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Training and Evaluation of a Multilingual Tokenizer for GPT-SW3

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arxiv 2304.14780 v1 pith:SWLAK2OW submitted 2023-04-28 cs.CL cs.AI

Felix Stollenwerk This is my paper · ORCID
classification cs.CLcs.AI
keywords tokenizergpt-sw3multilingualadditionalgorithmanalyzedatadetailed
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper provides a detailed discussion of the multilingual tokenizer used for GPT-SW3. It was trained on the Nordic Pile using the SentencePiece library and the BPE algorithm. We outline the tokenizer's most important features and share details on its learned vocabulary. In addition, we systematically analyze the properties and evaluate the performance of the tokenizer with regard to the different languages present in the data.

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  1. Beyond Text Compression: Evaluating Tokenizers Across Scales

    cs.CL 2025-06 conditional novelty 5.0 of 10

    Tokenizer choice matters mostly for multilingual tasks, and 350M-parameter models can predict 2.7B model ranking on translation but not on English benchmarks.

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