Datasets:
metadata
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
- mteb/Vidore3ComputerScienceOCRRetrieval
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:
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num_examples: 1360
download_size: 521123571
dataset_size: 522620514
- config_name: english-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
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- config_name: english-queries
features:
- name: language
dtype: string
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
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download_size: 16744
dataset_size: 29204
- config_name: english-top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: test
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num_examples: 215
download_size: 208526
dataset_size: 209490
- 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: 521123571
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- 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: 25667
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- config_name: french-queries
features:
- name: language
dtype: string
- name: id
dtype: string
- name: text
dtype: string
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download_size: 19084
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features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
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- config_name: german-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
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num_examples: 1360
download_size: 521123571
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- config_name: german-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
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- config_name: german-queries
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- name: id
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- name: text
dtype: string
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- config_name: german-top_ranked
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- config_name: italian-corpus
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- name: id
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- name: text
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splits:
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- config_name: italian-qrels
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- name: corpus-id
dtype: string
- name: score
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- config_name: italian-queries
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- name: language
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- name: id
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- name: text
dtype: string
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- config_name: italian-top_ranked
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- name: query-id
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- 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: 521123571
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
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- config_name: portuguese-queries
features:
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dtype: string
- name: id
dtype: string
- name: text
dtype: string
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dataset_size: 33363
- config_name: portuguese-top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: test
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num_examples: 215
download_size: 208626
dataset_size: 209871
- 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: 521123571
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
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dataset_size: 284471
- config_name: spanish-queries
features:
- name: language
dtype: string
- name: id
dtype: string
- name: text
dtype: string
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- config_name: spanish-top_ranked
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- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
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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: english-top_ranked
data_files:
- split: test
path: english-top_ranked/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: french-top_ranked
data_files:
- split: test
path: french-top_ranked/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: german-top_ranked
data_files:
- split: test
path: german-top_ranked/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: italian-top_ranked
data_files:
- split: test
path: italian-top_ranked/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: portuguese-top_ranked
data_files:
- split: test
path: portuguese-top_ranked/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-*
- config_name: spanish-top_ranked
data_files:
- split: test
path: spanish-top_ranked/test-*
tags:
- mteb
- text
- image
This task is used to evaluate the reranking performance for visual document retrieval. The candidates for reranking are the top-50 pages retrieved by the BAAI/bge-m3 model. 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 version add the OCR'ed markdown to allow for comparison across image-text, image-only and text-only models.
| Task category | DocumentUnderstanding (text-to-image+text) |
| Domains | Engineering, Programming |
| Reference | ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios |
| Contributed by | Illuin Technology |
Source datasets:
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("Vidore3ComputerScienceBGEm3Reranking.v2")
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.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@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:
import mteb
task = mteb.get_task("Vidore3ComputerScienceBGEm3Reranking.v2")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB