Text Classification On Rcv1
Métriques
P@1
P@3
P@5
nDCG@1
nDCG@3
nDCG@5
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | P@1 | P@3 | P@5 | nDCG@1 | nDCG@3 | nDCG@5 | Paper Title | Repository |
|---|---|---|---|---|---|---|---|---|
| NLP-Cap | 97.05 | 81.27 | 56.33 | 97.05 | 92.47 | 93.11 | Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications | |
| oh-CNN + two LSTM tv-embed. | - | - | - | - | - | - | Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings | - |
| HiLAP (bow-CNN) | - | - | - | - | - | - | Hierarchical Text Classification with Reinforced Label Assignment | |
| MAGNET | - | - | - | - | - | - | MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network |
0 of 4 row(s) selected.