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README.md
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---
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license: apache-2.0
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language:
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- knc
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- kau
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datasets:
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- allenai/nllb
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library_name: transformers
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This model is the <b>Central Kanuri</b> (Arabic script) model trained on 100MB of data, after accounting for an estimated byte premium of 2.50; content-matched text in Central Kanuri takes on average 2.50x 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: knc_arab is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. It is not contained in any macrolanguage codes contained in Goldfish (for script arab).
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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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- kau
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- knc
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datasets:
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- allenai/nllb
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library_name: transformers
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This model is the <b>Central Kanuri</b> (Arabic script) model trained on 100MB of data, after accounting for an estimated byte premium of 2.50; content-matched text in Central Kanuri takes on average 2.50x 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: knc_latn.
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Note: knc_arab is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. It is not contained in any macrolanguage codes contained in Goldfish (for script arab).
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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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