README.md

---
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: test
    num_bytes: 654910
    num_examples: 5277
  download_size: 486691
  dataset_size: 654910
- 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: test
    num_bytes: 222948506.0
    num_examples: 2362514
  download_size: 124721520
  dataset_size: 222948506.0
- 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: 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: 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
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">MindSmallReranking</h1>
  <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

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)
```

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
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
<details>
  <summary> Dataset Statistics</summary>

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
    }
}
```

</details>

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