--- annotations_creators: - derived language: - deu - eng - fra - ita - por - spa license: cc-by-4.0 multilinguality: translated source_datasets: - vidore/vidore_v3_computer_science_mteb_format - vidore/vidore_v3_computer_science_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: 522620514 num_examples: 1360 download_size: 512759028 dataset_size: 522620514 - config_name: english-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 284471 num_examples: 6294 download_size: 25288 dataset_size: 284471 - config_name: english-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 29204 num_examples: 215 download_size: 16744 dataset_size: 29204 - config_name: french-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 522620514 num_examples: 1360 download_size: 512759028 dataset_size: 522620514 - config_name: french-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 284471 num_examples: 6294 download_size: 26337 dataset_size: 284471 - config_name: french-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 35650 num_examples: 215 download_size: 19084 dataset_size: 35650 - config_name: german-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 522620514 num_examples: 1360 download_size: 512759028 dataset_size: 522620514 - config_name: german-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 284471 num_examples: 6294 download_size: 24113 dataset_size: 284471 - config_name: german-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 33415 num_examples: 215 download_size: 19032 dataset_size: 33415 - config_name: italian-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 522620514 num_examples: 1360 download_size: 512759028 dataset_size: 522620514 - config_name: italian-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 284471 num_examples: 6294 download_size: 24343 dataset_size: 284471 - config_name: italian-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 33035 num_examples: 215 download_size: 18637 dataset_size: 33035 - config_name: portuguese-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 522620514 num_examples: 1360 download_size: 512759028 dataset_size: 522620514 - config_name: portuguese-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 284471 num_examples: 6294 download_size: 24451 dataset_size: 284471 - config_name: portuguese-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 33363 num_examples: 215 download_size: 18147 dataset_size: 33363 - config_name: spanish-corpus features: - name: image dtype: image - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 522620514 num_examples: 1360 download_size: 512759028 dataset_size: 522620514 - config_name: spanish-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 284471 num_examples: 6294 download_size: 24386 dataset_size: 284471 - config_name: spanish-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 33790 num_examples: 215 download_size: 18467 dataset_size: 33790 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 ---

Vidore3ComputerScienceOCRRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions. This dataset, Computer Science, is a corpus of textbooks from the openstacks website, 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 | Engineering, Programming | | Reference | https://arxiv.org/abs/2601.08620 | Source datasets: - [vidore/vidore_v3_computer_science_mteb_format](https://huggingface.co/datasets/vidore/vidore_v3_computer_science_mteb_format) - [vidore/vidore_v3_computer_science_mteb_format](https://huggingface.co/datasets/vidore/vidore_v3_computer_science_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("Vidore3ComputerScienceOCRRetrieval") 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("Vidore3ComputerScienceOCRRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json {} ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*