--- annotations_creators: - derived language: - fas license: unknown multilinguality: monolingual source_datasets: - mteb/nq task_categories: - text-retrieval task_ids: - multiple-choice-qa dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 2089201875 num_examples: 2681468 download_size: 964979768 dataset_size: 2089201875 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 133323 num_examples: 4201 download_size: 49635 dataset_size: 133323 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: test num_bytes: 310566 num_examples: 3452 download_size: 168210 dataset_size: 310566 configs: - config_name: corpus data_files: - split: test path: corpus/test-* - config_name: qrels data_files: - split: test path: qrels/test-* - config_name: queries data_files: - split: test path: queries/test-* tags: - mteb - text ---

NQ-Fa

An MTEB dataset
Massive Text Embedding Benchmark
NQ-Fa | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Encyclopaedic | | Reference | https://huggingface.co/datasets/MCINext/nq-fa | ## 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_tasks(["NQ-Fa"]) 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 repitory](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{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, Lo{\"\i}c 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("NQ-Fa") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 2684920, "number_of_characters": 1167597064, "num_documents": 2681468, "min_document_length": 3, "average_document_length": 435.3785184085732, "max_document_length": 5304, "unique_documents": 2681468, "num_queries": 3452, "min_query_length": 15, "average_query_length": 41.56981460023175, "max_query_length": 102, "unique_queries": 3452, "none_queries": 0, "num_relevant_docs": 4201, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 1.2169756662804172, "max_relevant_docs_per_query": 4, "unique_relevant_docs": 4201, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*