Text Classification On Ag News
Metrics
Error
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Error |
|---|---|
| learning-to-remember-more-with-less | 6.10 |
| xlnet-generalized-autoregressive-pretraining | 4.45 |
| very-deep-convolutional-networks-for-text | 8.67 |
| explicit-interaction-model-towards-text | 7 |
| squeezed-very-deep-convolutional-neural | 9.45 |
| revisiting-lstm-networks-for-semi-supervised-1 | 4.95 |
| baseline-needs-more-love-on-simple-word | 7.34 |
| abstractive-text-classification-using | 9.64 |
| disconnected-recurrent-neural-networks-for | 5.53 |
| joint-embedding-of-words-and-labels-for-text | 7.55 |
| bag-of-tricks-for-efficient-text | 7.5 |
| deep-pyramid-convolutional-neural-networks | 6.87 |
| sampling-bias-in-deep-active-classification | 6.3 |
| how-to-fine-tune-bert-for-text-classification | 4.8 |
| character-level-convolutional-networks-for | 9.51 |
| investigating-capsule-networks-with-dynamic | 7.4 |
| compositional-coding-capsule-network-with-k | 7.61 |
| on-tree-based-neural-sentence-modeling | 7.9 |
| task-oriented-word-embedding-for-text | 14.0 |
| universal-language-model-fine-tuning-for-text | 5.01 |
| supervised-and-semi-supervised-text | 6.57 |