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@@ -11,7 +11,7 @@ library_name: transformers
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  pipeline_tag: text-generation
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  tags:
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  - goldfish
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-
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  ---
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  # uzb_latn_1000mb
@@ -24,7 +24,7 @@ Note: This language is available in Goldfish with other scripts (writing systems
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  Note: uzb_latn is a [macrolanguage](https://iso639-3.sil.org/code_tables/639/data) code. Individual language code uzn_latn (Northern Uzbek) is included in Goldfish, although with less data.
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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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  Training code and sample usage: https://github.com/tylerachang/goldfish
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@@ -34,6 +34,7 @@ Sample usage also in this Google Colab: [link](https://colab.research.google.com
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  To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/blob/main/model_details.json.
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  All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
 
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  Details for this model specifically:
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  * Architecture: gpt2
@@ -62,5 +63,6 @@ If you use this model, please cite:
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  author={Chang, Tyler A. and Arnett, Catherine and Tu, Zhuowen and Bergen, Benjamin K.},
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  journal={Preprint},
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  year={2024},
 
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  }
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  ```
 
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  pipeline_tag: text-generation
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  tags:
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  - goldfish
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+ - arxiv:2408.10441
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  ---
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  # uzb_latn_1000mb
 
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  Note: uzb_latn is a [macrolanguage](https://iso639-3.sil.org/code_tables/639/data) code. Individual language code uzn_latn (Northern Uzbek) is included in Goldfish, although with less data.
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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://www.arxiv.org/abs/2408.10441).
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  Training code and sample usage: https://github.com/tylerachang/goldfish
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  To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/blob/main/model_details.json.
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  All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
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+ For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
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  Details for this model specifically:
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  * Architecture: gpt2
 
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  author={Chang, Tyler A. and Arnett, Catherine and Tu, Zhuowen and Bergen, Benjamin K.},
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  journal={Preprint},
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  year={2024},
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+ url={https://www.arxiv.org/abs/2408.10441},
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  }
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  ```