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Sentiment Analysis On Imdb

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Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper Title
RoBERTa-large with LlamBERT96.68LlamBERT: Large-scale low-cost data annotation in NLP
RoBERTa-large96.54LlamBERT: Large-scale low-cost data annotation in NLP
XLNet96.21XLNet: Generalized Autoregressive Pretraining for Language Understanding
Heinsen Routing + RoBERTa Large96.2An Algorithm for Routing Vectors in Sequences
RoBERTa-large 355M + Entailment as Few-shot Learner96.1Entailment as Few-Shot Learner
GraphStar96.0Graph Star Net for Generalized Multi-Task Learning
DV-ngrams-cosine with NB sub-sampling + RoBERTa.base95.94The Document Vectors Using Cosine Similarity Revisited
DV-ngrams-cosine + RoBERTa.base95.92The Document Vectors Using Cosine Similarity Revisited
BERT large finetune UDA95.8Unsupervised Data Augmentation for Consistency Training
RoBERTa.base95.79The Document Vectors Using Cosine Similarity Revisited
BERT_large+ITPT95.79How to Fine-Tune BERT for Text Classification?
L MIXED95.68Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
BERT_base+ITPT95.63How to Fine-Tune BERT for Text Classification?
BERT large95.49Unsupervised Data Augmentation for Consistency Training
ULMFiT95.4Universal Language Model Fine-tuning for Text Classification
Llama-2-70b-chat (0-shot)95.39LlamBERT: Large-scale low-cost data annotation in NLP
FLAN 137B (few-shot, k=2)95Finetuned Language Models Are Zero-Shot Learners
Block-sparse LSTM94.99GPU Kernels for Block-Sparse Weights
Space-XLNet94.88Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
CEN-tpc94.52Contextual Explanation Networks
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Sentiment Analysis On Imdb | SOTA | HyperAI