--- annotations_creators: - derived language: - ara - deu - eng - fra - ita - jpn - kor - nor - por - spa - swe license: cc-by-4.0 multilinguality: translated source_datasets: - zeta-alpha-ai/NanoClimateFEVER - LiquidAI/nanobeir-multilingual-extended task_categories: - text-retrieval task_ids: - fact-checking - fact-checking-retrieval dataset_info: - config_name: ar-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 8323319 num_examples: 3408 download_size: 3818305 dataset_size: 8323319 - config_name: ar-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: ar-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 11146 num_examples: 50 download_size: 7990 dataset_size: 11146 - config_name: de-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 6216090 num_examples: 3408 download_size: 3588621 dataset_size: 6216090 - config_name: de-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: de-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 8164 num_examples: 50 download_size: 7789 dataset_size: 8164 - config_name: en-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5617105 num_examples: 3408 download_size: 3250029 dataset_size: 5617105 - config_name: en-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: en-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 7044 num_examples: 50 download_size: 7240 dataset_size: 7044 - config_name: es-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 6253735 num_examples: 3408 download_size: 3523046 dataset_size: 6253735 - config_name: es-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: es-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 8491 num_examples: 50 download_size: 7602 dataset_size: 8491 - config_name: fr-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 6530540 num_examples: 3408 download_size: 3587017 dataset_size: 6530540 - config_name: fr-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: fr-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 8793 num_examples: 50 download_size: 8015 dataset_size: 8793 - config_name: it-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 6073753 num_examples: 3408 download_size: 3514694 dataset_size: 6073753 - config_name: it-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: it-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 8269 num_examples: 50 download_size: 7648 dataset_size: 8269 - config_name: ja-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 6537653 num_examples: 3408 download_size: 3688750 dataset_size: 6537653 - config_name: ja-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: ja-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 8888 num_examples: 50 download_size: 7446 dataset_size: 8888 - config_name: ko-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 6185771 num_examples: 3408 download_size: 3620871 dataset_size: 6185771 - config_name: ko-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: ko-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 8480 num_examples: 50 download_size: 7859 dataset_size: 8480 - config_name: no-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5382119 num_examples: 3408 download_size: 3154387 dataset_size: 5382119 - config_name: no-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: no-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 6978 num_examples: 50 download_size: 6839 dataset_size: 6978 - config_name: pt-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 6000608 num_examples: 3408 download_size: 3456990 dataset_size: 6000608 - config_name: pt-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: pt-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 8190 num_examples: 50 download_size: 7866 dataset_size: 8190 - config_name: sv-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5529258 num_examples: 3408 download_size: 3241528 dataset_size: 5529258 - config_name: sv-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 5545 num_examples: 148 download_size: 4206 dataset_size: 5545 - config_name: sv-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 7492 num_examples: 50 download_size: 6991 dataset_size: 7492 configs: - config_name: ar-corpus data_files: - split: test path: ar-corpus/test-* - config_name: ar-qrels data_files: - split: test path: ar-qrels/test-* - config_name: ar-queries data_files: - split: test path: ar-queries/test-* - config_name: de-corpus data_files: - split: test path: de-corpus/test-* - config_name: de-qrels data_files: - split: test path: de-qrels/test-* - config_name: de-queries data_files: - split: test path: de-queries/test-* - config_name: en-corpus data_files: - split: test path: en-corpus/test-* - config_name: en-qrels data_files: - split: test path: en-qrels/test-* - config_name: en-queries data_files: - split: test path: en-queries/test-* - config_name: es-corpus data_files: - split: test path: es-corpus/test-* - config_name: es-qrels data_files: - split: test path: es-qrels/test-* - config_name: es-queries data_files: - split: test path: es-queries/test-* - config_name: fr-corpus data_files: - split: test path: fr-corpus/test-* - config_name: fr-qrels data_files: - split: test path: fr-qrels/test-* - config_name: fr-queries data_files: - split: test path: fr-queries/test-* - config_name: it-corpus data_files: - split: test path: it-corpus/test-* - config_name: it-qrels data_files: - split: test path: it-qrels/test-* - config_name: it-queries data_files: - split: test path: it-queries/test-* - config_name: ja-corpus data_files: - split: test path: ja-corpus/test-* - config_name: ja-qrels data_files: - split: test path: ja-qrels/test-* - config_name: ja-queries data_files: - split: test path: ja-queries/test-* - config_name: ko-corpus data_files: - split: test path: ko-corpus/test-* - config_name: ko-qrels data_files: - split: test path: ko-qrels/test-* - config_name: ko-queries data_files: - split: test path: ko-queries/test-* - config_name: no-corpus data_files: - split: test path: no-corpus/test-* - config_name: no-qrels data_files: - split: test path: no-qrels/test-* - config_name: no-queries data_files: - split: test path: no-queries/test-* - config_name: pt-corpus data_files: - split: test path: pt-corpus/test-* - config_name: pt-qrels data_files: - split: test path: pt-qrels/test-* - config_name: pt-queries data_files: - split: test path: pt-queries/test-* - config_name: sv-corpus data_files: - split: test path: sv-corpus/test-* - config_name: sv-qrels data_files: - split: test path: sv-qrels/test-* - config_name: sv-queries data_files: - split: test path: sv-queries/test-* tags: - mteb - text ---

MultilingualNanoClimateFeverRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
NanoClimateFever is a small version of the BEIR dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change. | | | |---------------|---------------------------------------------| | Task category | Retrieval (text-to-text) | | Domains | Non-fiction, Academic, News | | Reference | [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://huggingface.co/datasets/LiquidAI/nanobeir-multilingual-extended) | Source datasets: - [zeta-alpha-ai/NanoClimateFEVER](https://huggingface.co/datasets/zeta-alpha-ai/NanoClimateFEVER) - [LiquidAI/nanobeir-multilingual-extended](https://huggingface.co/datasets/LiquidAI/nanobeir-multilingual-extended) ## 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_task("MultilingualNanoClimateFeverRetrieval") model = mteb.get_model(YOUR_MODEL) mteb.evaluate(model, task) ``` To learn more about how to run models on `mteb` task check out the [GitHub repository](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 @misc{diggelmann2021climatefever, archiveprefix = {arXiv}, author = {Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold}, eprint = {2012.00614}, primaryclass = {cs.CL}, title = {CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims}, year = {2021}, } @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: ```python import mteb task = mteb.get_task("MultilingualNanoClimateFeverRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 38038, "num_queries": 550, "num_documents": 37488, "number_of_characters": 55485797, "documents_text_statistics": { "total_text_length": 55416383, "min_text_length": 11, "average_text_length": 1478.2432511737088, "max_text_length": 35086, "unique_texts": 37488 }, "documents_image_statistics": null, "documents_audio_statistics": null, "documents_video_statistics": null, "queries_text_statistics": { "total_text_length": 69414, "min_text_length": 19, "average_text_length": 126.20727272727272, "max_text_length": 337, "unique_texts": 550 }, "queries_image_statistics": null, "queries_audio_statistics": null, "queries_video_statistics": null, "relevant_docs_statistics": { "num_relevant_docs": 1628, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 2.96, "max_relevant_docs_per_query": 5, "unique_relevant_docs": 1265 }, "top_ranked_statistics": null } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*