--- 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/NanoArguAna - 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: 5668736 num_examples: 3635 download_size: 2513373 dataset_size: 5668736 - config_name: ar-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: ar-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 82991 num_examples: 50 download_size: 45731 dataset_size: 82991 - config_name: de-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4384035 num_examples: 3635 download_size: 2429845 dataset_size: 4384035 - config_name: de-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: de-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 65178 num_examples: 50 download_size: 42543 dataset_size: 65178 - config_name: en-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3854860 num_examples: 3635 download_size: 2166574 dataset_size: 3854860 - config_name: en-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: en-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 62331 num_examples: 50 download_size: 43021 dataset_size: 62331 - config_name: es-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4271673 num_examples: 3635 download_size: 2343252 dataset_size: 4271673 - config_name: es-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: es-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 64101 num_examples: 50 download_size: 43798 dataset_size: 64101 - config_name: fr-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4490981 num_examples: 3635 download_size: 2441786 dataset_size: 4490981 - config_name: fr-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: fr-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 67535 num_examples: 50 download_size: 44267 dataset_size: 67535 - config_name: it-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4204178 num_examples: 3635 download_size: 2336445 dataset_size: 4204178 - config_name: it-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: it-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 61837 num_examples: 50 download_size: 42165 dataset_size: 61837 - config_name: ja-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4725257 num_examples: 3635 download_size: 2540959 dataset_size: 4725257 - config_name: ja-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: ja-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 77515 num_examples: 50 download_size: 52037 dataset_size: 77515 - config_name: ko-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4495539 num_examples: 3635 download_size: 2459038 dataset_size: 4495539 - config_name: ko-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: ko-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 73164 num_examples: 50 download_size: 49903 dataset_size: 73164 - config_name: no-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3824994 num_examples: 3635 download_size: 2155753 dataset_size: 3824994 - config_name: no-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: no-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 57641 num_examples: 50 download_size: 39886 dataset_size: 57641 - config_name: pt-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4151660 num_examples: 3635 download_size: 2310466 dataset_size: 4151660 - config_name: pt-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: pt-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 61690 num_examples: 50 download_size: 41578 dataset_size: 61690 - config_name: sv-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3973260 num_examples: 3635 download_size: 2199289 dataset_size: 3973260 - config_name: sv-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3896 num_examples: 50 download_size: 3798 dataset_size: 3896 - config_name: sv-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 59148 num_examples: 50 download_size: 40439 dataset_size: 59148 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 ---

MultilingualNanoArguAnaRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
NanoArguAna is a smaller subset of ArguAna, a dataset for argument retrieval in debate contexts. | | | |---------------|---------------------------------------------| | Task category | Retrieval (text-to-text) | | Domains | Social, Web, Written | | Reference | [ACL](https://huggingface.co/datasets/LiquidAI/nanobeir-multilingual-extended) | Source datasets: - [zeta-alpha-ai/NanoArguAna](https://huggingface.co/datasets/zeta-alpha-ai/NanoArguAna) - [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("MultilingualNanoArguAnaRetrieval") 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 @inproceedings{wachsmuth2018retrieval, author = {Wachsmuth, Henning and Syed, Shahbaz and Stein, Benno}, booktitle = {ACL}, title = {Retrieval of the Best Counterargument without Prior Topic Knowledge}, year = {2018}, } @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("MultilingualNanoArguAnaRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 40535, "num_queries": 550, "num_documents": 39985, "number_of_characters": 38443190, "documents_text_statistics": { "total_text_length": 37866168, "min_text_length": 33, "average_text_length": 947.0093284981868, "max_text_length": 79698, "unique_texts": 39954 }, "documents_image_statistics": null, "documents_audio_statistics": null, "documents_video_statistics": null, "queries_text_statistics": { "total_text_length": 577022, "min_text_length": 199, "average_text_length": 1049.1309090909092, "max_text_length": 2525, "unique_texts": 550 }, "queries_image_statistics": null, "queries_audio_statistics": null, "queries_video_statistics": null, "relevant_docs_statistics": { "num_relevant_docs": 550, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.0, "max_relevant_docs_per_query": 1, "unique_relevant_docs": 550 }, "top_ranked_statistics": null } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*