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  ---
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  license: apache-2.0
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  language:
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- - chi
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  - cmn
 
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  - zho
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  datasets:
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  - oscar-corpus/OSCAR-2109
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  This model is the <b>Chinese</b> (Han Simplified script) model trained on 5MB of data, after accounting for an estimated byte premium of 0.94; content-matched text in Chinese takes on average 0.94x 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: zho_hans is a [macrolanguage](https://iso639-3.sil.org/code_tables/639/data) code. None of its contained individual languages are included in Goldfish (for script hans).
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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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  ---
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  license: apache-2.0
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  language:
 
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  - cmn
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+ - chi
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  - zho
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  datasets:
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  - oscar-corpus/OSCAR-2109
 
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  This model is the <b>Chinese</b> (Han Simplified script) model trained on 5MB of data, after accounting for an estimated byte premium of 0.94; content-matched text in Chinese takes on average 0.94x 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: zho_hant.
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+
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  Note: zho_hans is a [macrolanguage](https://iso639-3.sil.org/code_tables/639/data) code. None of its contained individual languages are included in Goldfish (for script hans).
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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).