--- annotations_creators: - expert-annotated language: - eng license: other multilinguality: monolingual task_categories: - text-ranking task_ids: [] dataset_info: - config_name: corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 2468752 num_examples: 19810 - name: test num_bytes: 654910 num_examples: 5277 download_size: 2323653 dataset_size: 3123662 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 17048656195 num_examples: 207455003 - name: test num_bytes: 7834166688 num_examples: 97006943 download_size: 1595227632 dataset_size: 24882822883 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: train num_bytes: 487986366 num_examples: 5107639 - name: test num_bytes: 222948506 num_examples: 2362514 download_size: 396128815 dataset_size: 710934872 - config_name: top_ranked features: - name: query-id dtype: string - name: corpus-ids sequence: string splits: - name: train num_bytes: 10729389945 num_examples: 5107639 - name: test num_bytes: 5013381576 num_examples: 2362514 download_size: 302864452 dataset_size: 15742771521 configs: - config_name: corpus data_files: - split: train path: corpus/train-* - split: test path: corpus/test-* - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - config_name: queries data_files: - split: train path: queries/train-* - split: test path: queries/test-* - config_name: top_ranked data_files: - split: train path: top_ranked/train-* - split: test path: top_ranked/test-* tags: - mteb - text ---

MindSmallReranking

An MTEB dataset
Massive Text Embedding Benchmark
Microsoft News Dataset: A Large-Scale English Dataset for News Recommendation Research | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | News, Written | | Reference | https://msnews.github.io/assets/doc/ACL2020_MIND.pdf | ## 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(["MindSmallReranking"]) 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 @inproceedings{wu-etal-2020-mind, abstract = {News recommendation is an important technique for personalized news service. Compared with product and movie recommendations which have been comprehensively studied, the research on news recommendation is much more limited, mainly due to the lack of a high-quality benchmark dataset. In this paper, we present a large-scale dataset named MIND for news recommendation. Constructed from the user click logs of Microsoft News, MIND contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. We demonstrate MIND a good testbed for news recommendation through a comparative study of several state-of-the-art news recommendation methods which are originally developed on different proprietary datasets. Our results show the performance of news recommendation highly relies on the quality of news content understanding and user interest modeling. Many natural language processing techniques such as effective text representation methods and pre-trained language models can effectively improve the performance of news recommendation. The MIND dataset will be available at https://msnews.github.io.}, address = {Online}, author = {Wu, Fangzhao and Qiao, Ying and Chen, Jiun-Hung and Wu, Chuhan and Qi, Tao and Lian, Jianxun and Liu, Danyang and Xie, Xing and Gao, Jianfeng and Wu, Winnie and Zhou, Ming}, booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, doi = {10.18653/v1/2020.acl-main.331}, editor = {Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel}, month = jul, pages = {3597--3606}, publisher = {Association for Computational Linguistics}, title = {{MIND}: A Large-scale Dataset for News Recommendation}, url = {https://aclanthology.org/2020.acl-main.331}, year = {2020}, } @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("MindSmallReranking") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 2367791, "number_of_characters": 162620316, "num_documents": 5277, "min_document_length": 11, "average_document_length": 65.06348303960584, "max_document_length": 176, "unique_documents": 5277, "num_queries": 2362514, "min_query_length": 11, "average_query_length": 68.68826004840606, "max_query_length": 251, "unique_queries": 2362514, "none_queries": 0, "num_relevant_docs": 97006943, "min_relevant_docs_per_query": 2, "average_relevant_docs_per_query": 1.8289660928993436, "max_relevant_docs_per_query": 295, "unique_relevant_docs": 5277, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, "num_top_ranked": 2362514, "min_top_ranked_per_query": 2, "average_top_ranked_per_query": 41.06168556038187, "max_top_ranked_per_query": 295 } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*