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imagewidth (px) 246
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the man with pierced ears is wearing glasses and an orange hat
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the hat with pierced glasses is wearing man and an orange ears
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and man with ears is an glasses the pierced orange hat wearing
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man with the pierced ears is wearing glasses and orange an hat
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the man with pierced ears is an orange hat wearing glasses and
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a man with glasses is wearing a beer can crocheted hat
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a glasses with hat is wearing a beer can crocheted man
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a man crocheted glasses a can is beer wearing with hat
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man a with wearing glasses is can a beer crocheted hat
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crocheted hat glasses is wearing a man with a beer can
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a man with gauges and glasses is wearing a blitz hat
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a blitz with hat and glasses is wearing a man gauges
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and man gauges with glasses wearing is a blitz hat a
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a man with and glasses gauges is wearing a blitz hat
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blitz hat is wearing a a man with gauges and glasses
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a man in an orange hat starring at something
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a something in an orange man starring at hat
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a man an starring orange hat at in something
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in a man an hat orange starring something at
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starring at something an orange hat a man in
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a man wears an orange hat and glasses
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a glasses wears an orange man and hat
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a man and orange hat an glasses wears
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wears a man orange an hat glasses and
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a man wears and glasses an orange hat
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a black and white dog is running in a grassy garden surrounded by a white fence
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a white and grassy fence is running in a white garden surrounded by a black dog
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running black and white dog by a a in grassy garden surrounded is white fence a
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a and black white is dog running in a garden surrounded grassy a by white fence
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fence white dog is grassy garden surrounded by a white running in a a black and
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a boston terrier is running on lush green grass in front of a white fence
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a front fence is running on white green terrier in grass of a lush boston
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running boston terrier is a lush green grass a front on of white fence in
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terrier a boston running on is grass lush green front of in a white fence
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a black and white dog is running through the grass
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a black and white dog is running through the grass
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and a black is dog white running the through grass
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white dog is grass running through the a black and
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a dog runs on the green grass near a wooden fence
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a dog runs on the green grass near a wooden fence
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dog a runs on the green near grass a wooden fence
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a boston terrier is running in the grass
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a young female student performing a downward kick to break a board held by her karate instructor
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a female young kick performing a downward karate to break a board held by her student instructor
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girl about to kick a piece of wood in half while karate instructor holds it
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a girl in karate uniform breaking a stick with a front kick
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a girl breaking boards by using karate
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five snowmobile riders all wearing helmets and goggles line up in a snowy clearing in a forest in front of their snowmobiles they are all wearing black snow pants and
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five people wearing winter jackets and helmets stand in the snow, with snowmobiles in the background
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five people wearing winter clothing, helmets, and ski goggles stand outside in the snow
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two men sitting on the roof of a house while another one stands on a ladder
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a bride in a light pink dress poses for a picture with male relatives and is being photographed by a man in a cream shirt with white pants
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a man in light colored clothing photographs a group of men wearing dark suits and hats standing around a woman dressed in a strapless gown
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a photographer takes a picture of a group of one girl in a pink dress and 10 boys in suits and hats
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a man photographs a woman in a pink dress and a throng of men in suits
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man taking a photograph of a well dressed group of teens
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a group of 11 people in winter wear such as beanies, skiing jackets, gloves and backpacks are standing in snow paddles outside a house made of ice blocks while a
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a baseball player in red leaps into the air to avoid a player in blue, who is shortly behind him as he catches the ball, while other blue team members
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a boy in a red uniform is attempting to avoid getting out at home plate, while the catcher in the blue uniform is attempting to catch him
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a player baseball attempting to score a run before the player tags the catcher out
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a man wearing a hat and a white shirt is cleaning windows
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a man in a white shirt stands high up on scaffolding
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a man stands on boards on top of a huge ladder
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a man wearing a reflective vest sits on the sidewalk and holds up pamphlets with bicycles on the cover
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man in bright yellow vest displays bicycle safety information on street
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a man and woman talking are interrupted and a man covers his face
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a mother decides to take her child on a piggyback ride outside their apartment complex
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a baby boy in a blue and white striped shirt is sitting on his mother's shoulders
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a male volleyball player in red trunks tries to block the shot of the opposing player during a professional volleyball game
