Sentiment Analysis On Imdb

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

		Paper Title
RoBERTa-large with LlamBERT	96.68	LlamBERT: Large-scale low-cost data annotation in NLP
RoBERTa-large	96.54	LlamBERT: Large-scale low-cost data annotation in NLP
XLNet	96.21	XLNet: Generalized Autoregressive Pretraining for Language Understanding
Heinsen Routing + RoBERTa Large	96.2	An Algorithm for Routing Vectors in Sequences
RoBERTa-large 355M + Entailment as Few-shot Learner	96.1	Entailment as Few-Shot Learner
GraphStar	96.0	Graph Star Net for Generalized Multi-Task Learning
DV-ngrams-cosine with NB sub-sampling + RoBERTa.base	95.94	The Document Vectors Using Cosine Similarity Revisited
DV-ngrams-cosine + RoBERTa.base	95.92	The Document Vectors Using Cosine Similarity Revisited
BERT large finetune UDA	95.8	Unsupervised Data Augmentation for Consistency Training
RoBERTa.base	95.79	The Document Vectors Using Cosine Similarity Revisited
BERT_large+ITPT	95.79	How to Fine-Tune BERT for Text Classification?
L MIXED	95.68	Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
BERT_base+ITPT	95.63	How to Fine-Tune BERT for Text Classification?
BERT large	95.49	Unsupervised Data Augmentation for Consistency Training
ULMFiT	95.4	Universal Language Model Fine-tuning for Text Classification
Llama-2-70b-chat (0-shot)	95.39	LlamBERT: Large-scale low-cost data annotation in NLP
FLAN 137B (few-shot, k=2)	95	Finetuned Language Models Are Zero-Shot Learners
Block-sparse LSTM	94.99	GPU Kernels for Block-Sparse Weights
Space-XLNet	94.88	Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
CEN-tpc	94.52	Contextual Explanation Networks

0 of 48 row(s) selected.

Command Palette

Sentiment Analysis On Imdb

평가 지표

평가 결과