Add dataset card

README.md

@@ -1,4 +1,13 @@
 ---
+annotations_creators:
+- expert-annotated
+language:
+- eng
+license: other
+multilinguality: monolingual
+task_categories:
+- text-ranking
+task_ids: []
 dataset_info:
 - config_name: corpus
   features:
@@ -89,4 +98,149 @@ configs:
     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)*