metadata
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
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:
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.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@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:
import mteb
task = mteb.get_task("MindSmallReranking")
desc_stats = task.metadata.descriptive_stats
{
"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