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README.md
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@@ -60,25 +60,26 @@ The large language model underwent rigorous training to excel in performing a va
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Dataset | exact_match | rouge1 | bleu | bertscore
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ragbench-
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ragbench-tatqa-
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ragbench-
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ragbench-
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ragbench-
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ragbench-
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ragbench-
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SILMA RAG QA Benchmark Score: 0.3478
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|Dataset | exact_match | rouge1 | bleu | bertscore|
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|ragbench-finqa-en-test | 0.000 | 0.587 | 0.321 | 0.760|
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|ragbench-tatqa-ar-test | 0.000 | 0.484 | 0.130 | 0.774|
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|ragbench-tatqa-en-test | 0.059 | 0.646 | 0.423 | 0.808|
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|rag-instruct-benchmark-tester-en | 0.370 | 0.683 | 0.196 | 0.791|
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|ragbench-expertqa-en-test |0.000 | 0.465 | 0.151 | 0.677|
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|ragbench-msmarco-ar-test |0.000 | 0.144 | 0.096 | 0.781|
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|sciq-ar-test |0.170 | 0.000 | 0.000 | 0.753|
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|ragbench-covidqa-en-test |0.020 | 0.521 | 0.242 | 0.734|
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|ragbench-emanual-ar-test |0.000 | 0.237 | 0.159 | 0.806|
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|ragbench-finqa-ar-test |0.000 | 0.377 | 0.109 | 0.780|
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|xquad-r-validation-en |0.120 | 0.326 | 0.041 | 0.603|
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|ragbench-emanual-en-test |0.000 | 0.565 | 0.288 | 0.722|
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|xquad-r-ar-validation |0.070 | 0.130 | 0.042 | 0.698|
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|boolq-ar-test |0.450 | 0.000 | 0.000 | 0.700|
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|ragbench-hotpotqa-en-test |0.060 | 0.732 | 0.503 | 0.837|
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|ragbench-covidqa-ar-test |0.000 | 0.179 | 0.104 | 0.783|
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|ragbench-msmarco-en-test |0.020 | 0.491 | 0.207 | 0.729|
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|### Benchmark Average Scores |0.079 | 0.386 | 0.177 | 0.749|
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SILMA RAG QA Benchmark Score: 0.3478
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