Papers › One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling

One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling

11 Dec 2013arXiv:1312.3005archive 2025-07-28

Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, Tony Robinson

We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned Kneser-Ney 5-gram model achieves perplexity 67.6; a combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models.

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neuspell/neuspell mentioned on GitHubpytorch report
tensorflow/models mentioned on GitHubtf report
tensorflow/models mentioned on GitHubtf report

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Language ModelingLanguage Modelling

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Billion Word BenchmarkOne Billion Word Benchmark

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Language Modelling One Billion Word RNN-1024 + 9 Gram Number of params 20B #24 of 27 Archive leaderboard report
Language Modelling One Billion Word RNN-1024 + 9 Gram PPL 51.3 #24 of 27 Archive leaderboard report

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