Text Classification On Yelp 5
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| BigBird | 72.16% | Big Bird: Transformers for Longer Sequences | - |
| BERT-ITPT-FiT | 70.58% | How to Fine-Tune BERT for Text Classification? | - |
| XLNet | 72.95% | XLNet: Generalized Autoregressive Pretraining for Language Understanding | - |
| HAHNN (CNN) | 73.28% | Hierarchical Attentional Hybrid Neural Networks for Document Classification | - |
| ULMFiT (Small data) | 67.6% | Sampling Bias in Deep Active Classification: An Empirical Study | - |
| LSTM-reg (single moedl) | 68.7% | Rethinking Complex Neural Network Architectures for Document Classification | |
| BERT Finetune + UDA | 67.92% | Unsupervised Data Augmentation for Consistency Training | - |
0 of 7 row(s) selected.