--- annotations_creators: - derived language: - ara - deu - eng - fra - ita - jpn - kor - nor - por - spa - swe license: cc-by-4.0 multilinguality: translated source_datasets: - zeta-alpha-ai/NanoNQ - LiquidAI/nanobeir-multilingual-extended task_categories: - text-retrieval task_ids: - multiple-choice-qa dataset_info: - config_name: ar-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4068842 num_examples: 5035 download_size: 2051862 dataset_size: 4068842 - config_name: ar-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: ar-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4431 num_examples: 50 download_size: 4222 dataset_size: 4431 - config_name: de-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3091937 num_examples: 5035 download_size: 1937761 dataset_size: 3091937 - config_name: de-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: de-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3584 num_examples: 50 download_size: 4140 dataset_size: 3584 - config_name: en-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2740852 num_examples: 5035 download_size: 1740902 dataset_size: 2740852 - config_name: en-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: en-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3132 num_examples: 50 download_size: 3644 dataset_size: 3132 - config_name: es-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3033561 num_examples: 5035 download_size: 1879179 dataset_size: 3033561 - config_name: es-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: es-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3590 num_examples: 50 download_size: 4089 dataset_size: 3590 - config_name: fr-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3151567 num_examples: 5035 download_size: 1932590 dataset_size: 3151567 - config_name: fr-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: fr-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3818 num_examples: 50 download_size: 4311 dataset_size: 3818 - config_name: it-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3005962 num_examples: 5035 download_size: 1880592 dataset_size: 3005962 - config_name: it-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: it-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3521 num_examples: 50 download_size: 4026 dataset_size: 3521 - config_name: ja-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3539146 num_examples: 5035 download_size: 2081014 dataset_size: 3539146 - config_name: ja-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: ja-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 6622 num_examples: 50 download_size: 5995 dataset_size: 6622 - config_name: ko-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3256050 num_examples: 5035 download_size: 2017503 dataset_size: 3256050 - config_name: ko-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: ko-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4182 num_examples: 50 download_size: 4457 dataset_size: 4182 - config_name: no-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2758468 num_examples: 5035 download_size: 1740408 dataset_size: 2758468 - config_name: no-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: no-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3218 num_examples: 50 download_size: 3791 dataset_size: 3218 - config_name: pt-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2929253 num_examples: 5035 download_size: 1857783 dataset_size: 2929253 - config_name: pt-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: pt-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3435 num_examples: 50 download_size: 4050 dataset_size: 3435 - config_name: sv-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2826385 num_examples: 5035 download_size: 1762668 dataset_size: 2826385 - config_name: sv-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1796 num_examples: 57 download_size: 2473 dataset_size: 1796 - config_name: sv-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3166 num_examples: 50 download_size: 3771 dataset_size: 3166 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: en-corpus data_files: - split: test path: en-corpus/test-* - config_name: en-qrels data_files: - split: test path: en-qrels/test-* - config_name: en-queries data_files: - split: test path: en-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: 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: ko-corpus data_files: - split: test path: ko-corpus/test-* - config_name: ko-qrels data_files: - split: test path: ko-qrels/test-* - config_name: ko-queries data_files: - split: test path: ko-queries/test-* - config_name: no-corpus data_files: - split: test path: no-corpus/test-* - config_name: no-qrels data_files: - split: test path: no-qrels/test-* - config_name: no-queries data_files: - split: test path: no-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-* - config_name: sv-corpus data_files: - split: test path: sv-corpus/test-* - config_name: sv-qrels data_files: - split: test path: sv-qrels/test-* - config_name: sv-queries data_files: - split: test path: sv-queries/test-* tags: - mteb - text ---

MultilingualNanoNQRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
NanoNQ is a smaller subset of a dataset which contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. | | | |---------------|---------------------------------------------| | Task category | Retrieval (text-to-text) | | Domains | Academic, Web | | Reference | [Natural Questions: a Benchmark for Question Answering Research](https://huggingface.co/datasets/LiquidAI/nanobeir-multilingual-extended) | Source datasets: - [zeta-alpha-ai/NanoNQ](https://huggingface.co/datasets/zeta-alpha-ai/NanoNQ) - [LiquidAI/nanobeir-multilingual-extended](https://huggingface.co/datasets/LiquidAI/nanobeir-multilingual-extended) ## 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("MultilingualNanoNQRetrieval") model = mteb.get_model(YOUR_MODEL) mteb.evaluate(model, task) ``` 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{47761, author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov}, journal = {Transactions of the Association of Computational Linguistics}, title = {Natural Questions: a Benchmark for Question Answering Research}, year = {2019}, } @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("MultilingualNanoNQRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 55935, "num_queries": 550, "num_documents": 55385, "number_of_characters": 27298486, "documents_text_statistics": { "total_text_length": 27272157, "min_text_length": 1, "average_text_length": 492.41052631578947, "max_text_length": 46225, "unique_texts": 54730 }, "documents_image_statistics": null, "documents_audio_statistics": null, "documents_video_statistics": null, "queries_text_statistics": { "total_text_length": 26329, "min_text_length": 12, "average_text_length": 47.87090909090909, "max_text_length": 463, "unique_texts": 550 }, "queries_image_statistics": null, "queries_audio_statistics": null, "queries_video_statistics": null, "relevant_docs_statistics": { "num_relevant_docs": 627, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.14, "max_relevant_docs_per_query": 2, "unique_relevant_docs": 627 }, "top_ranked_statistics": null } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*