PyTorch
mistral
Krutrim
language-model
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Update Indic Evals in Readme

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  1. README.md +3 -3
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@@ -98,13 +98,13 @@ We use the LM Evaluation Harness to evaluate our model on the En benchmarks task
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  | IndicSentiment (0-shot) | Accuracy | 0.65 | 0.70 | 0.95 | 0.96 |0.99 | 0.98 |
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  | IndicCOPA (0-shot) | Accuracy | 0.51 | 0.58 | 0.80 | 0.83 | 0.88 | 0.91 |
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  | IndicXParaphrase (0-shot) | Accuracy | 0.67 | 0.74 | 0.88 | 0.87 | 0.89 | 0.91 |
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- | IndicXNLI (0-shot) | Accuracy | 0.47 | 0.54 | 0.55 | 0.61 | TBD | 0.67 |
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  | IndicQA (0-shot) | Bert Score | 0.90 | 0.90 | 0.91 | 0.89 | 0.94 | TBD |
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  | CrossSumIN (1-shot) | chrF++ | 0.04 | 0.17 | 0.21 | 0.26 | 0.24 | 0.24 |
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  | FloresIN Translation xx-en (1-shot) | chrF++ | 0.54 | 0.50 | 0.58 | 0.60 | 0.62 | 0.63 |
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  | FloresIN Translation en-xx (1-shot) | chrF++ | 0.41 | 0.34 | 0.48 | 0.46 | 0.47 | 0.48 |
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- | IN22 Translation xx-en (0-shot) | chrF++ | 0.50 | 0.48 | 0.57 | 0.58 | 0.55 | 0.55 |
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- | IN22 Translation en-xx (0-shot) | chrF++ | 0.36 | 0.33 | 0.45 | 0.42 | 0.44 | 0.43 |
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  ### BharatBench
 
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  | IndicSentiment (0-shot) | Accuracy | 0.65 | 0.70 | 0.95 | 0.96 |0.99 | 0.98 |
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  | IndicCOPA (0-shot) | Accuracy | 0.51 | 0.58 | 0.80 | 0.83 | 0.88 | 0.91 |
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  | IndicXParaphrase (0-shot) | Accuracy | 0.67 | 0.74 | 0.88 | 0.87 | 0.89 | 0.91 |
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+ | IndicXNLI (0-shot) | Accuracy | 0.47 | 0.54 | 0.55 | 0.61 | 0.70 | 0.67 |
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  | IndicQA (0-shot) | Bert Score | 0.90 | 0.90 | 0.91 | 0.89 | 0.94 | TBD |
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  | CrossSumIN (1-shot) | chrF++ | 0.04 | 0.17 | 0.21 | 0.26 | 0.24 | 0.24 |
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  | FloresIN Translation xx-en (1-shot) | chrF++ | 0.54 | 0.50 | 0.58 | 0.60 | 0.62 | 0.63 |
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  | FloresIN Translation en-xx (1-shot) | chrF++ | 0.41 | 0.34 | 0.48 | 0.46 | 0.47 | 0.48 |
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+ | IN22 Translation xx-en (0-shot) | chrF++ | 0.50 | 0.48 | 0.57 | 0.58 | 0.55 | 0.60 |
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+ | IN22 Translation en-xx (0-shot) | chrF++ | 0.36 | 0.33 | 0.45 | 0.42 | 0.44 | 0.44 |
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  ### BharatBench