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As part of ongoing efforts to make Information Retrieval (IR) more inclusive, this model addresses the needs of low-resource languages, focusing specifically on Urdu.
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We created this model by translating the MS-Marco dataset into Urdu using the IndicTrans2 model.
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To establish baseline performance, we initially tested for zero-shot learning for IR in Urdu using the unicamp-dl/mt5-base-mmarco-v2 model
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and then applied fine-tuning with the mMARCO multilingual IR methodology on the translated dataset
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## Model Details
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As part of ongoing efforts to make Information Retrieval (IR) more inclusive, this model addresses the needs of low-resource languages, focusing specifically on Urdu.
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We created this model by translating the MS-Marco dataset into Urdu using the IndicTrans2 model.
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To establish baseline performance, we initially tested for zero-shot learning for IR in Urdu using the unicamp-dl/mt5-base-mmarco-v2 model
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and then applied fine-tuning with the mMARCO multilingual IR methodology on the translated dataset, resulting in State-Of-The-Art results for urdu IR
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## Model Details
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