Clustering

• Number of tasks: 98

AlloProfClusteringP2P

Clustering of document titles and descriptions from Allo Prof dataset. Clustering of 10 sets on the document topic.

Dataset: lyon-nlp/alloprof • License: mit • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fra	Encyclopaedic, Written	human-annotated	found

Citation

@misc{lef23,
  author = {Lefebvre-Brossard, Antoine and Gazaille, Stephane and Desmarais, Michel C.},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International},
  doi = {10.48550/ARXIV.2302.07738},
  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  publisher = {arXiv},
  title = {Alloprof: a new French question-answer education dataset and its use in an information retrieval case study},
  url = {https://arxiv.org/abs/2302.07738},
  year = {2023},
}

AlloProfClusteringP2P.v2

Clustering of document titles and descriptions from Allo Prof dataset. Clustering of 10 sets on the document topic.

Dataset: lyon-nlp/alloprof • License: mit • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fra	Encyclopaedic, Written	human-annotated	found

Citation

@misc{lef23,
  author = {Lefebvre-Brossard, Antoine and Gazaille, Stephane and Desmarais, Michel C.},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International},
  doi = {10.48550/ARXIV.2302.07738},
  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  publisher = {arXiv},
  title = {Alloprof: a new French question-answer education dataset and its use in an information retrieval case study},
  url = {https://arxiv.org/abs/2302.07738},
  year = {2023},
}

AlloProfClusteringS2S

Clustering of document titles from Allo Prof dataset. Clustering of 10 sets on the document topic.

Dataset: lyon-nlp/alloprof • License: mit • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fra	Encyclopaedic, Written	human-annotated	found

Citation

@misc{lef23,
  author = {Lefebvre-Brossard, Antoine and Gazaille, Stephane and Desmarais, Michel C.},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International},
  doi = {10.48550/ARXIV.2302.07738},
  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  publisher = {arXiv},
  title = {Alloprof: a new French question-answer education dataset and its use in an information retrieval case study},
  url = {https://arxiv.org/abs/2302.07738},
  year = {2023},
}

AlloProfClusteringS2S.v2

Clustering of document titles from Allo Prof dataset. Clustering of 10 sets on the document topic.

Dataset: lyon-nlp/alloprof • License: mit • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fra	Encyclopaedic, Written	human-annotated	found

Citation

@misc{lef23,
  author = {Lefebvre-Brossard, Antoine and Gazaille, Stephane and Desmarais, Michel C.},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International},
  doi = {10.48550/ARXIV.2302.07738},
  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  publisher = {arXiv},
  title = {Alloprof: a new French question-answer education dataset and its use in an information retrieval case study},
  url = {https://arxiv.org/abs/2302.07738},
  year = {2023},
}

ArXivHierarchicalClusteringP2P

Clustering of titles+abstract from arxiv. Clustering of 30 sets, either on the main or secondary category

Dataset: mteb/arxiv-clustering-p2p • License: cc0-1.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Written	derived	found

ArXivHierarchicalClusteringS2S

Clustering of titles from arxiv. Clustering of 30 sets, either on the main or secondary category

Dataset: mteb/arxiv-clustering-s2s • License: cc0-1.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Written	derived	found

ArxivClusteringP2P

Clustering of titles+abstract from arxiv. Clustering of 30 sets, either on the main or secondary category

Dataset: mteb/arxiv-clustering-p2p • License: cc0-1.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Written	derived	found

Citation

@misc{arxiv_org_submitters_2024,
  author = {arXiv.org submitters},
  doi = {10.34740/KAGGLE/DSV/7548853},
  publisher = {Kaggle},
  title = {arXiv Dataset},
  url = {https://www.kaggle.com/dsv/7548853},
  year = {2024},
}

ArxivClusteringP2P.v2

Clustering of titles+abstract from arxiv. Clustering of 30 sets, either on the main or secondary category

Dataset: mteb/arxiv-clustering-p2p • License: cc0-1.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Written	derived	found

Citation

@misc{arxiv_org_submitters_2024,
  author = {arXiv.org submitters},
  doi = {10.34740/KAGGLE/DSV/7548853},
  publisher = {Kaggle},
  title = {arXiv Dataset},
  url = {https://www.kaggle.com/dsv/7548853},
  year = {2024},
}

ArxivClusteringS2S

Clustering of titles from arxiv. Clustering of 30 sets, either on the main or secondary category

