Datasets:

mteb
/

sickr-sts

sentence1 stringlengths 15 151	sentence2 stringlengths 14 151	score float64 1 5
A group of kids is playing in a yard and an old man is standing in the background	A group of boys in a yard is playing and a man is standing in the background	4.5
A group of children is playing in the house and there is no man standing in the background	A group of kids is playing in a yard and an old man is standing in the background	3.2
The young boys are playing outdoors and the man is smiling nearby	The kids are playing outdoors near a man with a smile	4.7
The kids are playing outdoors near a man with a smile	A group of kids is playing in a yard and an old man is standing in the background	3.4
The young boys are playing outdoors and the man is smiling nearby	A group of kids is playing in a yard and an old man is standing in the background	3.7
Two dogs are fighting	Two dogs are wrestling and hugging	4
A brown dog is attacking another animal in front of the man in pants	Two dogs are fighting	3.5
A brown dog is attacking another animal in front of the man in pants	Two dogs are wrestling and hugging	3.2
Nobody is riding the bicycle on one wheel	A person in a black jacket is doing tricks on a motorbike	2.8
A person is riding the bicycle on one wheel	A man in a black jacket is doing tricks on a motorbike	3.7
A person on a black motorbike is doing tricks with a jacket	A person is riding the bicycle on one wheel	3.4
A man with a jersey is dunking the ball at a basketball game	The ball is being dunked by a man with a jersey at a basketball game	4.9
A man with a jersey is dunking the ball at a basketball game	A man who is playing dunks the basketball into the net and a crowd is in background	3.6
The player is dunking the basketball into the net and a crowd is in background	A man with a jersey is dunking the ball at a basketball game	3.8
Two people are kickboxing and spectators are not watching	Two people are kickboxing and spectators are watching	3.4
Two young women are sparring in a kickboxing fight	Two women are sparring in a kickboxing match	4.9
Two young women are not sparring in a kickboxing fight	Two women are sparring in a kickboxing match	3.9
Two people are kickboxing and spectators are watching	Two young women are not sparring in a kickboxing fight	3.415
Two women are sparring in a kickboxing match	Two people are kickboxing and spectators are not watching	3.7
Three boys are jumping in the leaves	Three kids are jumping in the leaves	4.4
Three kids are sitting in the leaves	Three kids are jumping in the leaves	3.8
Children in red shirts are playing in the leaves	Three kids are sitting in the leaves	3.5
Children in red shirts are playing in the leaves	Three kids are jumping in the leaves	4
Two angels are making snow on the lying children	Two children are lying in the snow and are making snow angels	2.9
Two children are lying in the snow and are drawing angels	Two people in snowsuits are lying in the snow and making snow angels	4.1
Two people in snowsuits are lying in the snow and making snow angels	Two angels are making snow on the lying children	2.5
Two children are lying in the snow and are making snow angels	Two people in snowsuits are lying in the snow and making snow angels	4.2
People wearing costumes are gathering in a forest and are looking in the same direction	Masked people are looking in the same direction in a forest	4.4
People wearing costumes are gathering in a forest and are looking in the same direction	People wearing costumes are scattering in a forest and are looking in different directions	3.2
People are looking at some costumes gathered in the vicinity of the forest	People wearing costumes are gathering in a forest and are looking in the same direction	3.635
A little girl is looking at a woman in costume	People wearing costumes are scattering in a forest and are looking in different directions	2.4
A little girl is looking at a woman in costume	People are looking at some costumes gathered in the vicinity of the forest	2.6
A young girl is looking at a woman in costume	People wearing costumes are gathering in a forest and are looking in the same direction	2.2
People wearing costumes are gathering in a forest and are looking in the same direction	The little girl is looking at a man in costume	3
People wearing costumes are gathering in a forest and are looking in the same direction	A little girl in costume looks like a woman	2
A lone biker is jumping in the air	A biker is jumping in the air, alone	5
There is no biker jumping in the air	A lone biker is jumping in the air	4.2
A man is jumping into an empty pool	A man is jumping into a full pool	3
A man is jumping into an empty pool	The man's jumper is in the empty pool	3.1
A lone biker is jumping in the air	A man is jumping into a full pool	1.7
The man's jumper is in the empty pool	A lone biker is jumping in the air	1.4
A lone biker is jumping in the air	A man is jumping into an empty pool	1.5
Four kids are doing backbends in the park	Four children are doing backbends in the park	4.8
Four children are doing backbends in the gym	Four children are doing backbends in the park	3.8
Four girls are doing backbends and playing in the garden	Four girls are doing backbends and playing outdoors	4.1
A man who is playing is running with the ball in his hands	A player is running with the ball	4.3
Two groups of people are playing football	A player is running with the ball	2.1
Two teams are competing in a baseball game	A player is running with the ball	3
A player is running with the ball	Two teams are competing in a football match	2.6
Five wooden stands are in front of each child's hut	Five children are standing in front of a wooden hut	3.2
Five kids are standing close together and one kid has a gun	Five kids are standing close together and none of the kids has a gun	3.7
Five kids are standing close together and none of the kids has a gun	Five children are standing in front of a wooden hut	2.6
Five children are standing in a wooden hut	Five kids are standing close together and one kid has a gun	2.7
Five wooden stands are in front of each child's hut	Five kids are standing close together and one kid has a gun	2.3
An old man is sitting in a field	A man is sitting in a field	4.4
A man is sitting in a field	A man is running in a field	2.6
A person is wearing a hat and is sitting on the grass	A person is sitting in a field and is wearing a hat	4.1
A person is sitting and wearing a grass hat	A person is wearing a hat and is sitting on the grass	3.4
A person is sitting and wearing a grass hat	A man is sitting in a field	3.3
A man is sitting in a field	A person is wearing a hat and is sitting on the grass	3.8
The current is being ridden by a group of friends in a raft	A group of friends are riding the current in a raft	4.9
A group of friends are riding the current in a raft	A group is not riding the current in a raft	3.7
The current is being ridden by a group of friends in a raft	This group of people is practicing water safety and wearing preservers	3.2
A group of friends are riding the current in a raft	This group of people is practicing water safety and wearing preservers	3.1
A deer is jumping over a fence	A deer isn't jumping over the fence	3.9
People are walking inside a building that has many murals on it	People are walking outside a building that has many murals on it	3.4
Several people are in front of a colorful building	Nobody is in front of the colorful building	3.5
People are walking outside a building that has many murals on it	Nobody is in front of the colorful building	3.6
People are walking outside the building that has several murals on it	Several people are in front of a colorful building	3.6
A family is watching a little boy who is hitting a baseball	A family is watching a little boy who is missing a baseball	3.9
A child is hitting a baseball	A family is watching a little boy who is missing a baseball	3.015
A child is missing a baseball	A family is watching a little boy who is hitting a baseball	2.7
A purple crowd of people is eating on various red lit restaurant tables	Various people are eating at red tables in a crowded restaurant with purple lights	3.3
A large group of Asian people is eating at a restaurant	Various customers are eating in a crowded restaurant with purple lights	2.9
A purple crowd of people is eating on various red lit restaurant tables	A large group of Asian people is eating at a restaurant	3.1
Various people are eating at red tables in a crowded restaurant with purple lights	A small group of people is waiting to eat in a restaurant	3.2
A motorcycle rider is standing up on the seat of a white motorcycle	No motorcycle rider is standing up on the seat of a motorcycle	3.8
Nobody is on a motorcycle and is standing on the seat	Someone is on a black and white motorcycle and is standing on the seat	3.7
A motorcyclist is riding a motorbike dangerously along a roadway	A motorcyclist is riding a motorbike along a roadway	4.6
There is no motorcyclist riding a motorbike along a roadway	A motorcyclist is riding a motorbike along a roadway	3.7
A man with a helmet painted red is riding a blue motorcycle down the road	A motorcyclist with a red helmet is riding a blue motorcycle down the road	4.8
A motorcyclist without a helmet is waiting on a blue motorcycle near the road	A motorcyclist is riding a motorbike along a roadway	3.3
Two dogs are playing by a tree	A dog is catching a stick in the air and another is watching	3.7
Two dogs are playing by a tree	There is no dog leaping in the air	2.7
Two dogs are playing by a tree	A dog is leaping high in the air and another is watching	3
A girl in white is dancing	The dancer is dancing in front of the sound equipment	4
A girl in white is dancing	The blond girl is dancing behind the sound equipment	3.3
A girl is wearing white clothes and is dancing	The blond girl is dancing in front of the sound equipment	3.5
The blond girl is dancing in front of the sound equipment	There is no girl in white dancing	3.3
Three Asian kids are dancing and a man is looking	Three Asian kids are dancing and there is no man looking	3.9
Three Asian kids are dancing and a man is looking	An Asian man is dancing and three kids are looking	3.7
The children of a family are playing and waiting	Three Asian kids are dancing and a serious man is looking	1.9
The children of a family are patiently playing and waiting	Three Asian kids are dancing and a man is looking	2.3
There are no children playing and waiting	Three Asian kids are dancing and a man is looking	1.6
A woman is wearing an Egyptian hat on her head	A woman is wearing an Egyptian headdress	4.3
A woman is wearing an Egyptian headdress	A woman is wearing an Indian headdress	4
The black woman is wearing glasses over the headdress	A woman is wearing an Egyptian headdress	3.6
A woman is wearing an Egyptian hat on her head	The woman is wearing glasses and a black headdress	2.5
A hiker is on top of the mountain and is dancing	There is no hiker dancing on top of the mountain	3.2
There is no man on a rock high above some trees standing in a strange position	A man is on a rock high above some trees and is standing in a strange position	4.3

