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README.md
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data_files:
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- split: test
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path: da.jsonl
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- config_name: de
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data_files:
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- split: test
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path: de.jsonl
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- config_name: en
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data_files:
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- split: test
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path: en.jsonl
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- config_name: es
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data_files:
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- split: test
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path: es.jsonl
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- config_name: fi
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data_files:
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- split: test
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path: fi.jsonl
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- config_name: fr
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data_files:
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- split: test
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path: fr.jsonl
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- config_name: he
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data_files:
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- split: test
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path: he.jsonl
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- config_name: hu
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data_files:
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path: hu.jsonl
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- config_name: it
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data_files:
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- split: test
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path: it.jsonl
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- config_name: ja
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data_files:
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- split: test
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path: ja.jsonl
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- config_name: ko
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data_files:
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- split: test
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path: ko.jsonl
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- config_name: km
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data_files:
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- split: test
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path: km.jsonl
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- config_name: ms
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data_files:
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- split: test
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path: ms.jsonl
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- config_name: nl
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data_files:
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- split: test
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path: nl.jsonl
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- config_name: no
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data_files:
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- split: test
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path: no.jsonl
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- config_name: pl
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data_files:
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- split: test
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path: pl.jsonl
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- config_name: pt
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data_files:
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- split: test
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path: pt.jsonl
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- config_name: ru
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data_files:
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- split: test
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path: ru.jsonl
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- config_name: sv
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data_files:
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- split: test
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path: sv.jsonl
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- config_name: th
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data_files:
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- split: test
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path: th.jsonl
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- config_name: tr
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data_files:
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- split: test
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path: tr.jsonl
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- config_name: vi
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data_files:
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- split: test
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path: vi.jsonl
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- config_name: zh
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data_files:
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- split: test
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path: zh.jsonl
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---
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# Dataset Description
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This is the [MKQA](https://github.com/apple/ml-mkqa?tab=readme-ov-file) ***with query embeddings***, which can be used jointly with [Multilingual Embeddings for Wikipedia in 300+ Languages](https://huggingface.co/datasets/Cohere/wikipedia-2023-11-embed-multilingual-v3) for doing multilingual passage retrieval, since the vectors are calculated via the same embedder [Cohere Embed v3](https://cohere.com/blog/introducing-embed-v3).
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data_files:
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- split: test
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path: da.jsonl
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---
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# Dataset Description
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This is the [MKQA](https://github.com/apple/ml-mkqa?tab=readme-ov-file) ***with query embeddings***, which can be used jointly with [Multilingual Embeddings for Wikipedia in 300+ Languages](https://huggingface.co/datasets/Cohere/wikipedia-2023-11-embed-multilingual-v3) for doing multilingual passage retrieval, since the vectors are calculated via the same embedder [Cohere Embed v3](https://cohere.com/blog/introducing-embed-v3).
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