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  ### Summary
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  The model corrects spelling errors and typos by bringing all words in the text to the standard English language.
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  The proofreader was trained based on the [T5-large](https://huggingface.co/t5-large) model.
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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 English-language Wikipedia and News blogs, then typos and spelling errors were automatically introduced into it using the functionality of the [SAGE] library (https://github.com /orgs/ai-forever/sage).
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- ### Articles and speeches
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- - [Speech about the SAGE library](https://youtu.be/yFfkV0Qjuu0), DataFest 2023
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- - [Article about synthetic error generation methods](https://www.dialog-21.ru/media/5914/martynovnplusetal056.pdf), Dialogue 2023
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- - [Article about SAGE and our best solution](https://arxiv.org/abs/2308.09435), Review EACL 2024
 
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  ### Examples
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  | Input | Output |
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  ```
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  ## Resources
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- - [SAGE library code with augmentation methods, access to datasets and open models](https://github.com/orgs/ai-forever/sage), GitHub
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  - [ruM2M100-1.2B](https://huggingface.co/ai-forever/RuM2M100-1.2B), HuggingFace
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  - [ruM2M100-418M](https://huggingface.co/ai-forever/RuM2M100-420M), HuggingFace
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  - [FredT5-large-spell](https://huggingface.co/ai-forever/FRED-T5-large-spell), HuggingFace
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  ## License
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  The [T5-large](https://huggingface.co/t5-large) model, on which our solution is based, and its source code are supplied under the APACHE-2.0 license.
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- Our solution is supplied under the MIT license.
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  ## Specifications
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  - File size: 3 Gb;
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  - Developer: SberDevices, AGI NLP
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  ## Contacts
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- For questions related to the operation and application of the model, please contact the product manager: Pavel Lebedev PIgLebedev@sberbank.ru.
 
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  ### Summary
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  The model corrects spelling errors and typos by bringing all words in the text to the standard English language.
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  The proofreader was trained based on the [T5-large](https://huggingface.co/t5-large) model.
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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 English-language Wikipedia and News blogs, then typos and spelling errors were automatically introduced into it using the functionality of the [SAGE library](https://github.com/orgs/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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+ - [Paper about SAGE and our best solution](https://arxiv.org/abs/2308.09435), Review EACL 2024
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+ - [Path_to_model](https://huggingface.co/ai-forever/T5-large-spell)
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  ### Examples
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  | Input | Output |
 
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  ```
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  ## Resources
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+ - [SAGE library](https://github.com/orgs/ai-forever/sage), GitHub
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  - [ruM2M100-1.2B](https://huggingface.co/ai-forever/RuM2M100-1.2B), HuggingFace
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  - [ruM2M100-418M](https://huggingface.co/ai-forever/RuM2M100-420M), HuggingFace
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  - [FredT5-large-spell](https://huggingface.co/ai-forever/FRED-T5-large-spell), HuggingFace
 
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  ## License
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  The [T5-large](https://huggingface.co/t5-large) model, on which our solution is based, and its source code are supplied under the APACHE-2.0 license.
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+ Our solution is supplied under MIT license.
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  ## Specifications
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  - File size: 3 Gb;
 
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  - Developer: SberDevices, AGI NLP
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  ## Contacts
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+ nikita.martynov.98@list.ru