Chinese Word Segmentation On Pku
Metrics
F1
Results
Performance results of various models on this benchmark
Model Name | F1 | Paper Title | Repository |
|---|---|---|---|
| BABERT-LE | 96.84 | Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling | |
| WMSeg + ZEN | 96.53 | Improving Chinese Word Segmentation with Wordhood Memory Networks | |
| Glyce + BERT | 96.7 | Glyce: Glyph-vectors for Chinese Character Representations | |
| BABERT | 96.70 | Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling |
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