Sentiment Analysis On Yelp Binary

Métriques

Error

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Error	Paper Title	Repository
DRNN	2.73	Disconnected Recurrent Neural Networks for Text Categorization	-
CNN	2.9	Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings	-
BERT large	1.89	Unsupervised Data Augmentation for Consistency Training
BERT_large+ITPT	1.81	How to Fine-Tune BERT for Text Classification?
ULMFiT	2.16	Universal Language Model Fine-tuning for Text Classification
Char-level CNN	4.88	Character-level Convolutional Networks for Text Classification
LEAM	4.69	Joint Embedding of Words and Labels for Text Classification
fastText, h=10, bigram	4.3	Bag of Tricks for Efficient Text Classification
DNC+CUW	3.60	Learning to Remember More with Less Memorization
DPCNN	2.64	Deep Pyramid Convolutional Neural Networks for Text Categorization
SVDCNN	4.74	Squeezed Very Deep Convolutional Neural Networks for Text Classification
CCCapsNet	3.52	Compositional Coding Capsule Network with K-Means Routing for Text Classification
Block-sparse LSTM	3.27	GPU Kernels for Block-Sparse Weights	-
BERT_base+ITPT	1.92	How to Fine-Tune BERT for Text Classification?
SRNN	3.96	Sliced Recurrent Neural Networks
BERT large finetune UDA	2.05	Unsupervised Data Augmentation for Consistency Training
LHTR	1.86	Heavy-tailed Representations, Text Polarity Classification & Data Augmentation	-
SWEM-hier	4.19	Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
M-ACNN	3.89	Learning Context-Sensitive Convolutional Filters for Text Processing	-
XLNet	1.37	XLNet: Generalized Autoregressive Pretraining for Language Understanding

0 of 20 row(s) selected.