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@@ -170,7 +170,13 @@ Fair warning BGE-M3 is $ expensive to evaluate, probably that's why it's not par
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  # Reference:
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  - [All Cohere numbers are copied form here](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12)
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  # Note on model bias:
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  - Like any model this model might carry inherent biases from the base models and the datasets it was pretrained and finetuned on. Please use responsibly.
 
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  # Reference:
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  - [All Cohere numbers are copied form here](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12)
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+ - [BGE M3-Embedding: Multi-Lingual, Multi-Functionality,
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+ Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://arxiv.org/pdf/2402.03216.pdf)
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+ - [Making a MIRACL: Multilingual Information Retrieval
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+ Across a Continuum of Languages](https://arxiv.org/pdf/2210.09984.pdf)
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+ - [IndicIRSuite: Multilingual Dataset and Neural
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+ Information Models for Indian Languages](https://arxiv.org/pdf/2312.09508)
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  # Note on model bias:
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  - Like any model this model might carry inherent biases from the base models and the datasets it was pretrained and finetuned on. Please use responsibly.