Dataset: mteb/arxiv-clustering-s2s • License: cc0-1.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Written	derived	found

Citation

@misc{arxiv_org_submitters_2024,
  author = {arXiv.org submitters},
  doi = {10.34740/KAGGLE/DSV/7548853},
  publisher = {Kaggle},
  title = {arXiv Dataset},
  url = {https://www.kaggle.com/dsv/7548853},
  year = {2024},
}

BeytooteClustering

Beytoote Website Articles Clustering

Dataset: MCINext/beytoote-clustering • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fas	News	derived	found

Citation

BigPatentClustering

Clustering of documents from the Big Patent dataset. Test set only includes documents belonging to a single category, with a total of 9 categories.

Dataset: jinaai/big-patent-clustering • License: cc-by-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Legal, Written	derived	found

Citation

@article{DBLP:journals/corr/abs-1906-03741,
  author = {Eva Sharma and
Chen Li and
Lu Wang},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
  eprint = {1906.03741},
  eprinttype = {arXiv},
  journal = {CoRR},
  timestamp = {Wed, 26 Jun 2019 07:14:58 +0200},
  title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent
Summarization},
  url = {http://arxiv.org/abs/1906.03741},
  volume = {abs/1906.03741},
  year = {2019},
}

BigPatentClustering.v2

Clustering of documents from the Big Patent dataset. Test set only includes documents belonging to a single category, with a total of 9 categories.

Dataset: mteb/big-patent • License: cc-by-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Legal, Written	derived	found

Citation

@article{DBLP:journals/corr/abs-1906-03741,
  author = {Eva Sharma and
Chen Li and
Lu Wang},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
  eprint = {1906.03741},
  eprinttype = {arXiv},
  journal = {CoRR},
  timestamp = {Wed, 26 Jun 2019 07:14:58 +0200},
  title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent
Summarization},
  url = {http://arxiv.org/abs/1906.03741},
  volume = {abs/1906.03741},
  year = {2019},
}

BiorxivClusteringP2P

Clustering of titles+abstract from biorxiv. Clustering of 10 sets, based on the main category.

Dataset: mteb/biorxiv-clustering-p2p • License: https://www.biorxiv.org/content/about-biorxiv • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Written	derived	created

BiorxivClusteringP2P.v2

Clustering of titles+abstract from biorxiv across 26 categories.

Dataset: mteb/biorxiv-clustering-p2p • License: https://www.biorxiv.org/content/about-biorxiv • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Written	derived	created

BiorxivClusteringS2S

Clustering of titles from biorxiv. Clustering of 10 sets, based on the main category.

Dataset: mteb/biorxiv-clustering-s2s • License: https://www.biorxiv.org/content/about-biorxiv • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Written	derived	created

BiorxivClusteringS2S.v2

Clustering of titles from biorxiv across 26 categories.

Dataset: mteb/biorxiv-clustering-s2s • License: https://www.biorxiv.org/content/about-biorxiv • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Written	derived	created

BlurbsClusteringP2P

Clustering of book titles+blurbs. Clustering of 28 sets, either on the main or secondary genre.

Dataset: slvnwhrl/blurbs-clustering-p2p • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu	Written	not specified	not specified

Citation

@inproceedings{Remus2019GermEval2T,
  author = {Steffen Remus and Rami Aly and Chris Biemann},
  booktitle = {Conference on Natural Language Processing},
  title = {GermEval 2019 Task 1: Hierarchical Classification of Blurbs},
  url = {https://api.semanticscholar.org/CorpusID:208334484},
  year = {2019},
}

BlurbsClusteringP2P.v2

Clustering of book titles+blurbs. Clustering of 28 sets, either on the main or secondary genre.

Dataset: slvnwhrl/blurbs-clustering-p2p • License: cc-by-nc-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu	Fiction, Written	derived	found

Citation

@inproceedings{Remus2019GermEval2T,
  author = {Steffen Remus and Rami Aly and Chris Biemann},
  booktitle = {Conference on Natural Language Processing},
  title = {GermEval 2019 Task 1: Hierarchical Classification of Blurbs},
  url = {https://api.semanticscholar.org/CorpusID:208334484},
  year = {2019},
}

BlurbsClusteringS2S

Clustering of book titles. Clustering of 28 sets, either on the main or secondary genre.

