Language Modelling On Text8

Metrics

Bit per Character (BPC)

Results

Performance results of various models on this benchmark

		Paper Title
td-LSTM (Zhang et al., 2016)	1.63	Architectural Complexity Measures of Recurrent Neural Networks
td-LSTM-large	1.49	Architectural Complexity Measures of Recurrent Neural Networks
BFN	1.41	Bayesian Flow Networks
Unregularised mLSTM	1.40	Multiplicative LSTM for sequence modelling
BN LSTM	1.36	Recurrent Batch Normalization
LayerNorm HM-LSTM	1.29	Hierarchical Multiscale Recurrent Neural Networks
Large mLSTM +emb +WN +VD	1.27	Multiplicative LSTM for sequence modelling
Large RHN	1.27	Recurrent Highway Networks
Bipartite flows (8 flows)	1.23	Discrete Flows: Invertible Generative Models of Discrete Data
mLSTM + dynamic eval	1.19	Dynamic Evaluation of Neural Sequence Models
12-layer Character Transformer Model	1.18	Character-Level Language Modeling with Deeper Self-Attention
PAR Transformer 24B	1.18	Pay Attention when Required
GAM-RHN-10	1.157	Recurrent Highway Networks with Grouped Auxiliary Memory
64-layer Character Transformer Model	1.13	Character-Level Language Modeling with Deeper Self-Attention
12L Transformer + 8K adaptive span	1.11	Adaptive Attention Span in Transformers
BP-Transformer - 12 Layers	1.11	BP-Transformer: Modelling Long-Range Context via Binary Partitioning
All-attention network - 18 layers	1.11	Augmenting Self-attention with Persistent Memory
Transformer-LS (small)	1.09	Long-Short Transformer: Efficient Transformers for Language and Vision
All-attention network - 36 layers	1.08	Augmenting Self-attention with Persistent Memory
Transformer-XL - 24 layers	1.08	Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

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Language Modelling On Text8

Metrics

Results