--- 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/NanoFiQA2018 - LiquidAI/nanobeir-multilingual-extended task_categories: - text-retrieval task_ids: - sentiment-analysis - sentiment-scoring - sentiment-classification - hate-speech-detection dataset_info: - config_name: ar-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 6525044 num_examples: 4598 download_size: 3028906 dataset_size: 6525044 - config_name: ar-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: ar-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5495 num_examples: 50 download_size: 4805 dataset_size: 5495 - config_name: de-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4970197 num_examples: 4598 download_size: 2839064 dataset_size: 4970197 - config_name: de-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: de-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4372 num_examples: 50 download_size: 4804 dataset_size: 4372 - config_name: en-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4201493 num_examples: 4598 download_size: 2496898 dataset_size: 4201493 - config_name: en-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: en-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3531 num_examples: 50 download_size: 4199 dataset_size: 3531 - config_name: es-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4713017 num_examples: 4598 download_size: 2679359 dataset_size: 4713017 - config_name: es-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: es-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4226 num_examples: 50 download_size: 4617 dataset_size: 4226 - config_name: fr-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5127009 num_examples: 4598 download_size: 2833228 dataset_size: 5127009 - config_name: fr-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: fr-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4813 num_examples: 50 download_size: 4869 dataset_size: 4813 - config_name: it-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4718555 num_examples: 4598 download_size: 2699497 dataset_size: 4718555 - config_name: it-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: it-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4346 num_examples: 50 download_size: 4714 dataset_size: 4346 - config_name: ja-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5677326 num_examples: 4598 download_size: 3016170 dataset_size: 5677326 - config_name: ja-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: ja-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4775 num_examples: 50 download_size: 4770 dataset_size: 4775 - config_name: ko-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 5302620 num_examples: 4598 download_size: 2941932 dataset_size: 5302620 - config_name: ko-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: ko-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4200 num_examples: 50 download_size: 4719 dataset_size: 4200 - config_name: no-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4349723 num_examples: 4598 download_size: 2501270 dataset_size: 4349723 - config_name: no-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: no-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3918 num_examples: 50 download_size: 4299 dataset_size: 3918 - config_name: pt-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4679793 num_examples: 4598 download_size: 2665171 dataset_size: 4679793 - config_name: pt-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: pt-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4306 num_examples: 50 download_size: 4620 dataset_size: 4306 - config_name: sv-corpus features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 4511769 num_examples: 4598 download_size: 2559481 dataset_size: 4511769 - config_name: sv-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 3184 num_examples: 123 download_size: 3106 dataset_size: 3184 - config_name: sv-queries features: - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 3847 num_examples: 50 download_size: 4295 dataset_size: 3847 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 ---

MultilingualNanoFiQA2018Retrieval

An MTEB dataset
Massive Text Embedding Benchmark
NanoFiQA2018 is a smaller subset of the Financial Opinion Mining and Question Answering dataset. | | | |---------------|---------------------------------------------| | Task category | Retrieval (text-to-text) | | Domains | Academic, Social | | Reference | [Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)](https://huggingface.co/datasets/LiquidAI/nanobeir-multilingual-extended) | Source datasets: - [zeta-alpha-ai/NanoFiQA2018](https://huggingface.co/datasets/zeta-alpha-ai/NanoFiQA2018) - [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("MultilingualNanoFiQA2018Retrieval") 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 @inproceedings{thakur2021beir, author = {Nandan Thakur and Nils Reimers and Andreas R{"u}ckl'e and Abhishek Srivastava and Iryna Gurevych}, booktitle = {Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, title = {{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, url = {https://openreview.net/forum?id=wCu6T5xFjeJ}, 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("MultilingualNanoFiQA2018Retrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 51128, "num_queries": 550, "num_documents": 50578, "number_of_characters": 43926960, "documents_text_statistics": { "total_text_length": 43893422, "min_text_length": 0, "average_text_length": 867.8362529162877, "max_text_length": 57298, "unique_texts": 50311 }, "documents_image_statistics": null, "documents_audio_statistics": null, "documents_video_statistics": null, "queries_text_statistics": { "total_text_length": 33538, "min_text_length": 6, "average_text_length": 60.97818181818182, "max_text_length": 196, "unique_texts": 550 }, "queries_image_statistics": null, "queries_audio_statistics": null, "queries_video_statistics": null, "relevant_docs_statistics": { "num_relevant_docs": 1353, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 2.46, "max_relevant_docs_per_query": 15, "unique_relevant_docs": 1353 }, "top_ranked_statistics": null } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*