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Text Classification On Yelp 5
Metrics
Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| HAHNN (CNN) | 73.28% | Hierarchical Attentional Hybrid Neural Networks for Document Classification | |
| XLNet | 72.95% | XLNet: Generalized Autoregressive Pretraining for Language Understanding | |
| BigBird | 72.16% | Big Bird: Transformers for Longer Sequences | |
| BERT-ITPT-FiT | 70.58% | How to Fine-Tune BERT for Text Classification? | |
| 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 | |
| ULMFiT (Small data) | 67.6% | Sampling Bias in Deep Active Classification: An Empirical Study |
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