model
tagger = pycrfsuite.Tagger()
tagger.open('model.crfsuite')

# Predict
y_pred = tagger.tag(x_seq)
```

### Feature Format

Features are lists of strings in `name=value` format:

```python
features = [
    ['word=hello', 'pos=NN', 'is_capitalized=True'],
    ['word=world', 'pos=NN', 'is_capitalized=False'],
]
```

## Training Algorithms

| Algorithm | Description |
|-----------|-------------|
| `lbfgs` | Limited-memory BFGS (default) |
| `l2sgd` | SGD with L2 regularization |
| `ap` | Averaged Perceptron |
| `pa` | Passive Aggressive |
| `arow` | Adaptive Regularization of Weights |

## Parameters (L-BFGS)

| Parameter | Default | Description |
|-----------|---------|-------------|
| `c1` | 0 | L1 regularization coefficient |
| `c2` | 1.0 | L2 regularization coefficient |
| `max_iterations` | unlimited | Maximum iterations |
| `num_memories` | 6 | Number of memories for L-BFGS |
| `epsilon` | 1e-5 | Convergence threshold |

## Related Projects

- **sklearn-crfsuite**: Scikit-learn compatible wrapper
- **CRFsuite**: Original C++ implementation

## Citation

```bibtex
@misc{python-crfsuite,
  author = {Scrapinghub},
  title = {python-crfsuite: Python binding to CRFsuite},
  year = {2014},
  publisher = {GitHub},
  url = {https://github.com/scrapinghub/python-crfsuite}
}

@misc{crfsuite,
  author = {Okazaki, Naoaki},
  title = {CRFsuite: A fast implementation of Conditional Random Fields},
  year = {2007},
  url = {http://www.chokkan.org/software/crfsuite/}
}
```