Sentiment Analysis On Cr
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | ||
|---|---|---|
| AnglE-LLaMA-7B | 93.54 | AnglE-optimized Text Embeddings |
| LM-CPPF RoBERTa-base | 93.3 | LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning |
| RoBERTa-large 355M + Entailment as Few-shot Learner | 92.5 | Entailment as Few-Shot Learner |
| Block-sparse LSTM | 92.2 | GPU Kernels for Block-Sparse Weights |
| byte mLSTM7 | 90.6 | A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors |
| USE_T+CNN (w2v w.e.) | 87.45 | Universal Sentence Encoder |
| SuBiLSTM-Tied | 86.5 | Improved Sentence Modeling using Suffix Bidirectional LSTM |
| Capsule-B | 85.1 | Investigating Capsule Networks with Dynamic Routing for Text Classification |
| STM+TSED+PT+2L | 82.73 | The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning |
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