NCRF++: An Open-source Neural Sequence Labeling Toolkit

Jie Yang, Yue Zhang
6/14/2018
cs.CL

Official code: jiesutd/NCRFpp (1,901 stars, Apache-2.0, PyTorch, last commit 2022-06-30).

Abstract

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer. It provides users with an inference for building the custom model structure through configuration file with flexible neural feature design and utilization. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. It also includes the implementations of most state-of-the-art neural sequence labeling models such as LSTM-CRF, facilitating reproducing and refinement on those methods.

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Code Implementations(1)

jiesutd/NCRFppOfficial100%
PyTorchApache-2.0

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

1,901433PythonDec 6, 20174 years agoApache-2.0
artificial-intelligencechar-cnnchar-rnnchunkingcnn+11 more

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Cite this paper

@article{yang2018ncrf,
  title  = {NCRF++: An Open-source Neural Sequence Labeling Toolkit},
  author = {Jie Yang and Yue Zhang},
  year   = {2018},
  eprint = {1806.05626},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1806.05626}
}

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