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### Summary
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The model corrects spelling errors and typos by bringing all the words in the text to the norm of the Russian language.
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Corrector
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An extensive dataset with “artificial” errors was taken as a training corpus: the corpus was assembled on the basis of the Russian-language Wikipedia and transcripts of Russian-language videos, then typos and spelling errors were automatically introduced into it using the library [SAGE](https://github.com/ai-forever/sage).
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### Public references
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- [SAGE library announcement](https://youtu.be/yFfkV0Qjuu0), DataFest 2023
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- [Paper about synthetic error generation methods](https://www.dialog-21.ru/media/5914/martynovnplusetal056.pdf), Dialogue 2023
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### Examples
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### Summary
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The model corrects spelling and punctuation errors and typos by bringing all the words in the text to the norm of the Russian language.
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Corrector had been trained based on the model [FRED-T5-large](https://huggingface.co/ai-forever/FRED-T5-large).
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An extensive dataset with “artificial” errors was taken as a training corpus: the corpus was assembled on the basis of the Russian-language Wikipedia and transcripts of Russian-language videos, then typos and spelling errors were automatically introduced into it using the library [SAGE](https://github.com/ai-forever/sage).
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### Public references
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- [SAGE library announcement](https://youtu.be/yFfkV0Qjuu0), DataFest 2023
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- [Paper about synthetic error generation methods](https://www.dialog-21.ru/media/5914/martynovnplusetal056.pdf), Dialogue 2023
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- [SAGE EACL 2024 paper](https://aclanthology.org/2024.findings-eacl.10/)
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### Examples
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