Sentiment Analysis On Mpqa
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | Paper Title | Repository |
|---|---|---|---|
| byte mLSTM7 | 88.8 | A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors | |
| STM+TSED+PT+2L | 89.83 | The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning | |
| RoBERTa-large 355M + Entailment as Few-shot Learner | 90.8 | Entailment as Few-Shot Learner | |
| USE_T+DAN (w2v w.e.) | 88.14 | Universal Sentence Encoder |
0 of 4 row(s) selected.