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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