---
configs:
- config_name: ar
  data_files:
  - split: test
    path: ar.jsonl
- config_name: da
  data_files:
  - split: test
    path: da.jsonl
- config_name: de
  data_files:
  - split: test
    path: de.jsonl
- config_name: en
  data_files:
  - split: test
    path: en.jsonl
- config_name: es
  data_files:
  - split: test
    path: es.jsonl
- config_name: fi
  data_files:
  - split: test
    path: fi.jsonl
- config_name: fr
  data_files:
  - split: test
    path: fr.jsonl
- config_name: he
  data_files:
  - split: test
    path: he.jsonl
- config_name: hu
  data_files:
  - split: test
    path: hu.jsonl
- config_name: it
  data_files:
  - split: test
    path: it.jsonl
- config_name: ja
  data_files:
  - split: test
    path: ja.jsonl
- config_name: ko
  data_files:
  - split: test
    path: ko.jsonl
- config_name: km
  data_files:
  - split: test
    path: km.jsonl
- config_name: ms
  data_files:
  - split: test
    path: ms.jsonl
- config_name: nl
  data_files:
  - split: test
    path: nl.jsonl
- config_name: 'no'
  data_files:
  - split: test
    path: 'no.jsonl'
- config_name: pl
  data_files:
  - split: test
    path: pl.jsonl
- config_name: pt
  data_files:
  - split: test
    path: pt.jsonl
- config_name: ru
  data_files:
  - split: test
    path: ru.jsonl
- config_name: sv
  data_files:
  - split: test
    path: sv.jsonl
- config_name: th
  data_files:
  - split: test
    path: th.jsonl
- config_name: tr
  data_files:
  - split: test
    path: tr.jsonl
- config_name: vi
  data_files:
  - split: test
    path: vi.jsonl
- config_name: zh
  data_files:
  - split: test
    path: zh.jsonl
    
---
# Dataset Description
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).

For more details about Global-MMLU, see the official [dataset repo](https://huggingface.co/datasets/apple/mkqa).

If you find the dataset useful, please cite it as follows:
```bibtex
@article{longpre-etal-2021-mkqa,
    title = "{MKQA}: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering",
    author = "Longpre, Shayne  and
      Lu, Yi  and
      Daiber, Joachim",
    editor = "Roark, Brian  and
      Nenkova, Ani",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "9",
    year = "2021",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/2021.tacl-1.82/",
    doi = "10.1162/tacl_a_00433",
    pages = "1389--1406",
    abstract = "Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets. We introduce Multilingual Knowledge Questions and Answers (MKQA), an open- domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). Answers are based on heavily curated, language- independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering. We benchmark a variety of state- of-the-art methods and baselines for generative and extractive question answering, trained on Natural Questions, in zero shot and translation settings. Results indicate this dataset is challenging even in English, but especially in low-resource languages.1"
}
```