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README.md
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@@ -87,7 +87,7 @@ The most significant observation is the model's performance on extremely low-res
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- **Magahi (MAG):** The Fine-Tuned model achieved a perplexity of 34.35, surpassing both the Base MuRIL (279.29) and the XLM-R baseline (67.77).
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- **Bhojpuri (BH):** The model demonstrated superior robustness, achieving a score of 116.41, edging out XLM-R (122.09) and vastly improving upon the Base MuRIL (444.35).
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This validates the efficacy of the
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### 5.2 Performance on Major Languages
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- **Magahi (MAG):** The Fine-Tuned model achieved a perplexity of 34.35, surpassing both the Base MuRIL (279.29) and the XLM-R baseline (67.77).
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- **Bhojpuri (BH):** The model demonstrated superior robustness, achieving a score of 116.41, edging out XLM-R (122.09) and vastly improving upon the Base MuRIL (444.35).
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This validates the efficacy of the RESPIN dataset for adapting encoders to under-represented Indic dialects.
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### 5.2 Performance on Major Languages
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