Document Classification On Reuters 21578
Métriques
F1
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | F1 |
|---|---|
| magnet-multi-label-text-classification-using | 89.9 |
| vector-of-locally-aggregated-word-embeddings | 89.3 |
| rep-the-set-neural-networks-for-learning-set | - |
| rethinking-complex-neural-network | 87.0 |
| speeding-up-word-movers-distance-and-its | - |
| docbert-bert-for-document-classification | 88.9 |
| improving-document-classification-with-multi | 82.71 |
| text-classification-with-word-embedding | - |