agentlans/GIST-small-c4-en-industry_corpus2
Text Classification ā¢ 33.4M ā¢ Updated
Alan Tseng
agentlans
AI & ML interests
Small data, boring AI
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Just published: how we built production Sango (Central African Republic) translation without fine-tuning, parallel corpus, or training compute.
The method ā vocabulary-augmented prompting with a 581-entry native-speaker-verified lexicon ā generalizes to any of the ~2,000 African languages at the same data-poverty level. Recipe, dataset, and code template all included.
š Blog: https://huggingface.co/blog/MEYNG/sangoai
š¦ Dataset: MEYNG/sango-vocabulary
Would especially value feedback from anyone working on other low-resource African languages ā Ewondo, Lingala, Wolof next on our roadmap.
The method ā vocabulary-augmented prompting with a 581-entry native-speaker-verified lexicon ā generalizes to any of the ~2,000 African languages at the same data-poverty level. Recipe, dataset, and code template all included.
š Blog: https://huggingface.co/blog/MEYNG/sangoai
š¦ Dataset: MEYNG/sango-vocabulary
Would especially value feedback from anyone working on other low-resource African languages ā Ewondo, Lingala, Wolof next on our roadmap.
- 1 reply
I'm not working on low-resource African languages but this method sounds interesting.
So you put the orthography, grammar, and vocabulary in the prompt and then get the LLM to translate a language that it doesn't know. Clever!
Then once you have enough native speaker-verified Sango-French translations, you can bootstrap it to a full-fledged dataset...
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