metadata

annotations_creators:
  - human-annotated
language:
  - ara
  - deu
  - eng
  - fra
  - ita
  - kor
  - nld
  - spa
  - tur
license: unknown
multilinguality: multilingual
task_categories:
  - sentence-similarity
task_ids:
  - semantic-similarity-scoring
configs:
  - config_name: default
    data_files:
      - split: test
        path: test/*
  - config_name: ko-ko
    data_files:
      - split: test
        path: test/ko-ko.jsonl.gz
  - config_name: ar-ar
    data_files:
      - split: test
        path: test/ar-ar.jsonl.gz
  - config_name: en-ar
    data_files:
      - split: test
        path: test/en-ar.jsonl.gz
  - config_name: en-de
    data_files:
      - split: test
        path: test/en-de.jsonl.gz
  - config_name: en-en
    data_files:
      - split: test
        path: test/en-en.jsonl.gz
  - config_name: en-tr
    data_files:
      - split: test
        path: test/en-tr.jsonl.gz
  - config_name: es-en
    data_files:
      - split: test
        path: test/es-en.jsonl.gz
  - config_name: es-es
    data_files:
      - split: test
        path: test/es-es.jsonl.gz
  - config_name: fr-en
    data_files:
      - split: test
        path: test/fr-en.jsonl.gz
  - config_name: it-en
    data_files:
      - split: test
        path: test/it-en.jsonl.gz
  - config_name: nl-en
    data_files:
      - split: test
        path: test/nl-en.jsonl.gz
tags:
  - mteb
  - text

STS17

An MTEB dataset

Massive Text Embedding Benchmark

Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation


Task category	t2t
Domains	News, Web, Written
Reference	https://alt.qcri.org/semeval2017/task1/

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(["STS17"])
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{cer-etal-2017-semeval,
  abstract = {Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in \textit{all language tracks}. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the \textit{STS Benchmark} is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017).},
  address = {Vancouver, Canada},
  author = {Cer, Daniel  and
Diab, Mona  and
Agirre, Eneko  and
Lopez-Gazpio, I{\\~n}igo  and
Specia, Lucia},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)},
  doi = {10.18653/v1/S17-2001},
  editor = {Bethard, Steven  and
Carpuat, Marine  and
Apidianaki, Marianna  and
Mohammad, Saif M.  and
Cer, Daniel  and
Jurgens, David},
  month = aug,
  pages = {1--14},
  publisher = {Association for Computational Linguistics},
  title = {{S}em{E}val-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation},
  url = {https://aclanthology.org/S17-2001},
  year = {2017},
}


@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("STS17")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 5346,
        "number_of_characters": 400264,
        "min_sentence1_length": 6,
        "average_sentence1_len": 38.14665170220726,
        "max_sentence1_length": 976,
        "unique_sentence1": 4900,
        "min_sentence2_length": 6,
        "average_sentence2_len": 36.72502805836139,
        "max_sentence2_length": 1007,
        "unique_sentence2": 4470,
        "min_score": 0.0,
        "avg_score": 2.3554804214989464,
        "max_score": 5.0
    }
}

This dataset card was automatically generated using MTEB