Text Classification On 20News
Metriken
Accuracy
F-measure
Precision
Recall
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy | F-measure | Precision | Recall |
|---|---|---|---|---|
| an-explainable-probabilistic-classifier-for | 87.3 | 86.6 | 87.1 | 86.6 |
| representation-learning-of-entities-and | 84.5 | 83.9 | - | - |
| a-comparison-of-svm-against-pre-trained | 93 | 93 | - | - |
| speeding-up-word-movers-distance-and-its | 74.78 | - | - | - |
| rep-the-set-neural-networks-for-learning-set | 76.18 | - | - | - |
| semi-supervised-nmf-models-for-topic-modeling | 81.88 | - | - | - |
| improving-document-classification-with-multi | 86.19 | 86.16 | 86.2 | 86.18 |
| neural-attentive-bag-of-entities-model-for | 86.8 | 86.2 | - | - |
| simplifying-graph-convolutional-networks | 88.5 | - | - | - |
| simplifying-graph-convolutional-networks | 88.5 | - | - | - |
| graph-convolutional-networks-for-text | 86.34 | - | - | - |
| rmdl-random-multimodel-deep-learning-for | 87.91 | - | - | - |
| text-classification-with-word-embedding | 70.28 | - | - | - |
| bertgcn-transductive-text-classification-by | 89.5 | - | - | - |
| simple-spectral-graph-convolution | 88.6 | - | - | - |
| graph-star-net-for-generalized-multi-task-1 | 86.9 | - | - | - |