Wissam42/SEA-VL-Crawling-T2I

SeaVLCrawlingT2IRetrieval

An MTEB dataset

Massive Text Embedding Benchmark

SEA-VL crawling is a large-scale Southeast Asia–focused image–caption collection (~1.27M web-crawled culturally relevant pairs). For MTEB evaluation we deterministically downsample to 2048 image–caption pairs via a seeded streaming shuffle (buffer=10000), using the first non-empty caption per image. Queries are captions; the corpus contains images (text→image retrieval).

Task category	Any2AnyRetrieval (text-to-image)
Domains	Web, Written
Reference	Crowdsource, Crawl, or Generate? Creating {SEA

Source datasets:

• SEACrowd/sea-vl_crawling

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("SeaVLCrawlingT2IRetrieval")
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.

@inproceedings{cahyawijaya2025seavl,
  title={Crowdsource, Crawl, or Generate? Creating {SEA}-{VL}, a Multicultural Vision-Language Dataset for Southeast Asia},
  author={Cahyawijaya, Samuel and Lovenia, Holy and Moniz, Joel Ruben Antony and Wong, Tack Hwa and Farhansyah, Mohammad Rifqi and Maung, Thant Thiri and Hudi, Frederikus and Anugraha, David and Habibi, Muhammad Ravi Shulthan and Qorib, Muhammad Reza and others},
  booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={18685--18717},
  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("SeaVLCrawlingT2IRetrieval")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 4096,
        "num_queries": 2048,
        "num_documents": 2048,
        "number_of_characters": 124745,
        "documents_text_statistics": null,
        "documents_image_statistics": {
            "min_image_width": 67,
            "average_image_width": 656.19287109375,
            "max_image_width": 5152,
            "min_image_height": 72,
            "average_image_height": 493.9140625,
            "max_image_height": 4272,
            "unique_images": 2048
        },
        "documents_audio_statistics": null,
        "documents_video_statistics": null,
        "queries_text_statistics": {
            "total_text_length": 124745,
            "min_text_length": 5,
            "average_text_length": 60.91064453125,
            "max_text_length": 1309,
            "unique_texts": 2047
        },
        "queries_image_statistics": null,
        "queries_audio_statistics": null,
        "queries_video_statistics": null,
        "relevant_docs_statistics": {
            "num_relevant_docs": 1810,
            "min_relevant_docs_per_query": 1,
            "average_relevant_docs_per_query": 1.0,
            "max_relevant_docs_per_query": 1,
            "unique_relevant_docs": 1810
        },
        "top_ranked_statistics": null
    }
}

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This dataset card was automatically generated using MTEB