Chinese Word Segmentation On Msr
Métriques
F1
Precision
Recall
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | F1 | Precision | Recall | Paper Title | Repository |
|---|---|---|---|---|---|
| Glyce + BERT | 98.3 | 98.2 | 98.3 | Glyce: Glyph-vectors for Chinese Character Representations | |
| BABERT-LE | 98.63 | - | - | Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling | |
| ZEN (Random Init) | 97.89 | - | - | ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations | |
| BABERT | 98.44 | - | - | Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling | |
| WMSeg + ZEN | 98.40 | - | - | Improving Chinese Word Segmentation with Wordhood Memory Networks | |
| ZEN (Init with Chinese BERT) | 98.35 | - | - | ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations |
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