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Language Modelling On Hutter Prize

Métriques

Bit per Character (BPC)

Number of params

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Bit per Character (BPC)	Number of params	Paper Title	Repository
Large RHN	1.27	46M	Recurrent Highway Networks
Large FS-LSTM-4	1.245	47M	Fast-Slow Recurrent Neural Networks
Transformer-XL + RMS dynamic eval	0.94	277M	Dynamic Evaluation of Transformer Language Models
18-layer Transformer-XL	1.03	88M	Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Large mLSTM +emb +WN +VD	1.24	46M	Multiplicative LSTM for sequence modelling	-
Mogrifier LSTM	1.122	96M	Mogrifier LSTM
12-layer Transformer-XL	1.06	41M	Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
64-layer Character Transformer Model	1.06	235M	Character-Level Language Modeling with Deeper Self-Attention
3-layer AWD-LSTM	1.232	47M	An Analysis of Neural Language Modeling at Multiple Scales
Longformer Small	1.00	41M	Longformer: The Long-Document Transformer
12-layer Character Transformer Model	1.11	44M	Character-Level Language Modeling with Deeper Self-Attention
FS-LSTM-4	1.277	27M	Fast-Slow Recurrent Neural Networks
mLSTM + dynamic eval	1.08	46M	Dynamic Evaluation of Neural Sequence Models
Longformer Large	0.99	102M	Longformer: The Long-Document Transformer
RHN - depth 5 [zilly2016recurrent]	1.31	-	Recurrent Highway Networks
24-layer Transformer-XL	0.99	277M	Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Compressive Transformer	0.97	-	Compressive Transformers for Long-Range Sequence Modelling
Mogrifier LSTM + dynamic eval	0.988	96M	Mogrifier LSTM

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