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@@ -71,7 +71,7 @@ All splits are created using stratified sampling to preserve the distribution of
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  - **Fine-Grained Explainability:** Explicit rationales for every positive/negative annotation.
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  - **Ambiguity Capture:** Optional secondary labels reflect subjective or mixed-sentiment content.
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  - **Multi-Annotator Design:** All three annotator opinions are provided, supporting robust analysis of inter-annotator agreement and label ambiguity.
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- - **Flexible Usage:** Supports explainable sentiment classification, rationale extraction, ambiguity analysis, and evaluation of subjective language understanding in Telugu.
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  - **Standard Splits:** Predefined train/val/test splits for reproducible experiments and fair comparisons.
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  - **Fine-Grained Explainability:** Explicit rationales for every positive/negative annotation.
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  - **Ambiguity Capture:** Optional secondary labels reflect subjective or mixed-sentiment content.
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  - **Multi-Annotator Design:** All three annotator opinions are provided, supporting robust analysis of inter-annotator agreement and label ambiguity.
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+ - **Flexible Usage:** Supports explainable sentiment classification, rationale extraction.
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  - **Standard Splits:** Predefined train/val/test splits for reproducible experiments and fair comparisons.
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