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README.md
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@@ -19,6 +19,8 @@ An intriguing aspect of adapting T-pro-it-1.0 is that this model was obtained th
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<img src="https://cdn-uploads.huggingface.co/production/uploads/652cedbdf120598322ae358a/sKwHvA9ztd7rHx37Ca2ey.png" style="display: block; margin: 0 auto; max-width: 50%; height: auto;">
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## Papers
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Tikhomirov M., Chernyshov D. Facilitating Large Language Model Russian Adaptation with Learned Embedding Propagation //Journal of Language and Education. β 2024. β Π’. 10. β β. 4. β Π‘. 130-145. (Preprint: https://arxiv.org/abs/2412.21140)
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<img src="https://cdn-uploads.huggingface.co/production/uploads/652cedbdf120598322ae358a/sKwHvA9ztd7rHx37Ca2ey.png" style="display: block; margin: 0 auto; max-width: 50%; height: auto;">
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For adaptation, we use sampling from a combination of the open datasets HuggingFaceFW/fineweb-2 and IlyaGusev/rulmIlyaGusev/rulm. The study of the impact of data volume and quality on the current process is ongoing.
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## Papers
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Tikhomirov M., Chernyshov D. Facilitating Large Language Model Russian Adaptation with Learned Embedding Propagation //Journal of Language and Education. β 2024. β Π’. 10. β β. 4. β Π‘. 130-145. (Preprint: https://arxiv.org/abs/2412.21140)
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