--- 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/NanoNFCorpus - 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: 7380822 num_examples: 2953 download_size: 3074397 dataset_size: 7380822 - config_name: ar-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: ar-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2975 num_examples: 50 download_size: 3258 dataset_size: 2975 - config_name: de-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5236634 num_examples: 2953 download_size: 2742111 dataset_size: 5236634 - config_name: de-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: de-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2361 num_examples: 50 download_size: 3066 dataset_size: 2361 - config_name: en-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4521394 num_examples: 2953 download_size: 2446454 dataset_size: 4521394 - config_name: en-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: en-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 1939 num_examples: 50 download_size: 2815 dataset_size: 1939 - config_name: es-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5258531 num_examples: 2953 download_size: 2688384 dataset_size: 5258531 - config_name: es-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: es-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2282 num_examples: 50 download_size: 3014 dataset_size: 2282 - config_name: fr-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5590747 num_examples: 2953 download_size: 2801063 dataset_size: 5590747 - config_name: fr-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: fr-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2398 num_examples: 50 download_size: 3163 dataset_size: 2398 - config_name: it-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5170490 num_examples: 2953 download_size: 2705321 dataset_size: 5170490 - config_name: it-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: it-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2317 num_examples: 50 download_size: 3033 dataset_size: 2317 - config_name: ja-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5296700 num_examples: 2953 download_size: 2687649 dataset_size: 5296700 - config_name: ja-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: ja-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2447 num_examples: 50 download_size: 3095 dataset_size: 2447 - config_name: ko-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4982616 num_examples: 2953 download_size: 2635750 dataset_size: 4982616 - config_name: ko-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: ko-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2255 num_examples: 50 download_size: 3000 dataset_size: 2255 - config_name: no-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4542371 num_examples: 2953 download_size: 2480917 dataset_size: 4542371 - config_name: no-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: no-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2116 num_examples: 50 download_size: 2921 dataset_size: 2116 - config_name: pt-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5071806 num_examples: 2953 download_size: 2646717 dataset_size: 5071806 - config_name: pt-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: pt-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2282 num_examples: 50 download_size: 3053 dataset_size: 2282 - config_name: sv-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4607632 num_examples: 2953 download_size: 2509834 dataset_size: 4607632 - config_name: sv-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 84995 num_examples: 2518 download_size: 13680 dataset_size: 84995 - config_name: sv-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 2083 num_examples: 50 download_size: 2850 dataset_size: 2083 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 ---

MultilingualNanoNFCorpusRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
NanoNFCorpus is a smaller subset of NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval. | | | |---------------|---------------------------------------------| | Task category | Retrieval (text-to-text) | | Domains | Medical, Academic, Written | | Reference | [A Full-Text Learning to Rank Dataset for Medical Information Retrieval](https://huggingface.co/datasets/LiquidAI/nanobeir-multilingual-extended) | Source datasets: - [zeta-alpha-ai/NanoNFCorpus](https://huggingface.co/datasets/zeta-alpha-ai/NanoNFCorpus) - [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("MultilingualNanoNFCorpusRetrieval") 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{boteva2016, author = {Boteva, Vera and Gholipour, Demian and Sokolov, Artem and Riezler, Stefan}, city = {Padova}, country = {Italy}, journal = {Proceedings of the 38th European Conference on Information Retrieval}, journal-abbrev = {ECIR}, title = {A Full-Text Learning to Rank Dataset for Medical Information Retrieval}, url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/ECIR2016.pdf}, year = {2016}, } @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("MultilingualNanoNFCorpusRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 33033, "num_queries": 550, "num_documents": 32483, "number_of_characters": 47168342, "documents_text_statistics": { "total_text_length": 47155673, "min_text_length": 35, "average_text_length": 1451.703137025521, "max_text_length": 57995, "unique_texts": 32447 }, "documents_image_statistics": null, "documents_audio_statistics": null, "documents_video_statistics": null, "queries_text_statistics": { "total_text_length": 12669, "min_text_length": 2, "average_text_length": 23.034545454545455, "max_text_length": 101, "unique_texts": 521 }, "queries_image_statistics": null, "queries_audio_statistics": null, "queries_video_statistics": null, "relevant_docs_statistics": { "num_relevant_docs": 27698, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 50.36, "max_relevant_docs_per_query": 463, "unique_relevant_docs": 17897 }, "top_ranked_statistics": null } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*