Sentiment Analysis On Sst 2 Binary
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Accuracy |
|---|---|
| on-the-role-of-text-preprocessing-in-neural | 91.2 |
| spanbert-improving-pre-training-by | 94.8 |
| recursive-deep-models-for-semantic | 82.9 |
| pay-attention-to-mlps | 94.8 |
| a-c-lstm-neural-network-for-text | 87.8 |
| adversarial-self-attention-for-language | 94.1 |
| emo2vec-learning-generalized-emotion | 81.2 |
| adversarial-self-attention-for-language | 96.3 |
| distilling-task-specific-knowledge-from-bert | 90.7 |
| smart-robust-and-efficient-fine-tuning-for | 93 |
| universal-sentence-encoder | 87.21 |
| message-passing-attention-networks-for | 87.75 |
| convolutional-neural-networks-for-sentence | 88.1 |
| exploring-the-limits-of-transfer-learning | 97.5 |
| how-to-train-bert-with-an-academic-budget | 93.0 |
| informer-transformer-likes-informed-attention | 94.04 |
| exploring-the-limits-of-transfer-learning | 97.4 |
| exploring-the-limits-of-transfer-learning | 96.3 |
| improved-semantic-representations-from-tree | 86.3 |
| text-classification-improved-by-integrating | 89.5 |
| electra-pre-training-text-encoders-as-1 | 96.9 |
| baseline-needs-more-love-on-simple-word | 84.3 |
| 190600095 | 86.95 |
| training-complex-models-with-multi-task-weak | 96.2 |
| a-helping-hand-transfer-learning-for-deep | 86.99 |
| a-la-carte-embedding-cheap-but-effective | 91.7 |
| exploring-joint-neural-model-for-sentence | 54.72 |
| q8bert-quantized-8bit-bert | 94.7 |
| deberta-decoding-enhanced-bert-with | 96.5 |
| entailment-as-few-shot-learner | 96.9 |
| squeezebert-what-can-computer-vision-teach | 91.4 |
| convolutional-neural-networks-with-recurrent | 90.0 |
| investigating-capsule-networks-with-dynamic | 86.8 |
| dual-contrastive-learning-text-classification | 94.91 |
| charformer-fast-character-transformers-via | 91.6 |
| neural-semantic-encoders | 89.7 |
| exploring-the-limits-of-transfer-learning | 91.8 |
| improved-semantic-representations-from-tree | 88.0 |
| multi-task-deep-neural-networks-for-natural | 95.6 |
| smart-robust-and-efficient-fine-tuning-for | 93.6 |
| big-bird-transformers-for-longer-sequences | 94.6 |
| q-bert-hessian-based-ultra-low-precision | 94.8 |
| clear-contrastive-learning-for-sentence | 94.5 |
| fnet-mixing-tokens-with-fourier-transforms | 94 |
| cell-aware-stacked-lstms-for-modeling | 91.3 |
| structbert-incorporating-language-structures | 97.1 |
| gpu-kernels-for-block-sparse-weights | 93.2 |
| learned-in-translation-contextualized-word | 90.3 |
| xlnet-generalized-autoregressive-pretraining | 96.8 |
| emo2vec-learning-generalized-emotion | 82.3 |
| learning-to-encode-position-for-transformer | 96.7 |
| lm-cppf-paraphrasing-guided-data-augmentation | 93.2 |
| learning-to-generate-reviews-and-discovering | 91.8 |
| information-aggregation-via-dynamic-routing | 87.2 |
| 190910351 | 93.1 |
| task-oriented-word-embedding-for-text | 78.8 |
| recursive-deep-models-for-semantic | 85.4 |
| distilbert-a-distilled-version-of-bert | 91.3 |
| smart-robust-and-efficient-fine-tuning-for | - |
| fine-grained-sentiment-classification-using | 91.2 |
| information-aggregation-via-dynamic-routing | 87.6 |
| an-algorithm-for-routing-vectors-in-sequences | 96.0 |
| practical-text-classification-with-large-pre | 90.9 |
| exploring-the-limits-of-transfer-learning | 95.2 |
| bert-pre-training-of-deep-bidirectional | 94.9 |
| cloze-driven-pretraining-of-self-attention | 94.6 |
| harnessing-deep-neural-networks-with-logic | 89.3 |
| smart-robust-and-efficient-fine-tuning-for | - |
| 190910351 | 92.6 |
| albert-a-lite-bert-for-self-supervised | 97.1 |
| muppet-massive-multi-task-representations | 96.7 |
| ernie-enhanced-language-representation-with | 93.5 |
| xlnet-generalized-autoregressive-pretraining | 97 |
| improving-multi-task-deep-neural-networks-via | 96.5 |
| an-algorithm-for-routing-capsules-in-all | 95.6 |
| roberta-a-robustly-optimized-bert-pretraining | 96.7 |
| fine-grained-sentiment-classification-using | 93.1 |
| ask-me-anything-dynamic-memory-networks-for | 88.6 |
| muppet-massive-multi-task-representations | 97.4 |
| smart-robust-and-efficient-fine-tuning-for | 97.5 |
| llm-int8-8-bit-matrix-multiplication-for | 96.4 |
| subregweigh-effective-and-efficient | 94.84 |
| a-statistical-framework-for-low-bitwidth | 96.2 |
| nystromformer-a-nystrom-based-algorithm-for | 91.4 |
| ernie-20-a-continual-pre-training-framework | 95 |
| smart-robust-and-efficient-fine-tuning-for | - |
| pay-attention-when-required | 91.6 |
| improved-sentence-modeling-using-suffix | 91.2 |