Datasets:
metadata
annotations_creators:
- derived
language:
- ara
- deu
- eng
- fra
- hin
- hun
- jpn
- rus
- spa
- zho
license: unknown
multilinguality: multilingual
source_datasets:
- jinaai/tweet-stock-synthetic-retrieval_beir
task_categories:
- visual-document-retrieval
- image-to-text
- text-to-image
task_ids: []
dataset_info:
- config_name: ar-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 44518011
num_examples: 1000
download_size: 44149485
dataset_size: 44518011
- config_name: ar-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 29548
num_examples: 661
download_size: 9980
dataset_size: 29548
- config_name: ar-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 88820
num_examples: 642
download_size: 19207
dataset_size: 88820
- config_name: de-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 46502107
num_examples: 1000
download_size: 46176938
dataset_size: 46502107
- config_name: de-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 29548
num_examples: 661
download_size: 9980
dataset_size: 29548
- config_name: de-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 78305
num_examples: 642
download_size: 17328
dataset_size: 78305
- config_name: en-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 46408268
num_examples: 1000
download_size: 46066770
dataset_size: 46408268
- config_name: en-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 29548
num_examples: 661
download_size: 9980
dataset_size: 29548
- config_name: en-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 72946
num_examples: 642
download_size: 16971
dataset_size: 72946
- config_name: es-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 48172507
num_examples: 1000
download_size: 47881943
dataset_size: 48172507
- config_name: es-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 29548
num_examples: 661
download_size: 9980
dataset_size: 29548
- config_name: es-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 81262
num_examples: 642
download_size: 17120
dataset_size: 81262
- config_name: fr-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 48797138
num_examples: 1000
download_size: 48479743
dataset_size: 48797138
- config_name: fr-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 29548
num_examples: 661
download_size: 9980
dataset_size: 29548
- config_name: fr-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 84174
num_examples: 642
download_size: 19026
dataset_size: 84174
- config_name: hi-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 33752902
num_examples: 1000
download_size: 33666946
dataset_size: 33752902
- config_name: hi-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 22295
num_examples: 499
download_size: 7936
dataset_size: 22295
- config_name: hi-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 107951
num_examples: 499
download_size: 16401
dataset_size: 107951
- config_name: hu-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 46777585
num_examples: 1000
download_size: 46450473
dataset_size: 46777585
- config_name: hu-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 29548
num_examples: 661
download_size: 9980
dataset_size: 29548
- config_name: hu-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 74945
num_examples: 642
download_size: 17992
dataset_size: 74945
- config_name: jp-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 48400235
num_examples: 1000
download_size: 48095759
dataset_size: 48400235
- config_name: jp-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 29548
num_examples: 661
download_size: 9980
dataset_size: 29548
- config_name: jp-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 85810
num_examples: 642
download_size: 18581
dataset_size: 85810
- config_name: ru-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 47198710
num_examples: 1000
download_size: 46884037
dataset_size: 47198710
- config_name: ru-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 29548
num_examples: 661
download_size: 9980
dataset_size: 29548
- config_name: ru-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 98699
num_examples: 642
download_size: 19575
dataset_size: 98699
- config_name: zh-corpus
features:
- name: image
dtype: image
- name: id
dtype: string
- name: text
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 45706167
num_examples: 1000
download_size: 45336577
dataset_size: 45706167
- config_name: zh-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 29548
num_examples: 661
download_size: 9980
dataset_size: 29548
- config_name: zh-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: 'null'
- name: modality
dtype: string
splits:
- name: test
num_bytes: 64449
num_examples: 642
download_size: 17096
dataset_size: 64449
configs:
- config_name: ar-corpus
data_files:
- split: test
path: ar-corpus/test-*
- config_name: ar-qrels
data_files:
- split: test
path: ar-qrels/test-*
- config_name: ar-queries
data_files:
- split: test
path: ar-queries/test-*
- config_name: de-corpus
data_files:
- split: test
path: de-corpus/test-*
- config_name: de-qrels
data_files:
- split: test
path: de-qrels/test-*
- config_name: de-queries
data_files:
- split: test
path: de-queries/test-*
- config_name: en-corpus
data_files:
- split: test
path: en-corpus/test-*
- config_name: en-qrels
data_files:
- split: test
path: en-qrels/test-*
- config_name: en-queries
data_files:
- split: test
path: en-queries/test-*
- config_name: es-corpus
data_files:
- split: test
path: es-corpus/test-*
- config_name: es-qrels
data_files:
- split: test
path: es-qrels/test-*
- config_name: es-queries
data_files:
- split: test
path: es-queries/test-*
- config_name: fr-corpus
data_files:
- split: test
path: fr-corpus/test-*
- config_name: fr-qrels
data_files:
- split: test
path: fr-qrels/test-*
- config_name: fr-queries
data_files:
- split: test
path: fr-queries/test-*
- config_name: hi-corpus
data_files:
- split: test
path: hi-corpus/test-*
- config_name: hi-qrels
data_files:
- split: test
path: hi-qrels/test-*
- config_name: hi-queries
data_files:
- split: test
path: hi-queries/test-*
- config_name: hu-corpus
data_files:
- split: test
path: hu-corpus/test-*
- config_name: hu-qrels
data_files:
- split: test
path: hu-qrels/test-*
- config_name: hu-queries
data_files:
- split: test
path: hu-queries/test-*
- config_name: jp-corpus
data_files:
- split: test
path: jp-corpus/test-*
- config_name: jp-qrels
data_files:
- split: test
path: jp-qrels/test-*
- config_name: jp-queries
data_files:
- split: test
path: jp-queries/test-*
- config_name: ru-corpus
data_files:
- split: test
path: ru-corpus/test-*
- config_name: ru-qrels
data_files:
- split: test
path: ru-qrels/test-*
- config_name: ru-queries
data_files:
- split: test
path: ru-queries/test-*
- config_name: zh-corpus
data_files:
- split: test
path: zh-corpus/test-*
- config_name: zh-qrels
data_files:
- split: test
path: zh-qrels/test-*
- config_name: zh-queries
data_files:
- split: test
path: zh-queries/test-*
tags:
- mteb
- text
- image
Retrieve rendered tables of stock prices based on templated queries.
| Task category | t2i |
| Domains | Social |
| Reference | https://huggingface.co/datasets/jinaai/tweet-stock-synthetic-retrieval_beir |
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("JinaVDRTweetStockSyntheticsRetrieval")
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.
@misc{günther2025jinaembeddingsv4universalembeddingsmultimodal,
archiveprefix = {arXiv},
author = {Michael Günther and Saba Sturua and Mohammad Kalim Akram and Isabelle Mohr and Andrei Ungureanu and Bo Wang and Sedigheh Eslami and Scott Martens and Maximilian Werk and Nan Wang and Han Xiao},
eprint = {2506.18902},
primaryclass = {cs.AI},
title = {jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval},
url = {https://arxiv.org/abs/2506.18902},
year = {2025},
}
@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("JinaVDRTweetStockSyntheticsRetrieval")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB