Language Modelling On Penn Treebank Character

评估指标

Bit per Character (BPC)

Number of params

评测结果

各个模型在此基准测试上的表现结果

模型名称	Bit per Character (BPC)	Number of params	Paper Title	Repository
TCN	1.31	5.9M	Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling
Past Decode Reg. + AWD-LSTM-MoS + dyn. eval.	1.169	13.8M	Improved Language Modeling by Decoding the Past	-
2-layer Norm HyperLSTM	1.219	14.4M	HyperNetworks
Feedback Transformer	1.160	10.7M	Addressing Some Limitations of Transformers with Feedback Memory
Mogrifier LSTM + dynamic eval	1.083	24M	Mogrifier LSTM
GAM-RHN-5	1.147	16.0M	Recurrent Highway Networks with Grouped Auxiliary Memory
Mogrifier LSTM	1.120	24M	Mogrifier LSTM
Seq-U-Net	1.3	5.9M	Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling
Trellis Network	1.158	13.4M	Trellis Networks for Sequence Modeling
R-Transformer	1.24	-	R-Transformer: Recurrent Neural Network Enhanced Transformer
6-layer QRNN	1.187	13.8M	An Analysis of Neural Language Modeling at Multiple Scales
IndRNN	1.19	-	Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Dense IndRNN	1.18	-	Deep Independently Recurrent Neural Network (IndRNN)
Temporal Convolutional Network	1.31	-	An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
NAS-RL	1.214	16.3M	Neural Architecture Search with Reinforcement Learning
FS-LSTM-4	1.190	27M	Fast-Slow Recurrent Neural Networks
Bipartite Flow	1.38	-	Discrete Flows: Invertible Generative Models of Discrete Data
STAR	1.30	-	Gating Revisited: Deep Multi-layer RNNs That Can Be Trained
3-layer AWD-LSTM	1.175	13.8M	An Analysis of Neural Language Modeling at Multiple Scales
FS-LSTM-2	1.193	27M	Fast-Slow Recurrent Neural Networks

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