goldfish-models
/

zho_hant_100mb

@@ -21,6 +21,8 @@ Goldfish is a suite of monolingual language models trained for 350 languages.
 This model is the <b>Chinese</b> (Han Traditional script) model trained on 100MB of data, after accounting for an estimated byte premium of 0.99; content-matched text in Chinese takes on average 0.99x as many UTF-8 bytes to encode as English.
 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).
 Note: zho_hant is a [macrolanguage](https://iso639-3.sil.org/code_tables/639/data) code. Individual language codes yue_hant (Yue Chinese) and lzh_hant (Literary Chinese) are included in Goldfish, although with less data.
 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).

 This model is the <b>Chinese</b> (Han Traditional script) model trained on 100MB of data, after accounting for an estimated byte premium of 0.99; content-matched text in Chinese takes on average 0.99x as many UTF-8 bytes to encode as English.
 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).
+Note: This language is available in Goldfish with other scripts (writing systems). See: zho_hans.
 Note: zho_hant is a [macrolanguage](https://iso639-3.sil.org/code_tables/639/data) code. Individual language codes yue_hant (Yue Chinese) and lzh_hant (Literary Chinese) are included in Goldfish, although with less data.
 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).