--- annotations_creators: - derived language: - ara - deu - fra - hin - ita - jpn - por - spa license: cc-by-4.0 multilinguality: translated source_datasets: - jinaai/mintakaqa task_categories: - text-retrieval task_ids: - multiple-choice-qa dataset_info: - config_name: ar-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 59219 num_examples: 1491 download_size: 35162 dataset_size: 59219 - config_name: ar-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 54567 num_examples: 2203 download_size: 24666 dataset_size: 54567 - config_name: ar-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 245632 num_examples: 2203 download_size: 117692 dataset_size: 245632 - config_name: de-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 51068 num_examples: 1655 download_size: 36909 dataset_size: 51068 - config_name: de-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 59047 num_examples: 2374 download_size: 26771 dataset_size: 59047 - config_name: de-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 187724 num_examples: 2374 download_size: 104325 dataset_size: 187724 - config_name: es-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 52100 num_examples: 1693 download_size: 37479 dataset_size: 52100 - config_name: es-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 60331 num_examples: 2424 download_size: 27256 dataset_size: 60331 - config_name: es-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 195740 num_examples: 2424 download_size: 105150 dataset_size: 195740 - config_name: fr-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 52994 num_examples: 1714 download_size: 38059 dataset_size: 52994 - config_name: fr-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 60803 num_examples: 2442 download_size: 27556 dataset_size: 60803 - config_name: fr-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 206591 num_examples: 2442 download_size: 110167 dataset_size: 206591 - config_name: hi-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 38005 num_examples: 770 download_size: 20066 dataset_size: 38005 - config_name: hi-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 32136 num_examples: 1337 download_size: 14220 dataset_size: 32136 - config_name: hi-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 213666 num_examples: 1337 download_size: 83801 dataset_size: 213666 - config_name: it-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 51135 num_examples: 1664 download_size: 36845 dataset_size: 51135 - config_name: it-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 59615 num_examples: 2396 download_size: 26981 dataset_size: 59615 - config_name: it-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 186497 num_examples: 2395 download_size: 103718 dataset_size: 186497 - config_name: ja-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 65908 num_examples: 1592 download_size: 38696 dataset_size: 65908 - config_name: ja-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 57373 num_examples: 2312 download_size: 26055 dataset_size: 57373 - config_name: ja-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 224887 num_examples: 2312 download_size: 114370 dataset_size: 224887 - config_name: pt-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 49999 num_examples: 1628 download_size: 36198 dataset_size: 49999 - config_name: pt-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 58537 num_examples: 2355 download_size: 26506 dataset_size: 58537 - config_name: pt-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 174935 num_examples: 2354 download_size: 98167 dataset_size: 174935 configs: - config_name: ar-corpus data_files: - split: test path: ar-corpus/test-* - config_name: ar-qrels data_files: - split: test path: ar-qrels/test-* - config_name: ar-queries data_files: - split: test path: ar-queries/test-* - config_name: de-corpus data_files: - split: test path: de-corpus/test-* - config_name: de-qrels data_files: - split: test path: de-qrels/test-* - config_name: de-queries data_files: - split: test path: de-queries/test-* - config_name: es-corpus data_files: - split: test path: es-corpus/test-* - config_name: es-qrels data_files: - split: test path: es-qrels/test-* - config_name: es-queries data_files: - split: test path: es-queries/test-* - config_name: fr-corpus data_files: - split: test path: fr-corpus/test-* - config_name: fr-qrels data_files: - split: test path: fr-qrels/test-* - config_name: fr-queries data_files: - split: test path: fr-queries/test-* - config_name: hi-corpus data_files: - split: test path: hi-corpus/test-* - config_name: hi-qrels data_files: - split: test path: hi-qrels/test-* - config_name: hi-queries data_files: - split: test path: hi-queries/test-* - config_name: it-corpus data_files: - split: test path: it-corpus/test-* - config_name: it-qrels data_files: - split: test path: it-qrels/test-* - config_name: it-queries data_files: - split: test path: it-queries/test-* - config_name: ja-corpus data_files: - split: test path: ja-corpus/test-* - config_name: ja-qrels data_files: - split: test path: ja-qrels/test-* - config_name: ja-queries data_files: - split: test path: ja-queries/test-* - config_name: pt-corpus data_files: - split: test path: pt-corpus/test-* - config_name: pt-qrels data_files: - split: test path: pt-qrels/test-* - config_name: pt-queries data_files: - split: test path: pt-queries/test-* tags: - mteb - text ---

MintakaRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Encyclopaedic, Written | | Reference | | Source datasets: - [jinaai/mintakaqa](https://huggingface.co/datasets/jinaai/mintakaqa) ## 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("MintakaRetrieval") evaluator = mteb.MTEB([task]) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` 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 @inproceedings{sen-etal-2022-mintaka, address = {Gyeongju, Republic of Korea}, author = {Sen, Priyanka and Aji, Alham Fikri and Saffari, Amir}, booktitle = {Proceedings of the 29th International Conference on Computational Linguistics}, month = oct, pages = {1604--1619}, publisher = {International Committee on Computational Linguistics}, title = {Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering}, url = {https://aclanthology.org/2022.coling-1.138}, year = {2022}, } @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
Dataset Statistics The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("MintakaRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json {} ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*