Dataset: slvnwhrl/blurbs-clustering-s2s • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu	Written	not specified	not specified

Citation

@inproceedings{Remus2019GermEval2T,
  author = {Steffen Remus and Rami Aly and Chris Biemann},
  booktitle = {Conference on Natural Language Processing},
  title = {GermEval 2019 Task 1: Hierarchical Classification of Blurbs},
  url = {https://api.semanticscholar.org/CorpusID:208334484},
  year = {2019},
}

BlurbsClusteringS2S.v2

Clustering of book titles. Clustering of 28 sets, either on the main or secondary genre.

Dataset: slvnwhrl/blurbs-clustering-s2s • License: cc-by-nc-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu	Fiction, Written	derived	found

Citation

@inproceedings{Remus2019GermEval2T,
  author = {Steffen Remus and Rami Aly and Chris Biemann},
  booktitle = {Conference on Natural Language Processing},
  title = {GermEval 2019 Task 1: Hierarchical Classification of Blurbs},
  url = {https://api.semanticscholar.org/CorpusID:208334484},
  year = {2019},
}

BuiltBenchClusteringP2P

Clustering of built asset item descriptions based on categories identified within industry classification systems such as IFC, Uniclass, etc.

Dataset: mehrzad-shahin/BuiltBench-clustering-p2p • License: cc-by-nd-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Engineering, Written	derived	created

Citation

@article{shahinmoghadam2024benchmarking,
  author = {Shahinmoghadam, Mehrzad and Motamedi, Ali},
  journal = {arXiv preprint arXiv:2411.12056},
  title = {Benchmarking pre-trained text embedding models in aligning built asset information},
  year = {2024},
}

BuiltBenchClusteringS2S

Clustering of built asset names/titles based on categories identified within industry classification systems such as IFC, Uniclass, etc.

Dataset: mehrzad-shahin/BuiltBench-clustering-s2s • License: cc-by-nd-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Engineering, Written	derived	created

Citation

@article{shahinmoghadam2024benchmarking,
  author = {Shahinmoghadam, Mehrzad and Motamedi, Ali},
  journal = {arXiv preprint arXiv:2411.12056},
  title = {Benchmarking pre-trained text embedding models in aligning built asset information},
  year = {2024},
}

CLSClusteringP2P

Clustering of titles + abstract from CLS dataset. Clustering of 13 sets on the main category.

Dataset: C-MTEB/CLSClusteringP2P • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	cmn	not specified	not specified	not specified

Citation

@article{li2022csl,
  author = {Li, Yudong and Zhang, Yuqing and Zhao, Zhe and Shen, Linlin and Liu, Weijie and Mao, Weiquan and Zhang, Hui},
  journal = {arXiv preprint arXiv:2209.05034},
  title = {CSL: A large-scale Chinese scientific literature dataset},
  year = {2022},
}

CLSClusteringP2P.v2

Clustering of titles + abstract from CLS dataset. Clustering of 13 sets on the main category.

Dataset: C-MTEB/CLSClusteringP2P • License: apache-2.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	cmn	Academic, Written	derived	found

Citation

@misc{li2022csl,
  archiveprefix = {arXiv},
  author = {Yudong Li and Yuqing Zhang and Zhe Zhao and Linlin Shen and Weijie Liu and Weiquan Mao and Hui Zhang},
  eprint = {2209.05034},
  primaryclass = {cs.CL},
  title = {CSL: A Large-scale Chinese Scientific Literature Dataset},
  year = {2022},
}

CLSClusteringS2S

Clustering of titles from CLS dataset. Clustering of 13 sets on the main category.

Dataset: C-MTEB/CLSClusteringS2S • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	cmn	not specified	not specified	not specified

Citation

@article{li2022csl,
  author = {Li, Yudong and Zhang, Yuqing and Zhao, Zhe and Shen, Linlin and Liu, Weijie and Mao, Weiquan and Zhang, Hui},
  journal = {arXiv preprint arXiv:2209.05034},
  title = {CSL: A large-scale Chinese scientific literature dataset},
  year = {2022},
}

CLSClusteringS2S.v2

Clustering of titles from CLS dataset. Clustering of 13 sets on the main category.

