Datasets:

mteb
/

AROFlickrOrder

annotations_creators:
  - expert-annotated
language:
  - eng
license: mit
multilinguality: monolingual
source_datasets:
  - gowitheflow/ARO-Flickr-Order
task_categories:
  - other
  - image-to-text
  - text-to-image
  - image-captioning
task_ids:
  - image-captioning
dataset_info:
  features:
    - name: correct_caption
      dtype: string
    - name: hard_text_1
      dtype: string
    - name: hard_text_2
      dtype: string
    - name: hard_text_3
      dtype: string
    - name: hard_text_4
      dtype: string
    - name: images
      dtype: image
  splits:
    - name: test
      num_bytes: 1726033859
      num_examples: 5000
  download_size: 1664110825
  dataset_size: 1726033859
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
tags:
  - mteb
  - text
  - image

AROFlickrOrder

An MTEB dataset

Massive Text Embedding Benchmark

Compositionality Evaluation of images to their captions.Each capation has four hard negatives created by order permutations.


Task category	i2t
Domains	Encyclopaedic
Reference	https://openreview.net/forum?id=KRLUvxh8uaX

Source datasets:

gowitheflow/ARO-Flickr-Order

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("AROFlickrOrder")
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.


@inproceedings{yuksekgonul2023and,
  author = {Yuksekgonul, Mert and Bianchi, Federico and Kalluri, Pratyusha and Jurafsky, Dan and Zou, James},
  booktitle = {The Eleventh International Conference on Learning Representations},
  title = {When and why vision-language models behave like bags-of-words, and what to do about it?},
  year = {2023},
}


@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

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("AROFlickrOrder")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 5000,
        "text_statistics": {
            "total_text_length": 1559324,
            "min_text_length": 11,
            "average_text_length": 62.37296,
            "max_text_length": 185,
            "unique_texts": 23892
        },
        "image_statistics": {
            "min_image_width": 246,
            "average_image_width": 457.795,
            "max_image_width": 500,
            "min_image_height": 151,
            "average_image_height": 397.488,
            "max_image_height": 500,
            "unique_images": 1000
        }
    }
}

This dataset card was automatically generated using MTEB