--- annotations_creators: - derived language: - deu - eng - fra - ita - por - spa license: cc-by-4.0 multilinguality: multilingual source_datasets: - vidore/vidore_v3_energy task_categories: - visual-document-retrieval - image-to-text - text-to-image task_ids: [] dataset_info: - config_name: english-corpus features: - name: image dtype: image - name: id dtype: string splits: - name: test num_bytes: 1132292097 num_examples: 2225 download_size: 1123882844 dataset_size: 1132292097 - config_name: english-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 302985 num_examples: 6618 download_size: 30549 dataset_size: 302985 - config_name: english-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 45810 num_examples: 308 download_size: 21613 dataset_size: 45810 - config_name: french-corpus features: - name: image dtype: image - name: id dtype: string splits: - name: test num_bytes: 1132292097 num_examples: 2225 download_size: 1123882844 dataset_size: 1132292097 - config_name: french-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 302985 num_examples: 6618 download_size: 30549 dataset_size: 302985 - config_name: french-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 45810 num_examples: 308 download_size: 24042 dataset_size: 45810 - config_name: german-corpus features: - name: image dtype: image - name: id dtype: string splits: - name: test num_bytes: 1132292097 num_examples: 2225 download_size: 1123882844 dataset_size: 1132292097 - config_name: german-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 302985 num_examples: 6618 download_size: 30549 dataset_size: 302985 - config_name: german-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 45810 num_examples: 308 download_size: 24496 dataset_size: 45810 - config_name: italian-corpus features: - name: image dtype: image - name: id dtype: string splits: - name: test num_bytes: 1132292097 num_examples: 2225 download_size: 1123882844 dataset_size: 1132292097 - config_name: italian-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 302985 num_examples: 6618 download_size: 30549 dataset_size: 302985 - config_name: italian-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 45810 num_examples: 308 download_size: 23813 dataset_size: 45810 - config_name: portuguese-corpus features: - name: image dtype: image - name: id dtype: string splits: - name: test num_bytes: 1132292097 num_examples: 2225 download_size: 1123882844 dataset_size: 1132292097 - config_name: portuguese-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 302985 num_examples: 6618 download_size: 30549 dataset_size: 302985 - config_name: portuguese-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 45810 num_examples: 308 download_size: 23526 dataset_size: 45810 - config_name: spanish-corpus features: - name: image dtype: image - name: id dtype: string splits: - name: test num_bytes: 1132292097 num_examples: 2225 download_size: 1123882844 dataset_size: 1132292097 - config_name: spanish-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 302985 num_examples: 6618 download_size: 30549 dataset_size: 302985 - config_name: spanish-queries features: - name: language dtype: string - name: id dtype: string - name: text dtype: string splits: - name: test num_bytes: 45810 num_examples: 308 download_size: 23878 dataset_size: 45810 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 ---

Vidore3EnergyRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions. | | | |---------------|---------------------------------------------| | Task category | t2i | | Domains | Academic | | Reference | https://huggingface.co/blog/QuentinJG/introducing-vidore-v3 | Source datasets: - [vidore/vidore_v3_energy](https://huggingface.co/datasets/vidore/vidore_v3_energy) ## 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("Vidore3EnergyRetrieval") 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 @misc{mace2025vidorev3, author = {Macé, Quentin and Loison, Antonio and EDY, Antoine and Xing, Victor and Viaud, Gautier}, title = {ViDoRe V3: a comprehensive evaluation of retrieval for enterprise use-cases}, year = {2025}, month = {November}, day = {5}, publisher = {Hugging Face}, journal = {Hugging Face Blog}, howpublished = {\url{https://huggingface.co/blog/QuentinJG/introducing-vidore-v3}} } @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("Vidore3EnergyRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json {} ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*