Datasets:
metadata
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:
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download_size: 990151273
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- config_name: english-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
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dtype: string
- name: id
dtype: string
- name: text
dtype: string
splits:
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download_size: 20693
dataset_size: 42341
- config_name: french-corpus
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dtype: image
- name: id
dtype: string
- name: text
dtype: string
splits:
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- config_name: french-qrels
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dtype: string
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dtype: string
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- config_name: german-corpus
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- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 1002929502
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- name: query-id
dtype: string
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dtype: string
- name: score
dtype: int64
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- config_name: italian-corpus
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- name: text
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splits:
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- config_name: portuguese-corpus
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dtype: image
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splits:
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- config_name: spanish-corpus
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dtype: string
- name: text
dtype: string
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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: 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
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:
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("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.
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("Vidore3FinanceFrOCRRetrieval")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB