Text Classification on Yahoo! Answer 500 labels
0.3092Top-1 Error RateSoftMatch/FreeMatch + SemiReward
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| SoftMatch/FreeMatch + SemiRewardBackbone=Bert [Devlin et al., 2018], SemiReward=true2023.10 | 0.3092 | 0.99 | |
| AdaMatch# Labels=500, Backbone=Bert-Base2023.05 | 0.3275 | — | |
| FreeMatch# Labels=500, Backbone=Bert-Base2023.05 | 0.3277 | — | |
| SoftMatch/FreeMatchBackbone=Bert [Devlin et al., 2018], SemiReward=false2023.10 | 0.3302 | — | |
| FixMatch# Labels=500, Backbone=Bert-Base2023.05 | 0.3303 | — | |
| SimMatch# Labels=500, Backbone=Bert-Base2023.05 | 0.3306 | — | |
| SoftMatch# Labels=500, Backbone=Bert-Base2023.05 | 0.3307 | — | |
| CoMatch# Labels=500, Backbone=Bert-Base2023.05 | 0.3348 | — | |
| FlexMatch + SemiRewardBackbone=Bert [Devlin et al., 2018], SemiReward=true2023.10 | 0.3364 | 0.99 | |
| ILL# Labels=500, Backbone=Bert-Base2023.05 | 0.338 | — | |
| FlexMatchBackbone=Bert [Devlin et al., 2018], SemiReward=false2023.10 | 0.3473 | — | |
| Pseudo LabelBackbone=Bert [Devlin et al., 2018], SemiReward=false2023.10 | 0.3487 | — | |
| VAT# Labels=500, Backbone=Bert-Base2023.05 | 0.3487 | — | |
| Pseudo Label + SemiRewardBackbone=Bert [Devlin et al., 2018], SemiReward=true2023.10 | 0.3508 | 0.99 | |
| Dash# Labels=500, Backbone=Bert-Base2023.05 | 0.3526 | — | |
| FlexMatch# Labels=500, Backbone=Bert-Base2023.05 | 0.3561 | — | |
| MixMatch# Labels=500, Backbone=Bert-Base2023.05 | 0.3575 | — | |
| Mean-Teacher# Labels=500, Backbone=Bert-Base2023.05 | 0.3709 | — | |
| Pseudo-Label# Labels=500, Backbone=Bert-Base2023.05 | 0.377 | — |