--- annotations_creators: - expert-annotated language: - fas - rus - zho license: odc-by multilinguality: multilingual source_datasets: - mteb/neuclir-2023 task_categories: - text-retrieval task_ids: [] dataset_info: - config_name: fas-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 8275313314 num_examples: 2232016 download_size: 3816764641 dataset_size: 8275313314 - config_name: fas-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1466410 num_examples: 26662 download_size: 1016379 dataset_size: 1466410 - config_name: fas-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 9779 num_examples: 76 download_size: 7097 dataset_size: 9779 - config_name: rus-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 14911638472 num_examples: 4627543 download_size: 7487737882 dataset_size: 14911638472 - config_name: rus-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1409870 num_examples: 25634 download_size: 978928 dataset_size: 1409870 - config_name: rus-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 11204 num_examples: 76 download_size: 8043 dataset_size: 11204 - config_name: zho-corpus features: - name: id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: test num_bytes: 6534387728 num_examples: 3179209 download_size: 4519787754 dataset_size: 6534387728 - config_name: zho-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 1520090 num_examples: 27638 download_size: 1055802 dataset_size: 1520090 - config_name: zho-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5592 num_examples: 76 download_size: 5739 dataset_size: 5592 configs: - config_name: fas-corpus data_files: - split: test path: fas-corpus/test-* - config_name: fas-qrels data_files: - split: test path: fas-qrels/test-* - config_name: fas-queries data_files: - split: test path: fas-queries/test-* - config_name: rus-corpus data_files: - split: test path: rus-corpus/test-* - config_name: rus-qrels data_files: - split: test path: rus-qrels/test-* - config_name: rus-queries data_files: - split: test path: rus-queries/test-* - config_name: zho-corpus data_files: - split: test path: zho-corpus/test-* - config_name: zho-qrels data_files: - split: test path: zho-qrels/test-* - config_name: zho-queries data_files: - split: test path: zho-queries/test-* tags: - mteb - text ---

NeuCLIR2023Retrieval

An MTEB dataset

Massive Text Embedding Benchmark

The task involves identifying and retrieving the documents that are relevant to the queries. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | News, Written | | Reference | https://neuclir.github.io/ | Source datasets: - [mteb/neuclir-2023](https://huggingface.co/datasets/mteb/neuclir-2023) ## 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("NeuCLIR2023Retrieval") 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 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{lawrie2024overview, archiveprefix = {arXiv}, author = {Dawn Lawrie and Sean MacAvaney and James Mayfield and Paul McNamee and Douglas W. Oard and Luca Soldaini and Eugene Yang}, eprint = {2404.08071}, primaryclass = {cs.IR}, title = {Overview of the TREC 2023 NeuCLIR Track}, year = {2024}, } @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("NeuCLIR2023Retrieval") desc_stats = task.metadata.descriptive_stats ``` ```json {} ```

--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*