Add dataset card

README.md

@@ -1,4 +1,14 @@
 ---
+annotations_creators:
+- derived
+language:
+- pol
+license: gpl-3.0
+multilinguality: monolingual
+task_categories:
+- text-classification
+task_ids:
+- topic-classification
 dataset_info:
   features:
   - name: sentences
@@ -16,4 +26,161 @@ configs:
   data_files:
   - split: test
     path: data/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;">EightTagsClustering</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>
+
+Clustering of headlines from social media posts in Polish belonging to 8 categories: film, history, food, medicine, motorization, work, sport and technology.
+
+|               |                                             |
+|---------------|---------------------------------------------|
+| Task category | t2c                              |
+| Domains       | Social, Written                               |
+| Reference     | https://aclanthology.org/2020.lrec-1.207.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(["EightTagsClustering"])
+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{dadas-etal-2020-evaluation,
+  abstract = {Methods for learning sentence representations have been actively developed in recent years. However, the lack of pre-trained models and datasets annotated at the sentence level has been a problem for low-resource languages such as Polish which led to less interest in applying these methods to language-specific tasks. In this study, we introduce two new Polish datasets for evaluating sentence embeddings and provide a comprehensive evaluation of eight sentence representation methods including Polish and multilingual models. We consider classic word embedding models, recently developed contextual embeddings and multilingual sentence encoders, showing strengths and weaknesses of specific approaches. We also examine different methods of aggregating word vectors into a single sentence vector.},
+  address = {Marseille, France},
+  author = {Dadas, Slawomir  and
+Pere{\\l}kiewicz, Micha{\\l}  and
+Po{\\'s}wiata, Rafa{\\l}},
+  booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference},
+  editor = {Calzolari, Nicoletta  and
+B{\'e}chet, Fr{\'e}d{\'e}ric  and
+Blache, Philippe  and
+Choukri, Khalid  and
+Cieri, Christopher  and
+Declerck, Thierry  and
+Goggi, Sara  and
+Isahara, Hitoshi  and
+Maegaard, Bente  and
+Mariani, Joseph  and
+Mazo, H{\\'e}l{\\`e}ne  and
+Moreno, Asuncion  and
+Odijk, Jan  and
+Piperidis, Stelios},
+  isbn = {979-10-95546-34-4},
+  language = {English},
+  month = may,
+  pages = {1674--1680},
+  publisher = {European Language Resources Association},
+  title = {Evaluation of Sentence Representations in {P}olish},
+  url = {https://aclanthology.org/2020.lrec-1.207},
+  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("EightTagsClustering")
+
+desc_stats = task.metadata.descriptive_stats
+```
+
+```json
+{
+    "test": {
+        "num_samples": 10,
+        "number_of_characters": 49373,
+        "min_text_length": 4373,
+        "average_text_length": 4937.3,
+        "max_text_length": 5000,
+        "unique_texts": 49373,
+        "min_labels_per_text": 4861,
+        "average_labels_per_text": 4937.3,
+        "max_labels_per_text": 7172,
+        "unique_labels": 8,
+        "labels": {
+            "0": {
+                "count": 6260
+            },
+            "7": {
+                "count": 6412
+            },
+            "5": {
+                "count": 6167
+            },
+            "2": {
+                "count": 7172
+            },
+            "4": {
+                "count": 4861
+            },
+            "1": {
+                "count": 5869
+            },
+            "3": {
+                "count": 6270
+            },
+            "6": {
+                "count": 6362
+            }
+        }
+    }
+}
+```
+
+</details>
+
+---
+*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*