NCRF++: An Open-source Neural Sequence Labeling Toolkit
About
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.
Jie Yang, Yue Zhang• 2018
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Named Entity Recognition | CoNLL 2003 (test) | F1 Score91.35 | 539 | |
| Named Entity Recognition | CoNLL English 2003 (test) | F1 Score91.35 | 135 | |
| Chunking | CoNLL 2000 (test) | F1 Score95.06 | 88 | |
| Part-of-Speech Tagging | WSJ (test) | Accuracy97.49 | 51 |
Showing 4 of 4 rows