Joint Word Segmentation and POS Tagging on Chinese (test)
84.5F1 ScoreViterbi
Evaluation Results
| Method | Links | |
|---|---|---|
| ViterbiEncoder layers=2 layers, Training approach=BCRF training2025.05 | 84.5 | |
| ViterbiEncoder layers=2 layers, Training approach=CRF training2025.05 | 84.3 | |
| ViterbiEncoder layers=4 layers, Training approach=CRF training2025.05 | 84.3 | |
| ViterbiEncoder layers=No self-attentive encoder, Training approach=CRF training2025.05 | 84.2 | |
| BCRFEncoder layers=2 layers, Training approach=BCRF training, Iterations=102025.05 | 84.2 | |
| ViterbiEncoder layers=4 layers, Training approach=BCRF training2025.05 | 84 | |
| BCRFEncoder layers=4 layers, Training approach=CRF training, Iterations=102025.05 | 83.8 | |
| BCRFEncoder layers=4 layers, Training approach=BCRF training, Iterations=102025.05 | 83.8 | |
| BCRFEncoder layers=No self-attentive encoder, Training approach=CRF training, Iterations=102025.05 | 83.7 | |
| BCRFEncoder layers=2 layers, Training approach=CRF training, Iterations=102025.05 | 83.7 | |
| ViterbiEncoder layers=No self-attentive encoder, Training approach=BCRF training2025.05 | 83.6 | |
| BCRFEncoder layers=2 layers, Training approach=BCRF training, Iterations=52025.05 | 83.3 | |
| BCRFEncoder layers=No self-attentive encoder, Training approach=BCRF training, Iterations=102025.05 | 83.2 | |
| BCRFEncoder layers=4 layers, Training approach=BCRF training, Iterations=52025.05 | 82.8 | |
| BCRFEncoder layers=No self-attentive encoder, Training approach=BCRF training, Iterations=52025.05 | 82.2 | |
| BCRFEncoder layers=4 layers, Training approach=CRF training, Iterations=52025.05 | 82 | |
| MFEncoder layers=No self-attentive encoder, Training approach=MF training, Iterations=102025.05 | 82 | |
| BCRFEncoder layers=No self-attentive encoder, Training approach=CRF training, Iterations=52025.05 | 81.9 | |
| BCRFEncoder layers=2 layers, Training approach=CRF training, Iterations=52025.05 | 81.7 | |
| MFEncoder layers=2 layers, Training approach=MF training, Iterations=102025.05 | 81.2 | |
| MFEncoder layers=4 layers, Training approach=MF training, Iterations=102025.05 | 81.2 | |
| ViterbiEncoder layers=4 layers, Training approach=MF training2025.05 | 81.1 | |
| MFEncoder layers=No self-attentive encoder, Training approach=MF training, Iterations=52025.05 | 81 | |
| MFEncoder layers=4 layers, Training approach=MF training, Iterations=52025.05 | 80.8 | |
| MFEncoder layers=2 layers, Training approach=MF training, Iterations=52025.05 | 80.7 | |
| ViterbiEncoder layers=2 layers, Training approach=MF training2025.05 | 79.9 | |
| ViterbiEncoder layers=No self-attentive encoder, Training approach=MF training2025.05 | 77.6 | |
| MFEncoder layers=No self-attentive encoder, Training approach=CRF training, Iterations=102025.05 | 74.6 | |
| MFEncoder layers=No self-attentive encoder, Training approach=CRF training, Iterations=52025.05 | 72.9 | |
| MFEncoder layers=4 layers, Training approach=CRF training, Iterations=102025.05 | 72.5 | |
| MFEncoder layers=2 layers, Training approach=CRF training, Iterations=102025.05 | 72.2 | |
| MFEncoder layers=4 layers, Training approach=CRF training, Iterations=52025.05 | 71.9 | |
| MFEncoder layers=2 layers, Training approach=CRF training, Iterations=52025.05 | 71.4 | |
| UnstructuredEncoder layers=4 layers2025.05 | 64.9 | |
| UnstructuredEncoder layers=2 layers2025.05 | 62 | |
| UnstructuredEncoder layers=No self-attentive encoder2025.05 | 47.3 |