Dataset: C-MTEB/CLSClusteringS2S • License: apache-2.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	cmn	Academic, Written	derived	found

Citation

@misc{li2022csl,
  archiveprefix = {arXiv},
  author = {Yudong Li and Yuqing Zhang and Zhe Zhao and Linlin Shen and Weijie Liu and Weiquan Mao and Hui Zhang},
  eprint = {2209.05034},
  primaryclass = {cs.CL},
  title = {CSL: A Large-scale Chinese Scientific Literature Dataset},
  year = {2022},
}

ClusTREC-Covid

A Topical Clustering Benchmark for COVID-19 Scientific Research across 50 covid-19 related topics.

Dataset: Uri-ka/ClusTREC-Covid • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Medical, Written	expert-annotated	created

Citation

@inproceedings{katz-etal-2024-knowledge,
  address = {Miami, Florida, USA},
  author = {Katz, Uri  and
Levy, Mosh  and
Goldberg, Yoav},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
  month = nov,
  pages = {8838--8855},
  publisher = {Association for Computational Linguistics},
  title = {Knowledge Navigator: {LLM}-guided Browsing Framework for Exploratory Search in Scientific Literature},
  url = {https://aclanthology.org/2024.findings-emnlp.516},
  year = {2024},
}

DigikalamagClustering

A total of 8,515 articles scraped from Digikala Online Magazine. This dataset includes seven different classes.

Dataset: PNLPhub/DigiMag • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fas	Web	derived	found

Citation

EightTagsClustering

Clustering of headlines from social media posts in Polish belonging to 8 categories: film, history, food, medicine, motorization, work, sport and technology.

Dataset: PL-MTEB/8tags-clustering • License: gpl-3.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	pol	Social, Written	derived	found

Citation

@inproceedings{dadas-etal-2020-evaluation,
  abstract = {Methods for learning sentence representations have been actively developed in recent years. However, the lack of pre-trained models and datasets annotated at the sentence level has been a problem for low-resource languages such as Polish which led to less interest in applying these methods to language-specific tasks. In this study, we introduce two new Polish datasets for evaluating sentence embeddings and provide a comprehensive evaluation of eight sentence representation methods including Polish and multilingual models. We consider classic word embedding models, recently developed contextual embeddings and multilingual sentence encoders, showing strengths and weaknesses of specific approaches. We also examine different methods of aggregating word vectors into a single sentence vector.},
  address = {Marseille, France},
  author = {Dadas, Slawomir  and
Pere{\\l}kiewicz, Micha{\\l}  and
Po{\\'s}wiata, Rafa{\\l}},
  booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference},
  editor = {Calzolari, Nicoletta  and
B{\'e}chet, Fr{\'e}d{\'e}ric  and
Blache, Philippe  and
Choukri, Khalid  and
Cieri, Christopher  and
Declerck, Thierry  and
Goggi, Sara  and
Isahara, Hitoshi  and
Maegaard, Bente  and
Mariani, Joseph  and
Mazo, H{\\'e}l{\\`e}ne  and
Moreno, Asuncion  and
Odijk, Jan  and
Piperidis, Stelios},
  isbn = {979-10-95546-34-4},
  language = {English},
  month = may,
  pages = {1674--1680},
  publisher = {European Language Resources Association},
  title = {Evaluation of Sentence Representations in {P}olish},
  url = {https://aclanthology.org/2020.lrec-1.207},
  year = {2020},
}

EightTagsClustering.v2

Clustering of headlines from social media posts in Polish belonging to 8 categories: film, history, food, medicine, motorization, work, sport and technology.

Dataset: PL-MTEB/8tags-clustering • License: gpl-3.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	pol	Social, Written	derived	found

Citation

@inproceedings{dadas-etal-2020-evaluation,
  abstract = {Methods for learning sentence representations have been actively developed in recent years. However, the lack of pre-trained models and datasets annotated at the sentence level has been a problem for low-resource languages such as Polish which led to less interest in applying these methods to language-specific tasks. In this study, we introduce two new Polish datasets for evaluating sentence embeddings and provide a comprehensive evaluation of eight sentence representation methods including Polish and multilingual models. We consider classic word embedding models, recently developed contextual embeddings and multilingual sentence encoders, showing strengths and weaknesses of specific approaches. We also examine different methods of aggregating word vectors into a single sentence vector.},
  address = {Marseille, France},
  author = {Dadas, Slawomir  and
Pere{\\l}kiewicz, Micha{\\l}  and
Po{\\'s}wiata, Rafa{\\l}},
  booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference},
  editor = {Calzolari, Nicoletta  and
B{\\'e}chet, Fr{\\'e}d{\\'e}ric  and
Blache, Philippe  and
Choukri, Khalid  and
Cieri, Christopher  and
Declerck, Thierry  and
Goggi, Sara  and
Isahara, Hitoshi  and
Maegaard, Bente  and
Mariani, Joseph  and
Mazo, H{\\'e}l{\\`e}ne  and
Moreno, Asuncion  and
Odijk, Jan  and
Piperidis, Stelios},
  isbn = {979-10-95546-34-4},
  language = {English},
  month = may,
  pages = {1674--1680},
  publisher = {European Language Resources Association},
  title = {Evaluation of Sentence Representations in {P}olish},
  url = {https://aclanthology.org/2020.lrec-1.207},
  year = {2020},
}

