--- annotations_creators: - derived language: - deu - eng - fra - ita - por - spa license: cc-by-4.0 multilinguality: translated source_datasets: - vidore/vidore_v3_finance_fr_mteb_format - vidore/vidore_v3_finance_fr_mteb_format task_categories: - visual-document-retrieval - image-to-text - text-to-image - image-text-to-text task_ids: [] dataset_info: - config_name: english-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 1002929502 num_examples: 2384 download_size: 990151273 dataset_size: 1002929502 - config_name: english-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 404037 num_examples: 8808 download_size: 34610 dataset_size: 404037 - config_name: english-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 42341 num_examples: 320 download_size: 20693 dataset_size: 42341 - config_name: french-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 1002929502 num_examples: 2384 download_size: 990151273 dataset_size: 1002929502 - config_name: french-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 404037 num_examples: 8808 download_size: 33758 dataset_size: 404037 - config_name: french-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 46385 num_examples: 320 download_size: 23146 dataset_size: 46385 - config_name: german-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 1002929502 num_examples: 2384 download_size: 990151273 dataset_size: 1002929502 - config_name: german-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 404037 num_examples: 8808 download_size: 35917 dataset_size: 404037 - config_name: german-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 48151 num_examples: 320 download_size: 23862 dataset_size: 48151 - config_name: italian-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 1002929502 num_examples: 2384 download_size: 990151273 dataset_size: 1002929502 - config_name: italian-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 404037 num_examples: 8808 download_size: 34359 dataset_size: 404037 - config_name: italian-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 45690 num_examples: 320 download_size: 22659 dataset_size: 45690 - config_name: portuguese-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 1002929502 num_examples: 2384 download_size: 990151273 dataset_size: 1002929502 - config_name: portuguese-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 404037 num_examples: 8808 download_size: 34178 dataset_size: 404037 - config_name: portuguese-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 46060 num_examples: 320 download_size: 22629 dataset_size: 46060 - config_name: spanish-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 1002929502 num_examples: 2384 download_size: 990151273 dataset_size: 1002929502 - config_name: spanish-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 404037 num_examples: 8808 download_size: 35371 dataset_size: 404037 - config_name: spanish-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 46395 num_examples: 320 download_size: 22863 dataset_size: 46395 configs: - config_name: english-corpus data_files: - split: test path: english-corpus/test-* - config_name: english-qrels data_files: - split: test path: english-qrels/test-* - config_name: english-queries data_files: - split: test path: english-queries/test-* - config_name: french-corpus data_files: - split: test path: french-corpus/test-* - config_name: french-qrels data_files: - split: test path: french-qrels/test-* - config_name: french-queries data_files: - split: test path: french-queries/test-* - config_name: german-corpus data_files: - split: test path: german-corpus/test-* - config_name: german-qrels data_files: - split: test path: german-qrels/test-* - config_name: german-queries data_files: - split: test path: german-queries/test-* - config_name: italian-corpus data_files: - split: test path: italian-corpus/test-* - config_name: italian-qrels data_files: - split: test path: italian-qrels/test-* - config_name: italian-queries data_files: - split: test path: italian-queries/test-* - config_name: portuguese-corpus data_files: - split: test path: portuguese-corpus/test-* - config_name: portuguese-qrels data_files: - split: test path: portuguese-qrels/test-* - config_name: portuguese-queries data_files: - split: test path: portuguese-queries/test-* - config_name: spanish-corpus data_files: - split: test path: spanish-corpus/test-* - config_name: spanish-qrels data_files: - split: test path: spanish-qrels/test-* - config_name: spanish-queries data_files: - split: test path: spanish-queries/test-* tags: - mteb - text - image ---

Vidore3FinanceFrOCRRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions. This task, Finance - FR, is a corpus of reports from french companies in the luxury domain, intended for long-document understanding tasks. Original queries were created in french, then translated to english, german, italian, portuguese and spanish. This variant includes the OCR'ed markdown so allow for comparison across image-text and text-only models. It is currently released as a beta and might be removed at a later stage. | | | |---------------|---------------------------------------------| | Task category | t2it | | Domains | Financial | | Reference | https://arxiv.org/abs/2601.08620 | Source datasets: - [vidore/vidore_v3_finance_fr_mteb_format](https://huggingface.co/datasets/vidore/vidore_v3_finance_fr_mteb_format) - [vidore/vidore_v3_finance_fr_mteb_format](https://huggingface.co/datasets/vidore/vidore_v3_finance_fr_mteb_format) ## 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("Vidore3FinanceFrOCRRetrieval") 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](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 @article{loison2026vidorev3comprehensiveevaluation, archiveprefix = {arXiv}, author = {António Loison and Quentin Macé and Antoine Edy and Victor Xing and Tom Balough and Gabriel Moreira and Bo Liu and Manuel Faysse and Céline Hudelot and Gautier Viaud}, eprint = {2601.08620}, primaryclass = {cs.AI}, title = {ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios}, url = {https://arxiv.org/abs/2601.08620}, year = {2026}, } @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("Vidore3FinanceFrOCRRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json {} ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*