README.md

---
annotations_creators:
- human-annotated
language:
- arb
- deu
- ell
- eng
- hin
- ron
- rus
- spa
- tha
- tur
- vie
- zho
license: cc-by-sa-4.0
multilinguality: multilingual
source_datasets:
- google/xquad
task_categories:
- text-retrieval
- multiple-choice-qa
- question-answering
task_ids:
- multiple-choice-qa
- question-answering
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tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">XQuADRetrieval</h1>
  <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

XQuAD is a benchmark dataset for evaluating cross-lingual question answering performance. It is repurposed retrieving relevant context for each question.

|               |                                             |
|---------------|---------------------------------------------|
| Task category | t2t                              |
| Domains       | Web, Written                               |
| Reference     | https://huggingface.co/datasets/xquad |

Source datasets:
- [google/xquad](https://huggingface.co/datasets/google/xquad)


## How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

```python
import mteb

task = mteb.get_task("XQuADRetrieval")
evaluator = mteb.MTEB([task])

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repository](https://github.com/embeddings-benchmark/mteb).

## Citation

If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).

```bibtex

@article{Artetxe:etal:2019,
  archiveprefix = {arXiv},
  author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
  eprint = {1910.11856},
  journal = {CoRR},
  title = {On the cross-lingual transferability of monolingual representations},
  volume = {abs/1910.11856},
  year = {2019},
}

@inproceedings{dumitrescu2021liro,
  author = {Stefan Daniel Dumitrescu and Petru Rebeja and Beata Lorincz and Mihaela Gaman and Andrei Avram and Mihai Ilie and Andrei Pruteanu and Adriana Stan and Lorena Rosia and Cristina Iacobescu and Luciana Morogan and George Dima and Gabriel Marchidan and Traian Rebedea and Madalina Chitez and Dani Yogatama and Sebastian Ruder and Radu Tudor Ionescu and Razvan Pascanu and Viorica Patraucean},
  booktitle = {Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
  title = {LiRo: Benchmark and leaderboard for Romanian language tasks},
  url = {https://openreview.net/forum?id=JH61CD7afTv},
  year = {2021},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}
```

# Dataset Statistics
<details>
  <summary> Dataset Statistics</summary>

The following code contains the descriptive statistics from the task. These can also be obtained using:

```python
import mteb

task = mteb.get_task("XQuADRetrieval")

desc_stats = task.metadata.descriptive_stats
```

```json
{
    "validation": {
        "num_samples": 17079,
        "number_of_characters": 3049962,
        "documents_text_statistics": {
            "total_text_length": 2211718,
            "min_text_length": 56,
            "average_text_length": 767.9576388888889,
            "max_text_length": 3884,
            "unique_texts": 2880
        },
        "documents_image_statistics": null,
        "queries_text_statistics": {
            "total_text_length": 838244,
            "min_text_length": 6,
            "average_text_length": 59.03542502993169,
            "max_text_length": 307,
            "unique_texts": 14196
        },
        "queries_image_statistics": null,
        "relevant_docs_statistics": {
            "num_relevant_docs": 14199,
            "min_relevant_docs_per_query": 1,
            "average_relevant_docs_per_query": 1.0,
            "max_relevant_docs_per_query": 1,
            "unique_relevant_docs": 2880
        },
        "top_ranked_statistics": null
    }
}
```

</details>

---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*