Sentiment Analysis On Imdb
المقاييس
Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | Accuracy |
|---|---|
| classifying-textual-data-with-pre-trained | 85 |
| the-document-vectors-using-cosine-similarity-1 | 95.79 |
| task-oriented-word-embedding-for-text | 90.8 |
| nystromformer-a-nystrom-based-algorithm-for | 93.2 |
| finetuned-language-models-are-zero-shot | 94.3 |
| efficient-vector-representation-for-documents | 88.3 |
| the-document-vectors-using-cosine-similarity-1 | 93.68 |
| unsupervised-data-augmentation-1 | 95.8 |
| llambert-large-scale-low-cost-data-annotation | 95.39 |
| xlnet-generalized-autoregressive-pretraining | 96.21 |
| graph-star-net-for-generalized-multi-task-1 | 96.0 |
| learned-in-translation-contextualized-word | 91.8 |
| cache-me-if-you-can-an-online-cost-aware | 93.06 |
| coupled-oscillatory-recurrent-neural-network | 87.4% |
| finetuned-language-models-are-zero-shot | 95 |
| sentiment-classification-using-document | 93.13 |
| on-the-role-of-text-preprocessing-in-neural | 88.9 |
| how-to-fine-tune-bert-for-text-classification | 95.63 |
| the-document-vectors-using-cosine-similarity-1 | 95.92 |
| the-document-vectors-using-cosine-similarity-1 | 95.94 |
| information-aggregation-via-dynamic-routing | 44.5 |
| entailment-as-few-shot-learner | 96.1 |
| llambert-large-scale-low-cost-data-annotation | 96.68 |
| classifying-textual-data-with-pre-trained | 87 |
| contextual-explanation-networks | 94.52 |
| long-short-term-memory-with-dynamic-skip | 90.1 |
| effective-use-of-word-order-for-text-1 | 92.33 |
| parallelizing-legendre-memory-unit-training | 93.20 |
| classifying-textual-data-with-pre-trained | 86 |
| bp-transformer-modelling-long-range-context | 92.12 |
| language-models-are-unsupervised-multitask | 92.36 |
| unsupervised-data-augmentation-1 | 95.49 |
| breaking-free-transformer-models-task | 94.88 |
| fine-tuning-pre-trained-language-model-with | 90.54 |
| gpu-kernels-for-block-sparse-weights | 94.99 |
| universal-language-model-fine-tuning-for-text | 95.4 |
| distilbert-a-distilled-version-of-bert | 92.82 |
| adversarial-training-methods-for-semi | 94.1 |
| sentence-state-lstm-for-text-representation | 87.15 |
| an-algorithm-for-routing-vectors-in-sequences | 96.2 |
| unicornn-a-recurrent-model-for-learning-very | 88.4 |
| llambert-large-scale-low-cost-data-annotation | 96.54 |
| information-aggregation-via-dynamic-routing | 45.1 |
| learning-in-wilson-cowan-model-for | 87.46 |
| how-to-fine-tune-bert-for-text-classification | 95.79 |
| revisiting-lstm-networks-for-semi-supervised-1 | 95.68 |
| supervised-and-semi-supervised-text | 94.1 |
| closed-form-continuous-depth-models | 88.4 |