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@@ -164,15 +164,17 @@ Values represent **relative performance drop**, computed as `(Acc_clean − Acc_
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  | **Avg** | 0.31 | 0.44 | 0.11 | 0.08 | 0.24 | **0.04** | 0.15 | 0.21 | 0.22 | 0.28 | **0.53** | 0.24 |
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  ## Intended Use
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  This model is intended for:
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- - research on tokenization behavior,
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- - robustness and perturbation analysis,
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  - controlled ablation studies,
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- - benchmarking subword tokenizers.
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- It is **not instruction-tuned** and **not aligned** for deployment or interactive use.
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  ---
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@@ -180,8 +182,8 @@ It is **not instruction-tuned** and **not aligned** for deployment or interactiv
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  - Trained on a limited set of five languages.
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  - Not optimized for instruction following or dialogue.
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- - Performance reflects tokenizer behavior rather than downstream task tuning.
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- - Intended strictly for research use.
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  ---
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  | **Avg** | 0.31 | 0.44 | 0.11 | 0.08 | 0.24 | **0.04** | 0.15 | 0.21 | 0.22 | 0.28 | **0.53** | 0.24 |
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+ ---
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+
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  ## Intended Use
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  This model is intended for:
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+ - research on tokenization and robustness,
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+ - multilingual NLP analysis,
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  - controlled ablation studies,
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+ - benchmarking tokenizer behavior under noise.
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+ It is **not** instruction-tuned, aligned, or optimized for deployment.
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  ---
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  - Trained on a limited set of five languages.
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  - Not optimized for instruction following or dialogue.
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+ - Fixed token budget constrains exposure to raw text depending on tokenization efficiency.
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+ - Intended strictly for research purposes.
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  ---
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