metadata
annotations_creators:
- human-annotated
language:
- slk
license: cc-by-nc-nd-4.0
multilinguality: monolingual
size_categories:
- n<1K
source_datasets:
- kinit/sk-factcheck-reranking
task_categories:
- text-ranking
task_ids: []
configs:
- config_name: corpus
data_files:
- split: test
path: corpus/test-*
- config_name: qrels
data_files:
- split: test
path: qrels/test-*
- config_name: queries
data_files:
- split: test
path: queries/test-*
- config_name: top_ranked
data_files:
- split: test
path: top_ranked/test-*
dataset_info:
- config_name: corpus
features:
- name: title
dtype: string
- name: text
dtype: string
- name: id
dtype: string
splits:
- name: test
num_bytes: 424819
num_examples: 1234
download_size: 155823
dataset_size: 424819
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 69407
num_examples: 1234
download_size: 11495
dataset_size: 69407
- config_name: queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 221650
num_examples: 232
download_size: 143941
dataset_size: 221650
- config_name: top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
sequence: string
splits:
- name: test
num_bytes: 43909
num_examples: 232
download_size: 10072
dataset_size: 43909
tags:
- mteb
- text
Created from Slovak part of the MultiClaim v2 dataset and curated for reranking task. The full dataset consists of 435k claims fact-checked by professional fact-checkers and 89k social media posts containing these claims which were all published before April 2025.
| Task category | t2t |
| Domains | News, Social, Written |
| Reference | https://zenodo.org/records/15413169 |
Source datasets:
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("SlovakFactCheckReranking")
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 repository.
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{pikuliak-etal-2023-multilingual,
address = {Singapore},
author = {Pikuliak, Mat{\'u}{\v{s}} and
Srba, Ivan and
Moro, Robert and
Hromadka, Timo and
Smole{\v{n}}, Timotej and
Meli{\v{s}}ek, Martin and
Vykopal, Ivan and
Simko, Jakub and
Podrou{\v{z}}ek, Juraj and
Bielikova, Maria},
booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
doi = {10.18653/v1/2023.emnlp-main.1027},
editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
month = dec,
pages = {16477--16500},
publisher = {Association for Computational Linguistics},
title = {Multilingual Previously Fact-Checked Claim Retrieval},
url = {https://aclanthology.org/2023.emnlp-main.1027},
year = {2023},
}
@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, Loïc 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("SlovakFactCheckReranking")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 1466,
"number_of_characters": 544840,
"documents_text_statistics": {
"total_text_length": 346323,
"min_text_length": 32,
"average_text_length": 280.6507293354943,
"max_text_length": 1237,
"unique_texts": 745
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 198517,
"min_text_length": 56,
"average_text_length": 855.676724137931,
"max_text_length": 5099,
"unique_texts": 232
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 249,
"min_relevant_docs_per_query": 3,
"average_relevant_docs_per_query": 1.0732758620689655,
"max_relevant_docs_per_query": 7,
"unique_relevant_docs": 1234
},
"top_ranked_statistics": {
"num_top_ranked": 1234,
"min_top_ranked_per_query": 3,
"average_top_ranked_per_query": 5.318965517241379,
"max_top_ranked_per_query": 7
}
}
}
This dataset card was automatically generated using MTEB