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@@ -98,10 +98,10 @@ This approach respects linguistic diversity while optimizing model efficiency fo
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  ### Rich linguistic mixes
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  A unique feature of this corpus is the inclusion of mixed texts combining various closely related Cyrillic languages, including:
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- - East Slavic mixes (ru_uk_mix, ru_be_mix, etc.)
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- - South Slavic mixes (bg_mk_mix, sr_bg_mix)
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- - Finno-Ugric mixes (koi_kv_mix, udm_koi_mix)
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- - Caucasian mixes (ab_kbd_mix, ab_ce_mix)
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  These combinations can be used to test the robustness of classifiers in the face of multilingual content, a situation frequently encountered in the media of multicultural regions.
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  ### Data augmentation methods
@@ -120,13 +120,13 @@ Three augmentation approaches were used to enrich the corpus, particularly benef
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  The corpus covers 26 Cyrillic languages, grouped by language family:
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- - **East Slavic** languages: Russian (ru), Ukrainian (uk), Belarusian (be), Rusyn (rue);
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- - **South Slavic** languages: Bulgarian (bg), Macedonian (mk), Serbian (sr);
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- - **Turkic** languages: Kazakh (kk), Kyrgyz (ky), Tatar (tt), Bashkir (ba), Chuvash (cv), Tuvin (tyv), Yakut (sah)
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- - **Iranian** languages: Tajik (tg), Ossetian (iron dialect) (os);
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- - **Finno-Ugric** languages: Komi (kv), Komi-Permyak (koi), Udmurt (udm), Mari (mhr), Erzya (myv)
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- - **Mongolian** languages: Mongolian (mn), Buryat (bxr);
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- - **Caucasian** languages: Abkhaz (ab), Kabardian (kbd), Chechen (ce)
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  Special attention has been paid to minority languages such as Abkhaz, Kabardian, Komi, and Meadow Mari, which are rarely included in standard training corpora. The final model achieves excellent performance across all language families, with 20 languages achieving perfect classification accuracy.
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@@ -178,7 +178,7 @@ The fine-tuned model achieved exceptional results:
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  The model demonstrates sophisticated linguistic understanding:
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  - 45.5% of errors are linguistically justified (all occurred with artificial mixtures containing the predicted language)
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- - most common confusion: Buryat (bxr) → Yakut (sah) and Udmurt (udm) → Kabardinian-Chechen mix (kbd_ce_mix), with 2 errors each
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  - only 9.1% of errors occur within the same linguistic family, showing very good discrimination between related languages
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  - strong metacognitive capabilities: errors occur primarily in moderate confidence ranges (50-80%), with near-perfect calibration (ECE = 0.0029) for high-confidence predictions
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@@ -225,13 +225,13 @@ Due to limitations of the Hugging Face interface, some information could not be
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  **License**: Although this corpus is registered under the MIT License in the metadata, the intention is that it be used and shared under the terms of the CC BY-SA 4.0 (Creative Commons Attribution-ShareAlike 4.0) license.
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  **Languages**: The metadata does not represent all the languages included in this corpus. In addition to the languages listed, this corpus also contains:
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- - Rusyn (rue)
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- - Komi (kv)
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- - Komi-Permyak (koi)
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- - Mari (mhr)
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- - Erzya (myv)
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- - Kabardinian (kbd)
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  - Abkhazian (ab)
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- (Full list in the "Included Languages" section)
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- **Language mixtures**: This corpus also contains examples of texts mixing several Cyrillic languages (identified by codes such as ru_uk_mix, ab_ce_mix, etc.), a specific characteristic that is not representable in standard metadata.
 
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  ### Rich linguistic mixes
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  A unique feature of this corpus is the inclusion of mixed texts combining various closely related Cyrillic languages, including:
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+ - East Slavic mixes (`ru_uk_mix`, `ru_be_mix`, etc.)
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+ - South Slavic mixes (`bg_mk_mix`, `sr_bg_mix`)
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+ - Finno-Ugric mixes (`koi_kv_mix`, `udm_koi_mix`)
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+ - Caucasian mixes (`ab_kbd_mix`, `ab_ce_mix`)
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  These combinations can be used to test the robustness of classifiers in the face of multilingual content, a situation frequently encountered in the media of multicultural regions.
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  ### Data augmentation methods
 
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  The corpus covers 26 Cyrillic languages, grouped by language family:
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+ - **East Slavic** languages: Russian (`ru`), Ukrainian (`uk`), Belarusian (`be`), Rusyn (`rue`);
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+ - **South Slavic** languages: Bulgarian (`bg`), Macedonian (`mk`), Serbian (`sr`);
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+ - **Turkic** languages: Kazakh (`kk`), Kyrgyz (`ky`), Tatar (`tt`), Bashkir (`ba`), Chuvash (`cv`), Tuvin (`tyv`), Yakut (`sah`);
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+ - **Iranian** languages: Tajik (`tg`), Ossetian (iron dialect) (`os`);
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+ - **Finno-Ugric** languages: Komi (`kv`), Komi-Permyak (`koi`), Udmurt (`udm`), Mari (`mhr`), Erzya (`myv`);
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+ - **Mongolian** languages: Mongolian (`mn`), Buryat (`bxr`);
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+ - **Caucasian** languages: Abkhaz (`ab`), Kabardian (`kbd`), Chechen (`ce`).
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  Special attention has been paid to minority languages such as Abkhaz, Kabardian, Komi, and Meadow Mari, which are rarely included in standard training corpora. The final model achieves excellent performance across all language families, with 20 languages achieving perfect classification accuracy.
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  The model demonstrates sophisticated linguistic understanding:
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  - 45.5% of errors are linguistically justified (all occurred with artificial mixtures containing the predicted language)
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+ - most common confusion: Buryat (`bxr`) → Yakut (`sah`) and Udmurt (`udm`) → Kabardinian-Chechen mix (`kbd_ce_mix`), with 2 errors each
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  - only 9.1% of errors occur within the same linguistic family, showing very good discrimination between related languages
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  - strong metacognitive capabilities: errors occur primarily in moderate confidence ranges (50-80%), with near-perfect calibration (ECE = 0.0029) for high-confidence predictions
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  **License**: Although this corpus is registered under the MIT License in the metadata, the intention is that it be used and shared under the terms of the CC BY-SA 4.0 (Creative Commons Attribution-ShareAlike 4.0) license.
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  **Languages**: The metadata does not represent all the languages included in this corpus. In addition to the languages listed, this corpus also contains:
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+ - Rusyn (`rue`)
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+ - Komi (`kv`)
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+ - Komi-Permyak (`koi`)
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+ - Mari (`mhr`)
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+ - Erzya (`myv`)
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+ - Kabardinian (`kbd`)
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  - Abkhazian (ab)
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+ (Full list in the #Included Languages section)
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+ **Language mixtures**: This corpus also contains examples of texts mixing several Cyrillic languages (identified by codes such as `ru_uk_mix`, `ab_ce_mix`, etc.), a specific characteristic that is not representable in standard metadata.