Natural Language Understanding On Pdp60
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy |
|---|---|
| unsupervised-deep-structured-semantic-models | 78.3 |
| a-simple-method-for-commonsense-reasoning | 53.3 |
| bert-pre-training-of-deep-bidirectional | 78.3 |
| attention-is-not-all-you-need-for-commonsense | 68.3 |
| attention-is-not-all-you-need-for-commonsense | 53.3 |
| unsupervised-deep-structured-semantic-models | 75 |
| unsupervised-deep-structured-semantic-models | 75.0 |
| probabilistic-reasoning-via-deep-learning | 55.0 |
| attention-is-all-you-need | 58.3 |
| a-simple-method-for-commonsense-reasoning | 60.0 |
| a-hybrid-neural-network-model-for-commonsense | 90 |
| attention-is-not-all-you-need-for-commonsense | 66.7 |
| unsupervised-deep-structured-semantic-models | 76.7 |