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a woman wearing a pink shirt and red apron stands in her restaurant holding food and surrounded by bagged breads, a microwave and the chalkboard menu up on the wall
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a girl in a polka dotted blue jean dress walks barefoot on a balance beam
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a man and woman wearing sunglasses sit halfway in the water
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a water and man wearing sunglasses sit halfway in the woman
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a man in woman the sunglasses halfway sit wearing water and
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a man and woman sunglasses wearing halfway in sit water the
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a man and sit halfway in woman wearing sunglasses the water
| |
a boy and a girl holding hands while sitting on a shore
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a boy and a hands holding shore while sitting on a girl
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holding boy a and girl sitting hands a while a shore on
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a boy and a girl holding hands sitting while on shore a
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hands while sitting on a shore a boy and a girl holding
| |
a couple with sunglasses are laying on the beach
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a couple with beach are laying on the sunglasses
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laying couple sunglasses a on with the beach are
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with a couple are laying sunglasses on the beach
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a couple with sunglasses are laying on the beach
| |
a blond holding hands with a guy in the sand
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a hands holding guy with a sand in the blond
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with blond holding hands a guy a in sand the
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blond holding a a with hands the in guy sand
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hands with a a blond holding sand guy in the
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a woman standing with 3 other people in a store with two tables, some shelves with coffee and tea for sale, and a refrigerated drink case
|
a sale standing with 3 other tea in a tables with two woman , some people with shelves and case for store , and a refrigerated drink coffee
|
in woman a standing other people with with store refrigerated , tables and for shelves , coffee a tea and sale two some a with drink case 3
|
a standing woman with other 3 a people in store two with tables some , shelves coffee with for tea and and , sale refrigerated a drink case
|
a woman standing tables , some case with 3 other and tea for store with two sale , and shelves with coffee people in a a refrigerated drink
| |
a woman in a gray sweater and black baseball cap is standing in line at a shop
|
a shop in a black sweater and gray woman line is standing in baseball at a cap
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at woman a and gray sweater a black baseball cap a in line standing is shop in
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woman in a a sweater gray baseball black and cap is standing in line at shop a
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a woman in cap is standing and black baseball in line at a gray sweater a shop
| |
a woman in a hat waits to be served at a store
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a waits in a store woman to be served at a hat
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a woman at in hat waits be a a served store to
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a woman in hat waits a served be to a store at
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to be served at a store a woman in a hat waits
| |
young people are standing in the store
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young store are standing in the people
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young people standing are the store in
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are young people the standing in store
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store standing in the young people are
| |
a woman in a cap at a coffee shop
|
a shop in a woman at a cap coffee
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a woman a cap in a coffee shop at
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a in woman cap at a a shop coffee
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a woman in a coffee shop a cap at
| |
the person has a striped shirt on and is holding on to a rope on a mountain
|
the rope has a striped mountain on and is holding on to a person on a shirt
|
and person holding the striped shirt on on on is to a rope a a mountain has
|
the has person shirt striped a and on is holding to on on a rope a mountain
|
the person has a mountain on and is a rope on a striped shirt holding on to
| |
a kid rock climbing against the backdrop of a green valley
|
a green rock climbing against the backdrop of a kid valley
|
the kid rock climbing a against backdrop a of green valley
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rock a kid the against climbing of backdrop a valley green
|
climbing against the backdrop of a green valley a kid rock
| |
the person in the striped shirt is mountain climbing
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the mountain in the striped shirt is climbing person
|
striped person in is shirt the mountain climbing the
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person the in shirt the striped mountain climbing is
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the person in is mountain climbing the striped shirt
| |
a woman in a striped shirt climbs up a mountain
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a mountain in a striped woman climbs up a shirt
|
climbs woman a in striped shirt up a mountain a
|
a woman in shirt a striped up climbs a mountain
|
a woman in mountain a striped shirt climbs up a
| |
a young man climbs a rocky hill
|
a young man climbs a rocky hill
|
climbs young man a rocky hill a
|
young man a rocky a climbs hill
|
a young man hill climbs a rocky
| |
two young attractive women in front of a store drop coins for street performers
|
two young attractive front in coins of a drop street women for store performers
|
young attractive women two front in for store drop coins of street performers a
|
attractive two young in front women a of store for coins drop street performers
|
of a store street performers women in front drop coins for two young attractive
| |
two women enjoy the entertainment of street performers
|
two performers enjoy the women of street entertainment
|
two women of entertainment the street performers enjoy
|
enjoy two women the of entertainment street performers
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the entertainment of two women enjoy street performers
| |
two men pretend to be statutes while women look on
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two women pretend to be statutes while men look on
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look men be to while statutes on women pretend two
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two pretend men be to statutes look while women on
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to be statutes while women look two men pretend on
| |
two young women stop and watch street performers
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two young performers stop and watch street women
|
and young women watch two stop street performers
|
young two women stop watch and performers street
|
stop and watch street performers two young women
| |
pedestrians interact with street artists
|
street interact with artists pedestrians
|
pedestrians interact street artists with
|
interact with pedestrians street artists
|
pedestrians interact with street artists
|
End of preview. Expand
in Data Studio
YAML Metadata
Warning:
The task_categories "image-captioning" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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:
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
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
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