Text Classification On R8
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy |
|---|---|
| transformers-are-short-text-classifiers-a | 98.09 |
| transformers-are-short-text-classifiers-a | 98.28 |
| transformers-are-short-text-classifiers-a | 98.23 |
| transformers-are-short-text-classifiers-a | 97.981 |
| simple-spectral-graph-convolution | 97.4 |
| transformers-are-short-text-classifiers-a | 96.13 |
| simplifying-graph-convolutional-networks | 97.2 |
| distributed-word-representation-in-tsetlin | 97.50 |
| graph-star-net-for-generalized-multi-task-1 | 97.4 |
| transformers-are-short-text-classifiers-a | 96.98 |
| transformers-are-short-text-classifiers-a | 98.041 |
| transformers-are-short-text-classifiers-a | 98.451 |
| neural-attentive-bag-of-entities-model-for | 97.1 |
| text-level-graph-neural-network-for-text | 97.8 |
| graph-convolutional-networks-for-text | 97.07 |
| simplifying-graph-convolutional-networks | 97.2 |
| transformers-are-short-text-classifiers-a | 98.171 |
| representation-learning-of-entities-and | 96.7 |
| bertgcn-transductive-text-classification-by | 98.2 |
| convtexttm-an-explainable-convolutional | 96.4 |
| transformers-are-short-text-classifiers-a | 97.62 |