Natural Language Inference On Mednli
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy |
|---|---|
| biomedgpt-a-unified-and-generalist-biomedical | 83.83 |
| scifive-a-text-to-text-transformer-model-for | 86.57 |
| characterbert-reconciling-elmo-and-bert-for | 84.95 |
| transfer-learning-in-biomedical-natural | 84.00 |
| saama-research-at-mediqa-2019-pre-trained | 83.45 |
| patient-trajectory-prediction-integrating | 86.59 |
| bioelectra-pretrained-biomedical-text-encoder | 86.34 |