Sentiment Analysis On Sst 2 Binary

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Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Accuracy	Paper Title	Repository
CNN	91.2	On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis
SpanBERT	94.8	SpanBERT: Improving Pre-training by Representing and Predicting Spans
MV-RNN	82.9	-	-
gMLP-large	94.8	Pay Attention to MLPs
C-LSTM	87.8	A C-LSTM Neural Network for Text Classification
ASA + BERT-base	94.1	Adversarial Self-Attention for Language Understanding
Emo2Vec	81.2	Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
ASA + RoBERTa	96.3	Adversarial Self-Attention for Language Understanding
Single layer bilstm distilled from BERT	90.7	Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
SMART+BERT-BASE	93	SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
USE_T+CNN (lrn w.e.)	87.21	Universal Sentence Encoder
MPAD-path	87.75	Message Passing Attention Networks for Document Understanding	-
CNN-multichannel [kim2013]	88.1	Convolutional Neural Networks for Sentence Classification
T5-11B	97.5	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
24hBERT	93.0	How to Train BERT with an Academic Budget
RealFormer	94.04	RealFormer: Transformer Likes Residual Attention
T5-3B	97.4	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
T5-Large 770M	96.3	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
2-layer LSTM [tai2015improved]	86.3	Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
BLSTM-2DCNN	89.5	Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling

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