--- annotations_creators: - derived language: - deu - eng - fra - ita - por - spa license: cc-by-4.0 multilinguality: translated source_datasets: - vidore/vidore_v3_pharmaceuticals_mteb_format - vidore/vidore_v3_pharmaceuticals_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: 760636114 num_examples: 2313 download_size: 751325719 dataset_size: 760636114 - config_name: english-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 478992 num_examples: 10392 download_size: 40056 dataset_size: 478992 - config_name: english-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 51011 num_examples: 364 download_size: 26814 dataset_size: 51011 - config_name: french-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 760636114 num_examples: 2313 download_size: 751325719 dataset_size: 760636114 - config_name: french-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 478992 num_examples: 10392 download_size: 38445 dataset_size: 478992 - config_name: french-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 62915 num_examples: 364 download_size: 30802 dataset_size: 62915 - config_name: german-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 760636114 num_examples: 2313 download_size: 751325719 dataset_size: 760636114 - config_name: german-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 478992 num_examples: 10392 download_size: 40044 dataset_size: 478992 - config_name: german-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 60383 num_examples: 364 download_size: 30667 dataset_size: 60383 - config_name: italian-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 760636114 num_examples: 2313 download_size: 751325719 dataset_size: 760636114 - config_name: italian-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 478992 num_examples: 10392 download_size: 40080 dataset_size: 478992 - config_name: italian-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 58778 num_examples: 364 download_size: 30299 dataset_size: 58778 - config_name: portuguese-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 760636114 num_examples: 2313 download_size: 751325719 dataset_size: 760636114 - config_name: portuguese-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 478992 num_examples: 10392 download_size: 40190 dataset_size: 478992 - config_name: portuguese-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 58833 num_examples: 364 download_size: 29372 dataset_size: 58833 - config_name: spanish-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 760636114 num_examples: 2313 download_size: 751325719 dataset_size: 760636114 - config_name: spanish-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 478992 num_examples: 10392 download_size: 40026 dataset_size: 478992 - config_name: spanish-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 60094 num_examples: 364 download_size: 30041 dataset_size: 60094 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 ---

Vidore3PharmaceuticalsOCRRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions. This dataset, Pharmaceutical, is a corpus of slides from the FDA, intended for long-document understanding tasks. Original queries were created in english, then translated to french, 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 | Medical | | Reference | https://arxiv.org/abs/2601.08620 | Source datasets: - [vidore/vidore_v3_pharmaceuticals_mteb_format](https://huggingface.co/datasets/vidore/vidore_v3_pharmaceuticals_mteb_format) - [vidore/vidore_v3_pharmaceuticals_mteb_format](https://huggingface.co/datasets/vidore/vidore_v3_pharmaceuticals_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("Vidore3PharmaceuticalsOCRRetrieval") 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("Vidore3PharmaceuticalsOCRRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json {} ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*