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@@ -19,6 +19,8 @@ Goldfish is a suite of monolingual language models trained for 350 languages.
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  This model is the <b>Northern Uzbek</b> (Latin script) model trained on 10MB of data, after accounting for an estimated byte premium of 1.65; content-matched text in Northern Uzbek takes on average 1.65x as many UTF-8 bytes to encode as English.
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  The Goldfish models are trained primarily for comparability across languages and for low-resource languages; Goldfish performance for high-resource languages is not designed to be comparable with modern large language models (LLMs).
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  Note: uzn_latn is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. Macrolanguage code uzb_latn (Uzbek) is included in Goldfish. Consider using that model depending on your use case.
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  All training and hyperparameter details are in our paper, [Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024)](https://github.com/tylerachang/goldfish/blob/main/goldfish_paper_20240815.pdf).
 
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  This model is the <b>Northern Uzbek</b> (Latin script) model trained on 10MB of data, after accounting for an estimated byte premium of 1.65; content-matched text in Northern Uzbek takes on average 1.65x as many UTF-8 bytes to encode as English.
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  The Goldfish models are trained primarily for comparability across languages and for low-resource languages; Goldfish performance for high-resource languages is not designed to be comparable with modern large language models (LLMs).
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+ Note: This language is available in Goldfish with other scripts (writing systems). See: uzn_cyrl.
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  Note: uzn_latn is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. Macrolanguage code uzb_latn (Uzbek) is included in Goldfish. Consider using that model depending on your use case.
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  All training and hyperparameter details are in our paper, [Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024)](https://github.com/tylerachang/goldfish/blob/main/goldfish_paper_20240815.pdf).