--- 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/NanoQuoraRetrieval - LiquidAI/nanobeir-multilingual-extended task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: ar-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 582228 num_examples: 5046 download_size: 288057 dataset_size: 582228 - config_name: ar-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: ar-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4628 num_examples: 50 download_size: 4462 dataset_size: 4628 - config_name: de-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 402050 num_examples: 5046 download_size: 252849 dataset_size: 402050 - config_name: de-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: de-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3501 num_examples: 50 download_size: 4317 dataset_size: 3501 - config_name: en-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 346228 num_examples: 5046 download_size: 224510 dataset_size: 346228 - config_name: en-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: en-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3087 num_examples: 50 download_size: 3889 dataset_size: 3087 - config_name: es-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 408871 num_examples: 5046 download_size: 248191 dataset_size: 408871 - config_name: es-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: es-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3591 num_examples: 50 download_size: 4239 dataset_size: 3591 - config_name: fr-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 438592 num_examples: 5046 download_size: 259854 dataset_size: 438592 - config_name: fr-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: fr-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3819 num_examples: 50 download_size: 4367 dataset_size: 3819 - config_name: it-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 398334 num_examples: 5046 download_size: 249145 dataset_size: 398334 - config_name: it-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: it-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3358 num_examples: 50 download_size: 4078 dataset_size: 3358 - config_name: ja-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 536226 num_examples: 5046 download_size: 296462 dataset_size: 536226 - config_name: ja-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: ja-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4630 num_examples: 50 download_size: 4644 dataset_size: 4630 - config_name: ko-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 467577 num_examples: 5046 download_size: 273295 dataset_size: 467577 - config_name: ko-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: ko-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4178 num_examples: 50 download_size: 4612 dataset_size: 4178 - config_name: no-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 367796 num_examples: 5046 download_size: 230877 dataset_size: 367796 - config_name: no-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: no-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3271 num_examples: 50 download_size: 3844 dataset_size: 3271 - config_name: pt-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 394871 num_examples: 5046 download_size: 244714 dataset_size: 394871 - config_name: pt-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: pt-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3491 num_examples: 50 download_size: 4193 dataset_size: 3491 - config_name: sv-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 371488 num_examples: 5046 download_size: 232739 dataset_size: 371488 - config_name: sv-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1919 num_examples: 70 download_size: 2737 dataset_size: 1919 - config_name: sv-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3226 num_examples: 50 download_size: 3923 dataset_size: 3226 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 ---

MultilingualNanoQuoraRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
NanoQuoraRetrieval is a smaller subset of the QuoraRetrieval dataset, which is based on questions that are marked as duplicates on the Quora platform. Given a question, find other (duplicate) questions. | | | |---------------|---------------------------------------------| | Task category | Retrieval (text-to-text) | | Domains | Social | | Reference | [Quora Question Pairs](https://huggingface.co/datasets/LiquidAI/nanobeir-multilingual-extended) | Source datasets: - [zeta-alpha-ai/NanoQuoraRetrieval](https://huggingface.co/datasets/zeta-alpha-ai/NanoQuoraRetrieval) - [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("MultilingualNanoQuoraRetrieval") 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 @misc{quora-question-pairs, author = {DataCanary, hilfialkaff, Lili Jiang, Meg Risdal, Nikhil Dandekar, tomtung}, publisher = {Kaggle}, title = {Quora Question Pairs}, url = {https://kaggle.com/competitions/quora-question-pairs}, year = {2017}, } @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("MultilingualNanoQuoraRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 56056, "num_queries": 550, "num_documents": 55506, "number_of_characters": 3159403, "documents_text_statistics": { "total_text_length": 3133135, "min_text_length": 2, "average_text_length": 56.44678052823118, "max_text_length": 36879, "unique_texts": 54909 }, "documents_image_statistics": null, "documents_audio_statistics": null, "documents_video_statistics": null, "queries_text_statistics": { "total_text_length": 26268, "min_text_length": 12, "average_text_length": 47.76, "max_text_length": 139, "unique_texts": 549 }, "queries_image_statistics": null, "queries_audio_statistics": null, "queries_video_statistics": null, "relevant_docs_statistics": { "num_relevant_docs": 770, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.4, "max_relevant_docs_per_query": 6, "unique_relevant_docs": 770 }, "top_ranked_statistics": null } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*