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SICK-R

An MTEB dataset

Massive Text Embedding Benchmark

Semantic Textual Similarity SICK-R dataset


Task category	t2t
Domains	Web, Written
Reference	https://aclanthology.org/L14-1314/

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_tasks(["SICK-R"])
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 repitory.

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{marelli-etal-2014-sick,
  abstract = {Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.},
  address = {Reykjavik, Iceland},
  author = {Marelli, Marco  and
Menini, Stefano  and
Baroni, Marco  and
Bentivogli, Luisa  and
Bernardi, Raffaella  and
Zamparelli, Roberto},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
  editor = {Calzolari, Nicoletta  and
Choukri, Khalid  and
Declerck, Thierry  and
Loftsson, Hrafn  and
Maegaard, Bente  and
Mariani, Joseph  and
Moreno, Asuncion  and
Odijk, Jan  and
Piperidis, Stelios},
  month = may,
  pages = {216--223},
  publisher = {European Language Resources Association (ELRA)},
  title = {A {SICK} cure for the evaluation of compositional distributional semantic models},
  url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf},
  year = {2014},
}


@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, Lo{\"\i}c 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("SICK-R")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 9927,
        "number_of_characters": 915617,
        "unique_pairs": 9842,
        "min_sentence1_length": 15,
        "average_sentence1_len": 46.602196031026494,
        "max_sentence1_length": 151,
        "unique_sentence1": 5014,
        "min_sentence2_length": 14,
        "average_sentence2_len": 45.63281958295558,
        "max_sentence2_length": 151,
        "unique_sentence2": 4946,
        "min_score": 1.0,
        "avg_score": 3.5291492898156607,
        "max_score": 5.0
    }
}

This dataset card was automatically generated using MTEB

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Number of rows:

9,927