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README.md
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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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# lus_latn_full
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Note: lus_latn 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 latn).
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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://
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Training code and sample usage: https://github.com/tylerachang/goldfish
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@@ -32,6 +32,7 @@ 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
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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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# lus_latn_full
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Note: lus_latn 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 latn).
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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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```
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