GeoreviewClusteringP2P

Review clustering based on Yandex Georeview dataset

Dataset: ai-forever/georeview-clustering-p2p • License: mit • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	rus	Reviews, Written	derived	found

HALClusteringS2S

Clustering of titles from HAL (https://huggingface.co/datasets/lyon-nlp/clustering-hal-s2s)

Dataset: lyon-nlp/clustering-hal-s2s • License: apache-2.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fra	Academic, Written	human-annotated	found

Citation

@misc{ciancone2024extending,
  archiveprefix = {arXiv},
  author = {Mathieu Ciancone and Imene Kerboua and Marion Schaeffer and Wissam Siblini},
  eprint = {2405.20468},
  primaryclass = {cs.CL},
  title = {Extending the Massive Text Embedding Benchmark to French},
  year = {2024},
}

HALClusteringS2S.v2

Clustering of titles from HAL (https://huggingface.co/datasets/lyon-nlp/clustering-hal-s2s)

Dataset: lyon-nlp/clustering-hal-s2s • License: apache-2.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fra	Academic, Written	human-annotated	found

Citation

@misc{ciancone2024extending,
  archiveprefix = {arXiv},
  author = {Mathieu Ciancone and Imene Kerboua and Marion Schaeffer and Wissam Siblini},
  eprint = {2405.20468},
  primaryclass = {cs.CL},
  title = {Extending the Massive Text Embedding Benchmark to French},
  year = {2024},
}

HamshahriClustring

These datasets have been extracted from the RSS feed of two Farsi news agency websites.

Dataset: community-datasets/farsi_news • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fas	News	derived	found

Citation

IndicReviewsClusteringP2P

Clustering of reviews from IndicSentiment dataset. Clustering of 14 sets on the generic categories label.

Dataset: mteb/IndicReviewsClusteringP2P • License: cc0-1.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	asm, ben, brx, guj, hin, ... (13)	Reviews, Written	human-annotated	machine-translated and verified

Citation

@article{doddapaneni2022towards,
  author = {Sumanth Doddapaneni and Rahul Aralikatte and Gowtham Ramesh and Shreyansh Goyal and Mitesh M. Khapra and Anoop Kunchukuttan and Pratyush Kumar},
  doi = {10.18653/v1/2023.acl-long.693},
  journal = {Annual Meeting of the Association for Computational Linguistics},
  title = {Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages},
  year = {2022},
}

KlueMrcDomainClustering

this dataset is a processed and redistributed version of the KLUE-MRC dataset. Domain: Game / Media / Automotive / Finance / Real Estate / Education

Dataset: on-and-on/clustering_klue_mrc_context_domain • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	kor	News, Written	human-annotated	found

Citation

@misc{park2021klue,
  archiveprefix = {arXiv},
  author = {Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},
  eprint = {2105.09680},
  primaryclass = {cs.CL},
  title = {KLUE: Korean Language Understanding Evaluation},
  year = {2021},
}

KlueYnatMrcCategoryClustering

this dataset is a processed and redistributed version of the KLUE-Ynat & KLUE-MRC dataset. News_category: IT/Science, Sports, Media/Culture, Ecomomy/Finance, Real Estate

Dataset: on-and-on/clustering_klue_mrc_ynat_title • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to text (t2t)	v_measure	kor	News, Written	human-annotated	found

Citation

@misc{park2021klue,
  archiveprefix = {arXiv},
  author = {Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},
  eprint = {2105.09680},
  primaryclass = {cs.CL},
  title = {KLUE: Korean Language Understanding Evaluation},
  year = {2021},
}

LivedoorNewsClustering

Clustering of the news reports of a Japanese news site, Livedoor News by RONDHUIT Co, Ltd. in 2012. It contains over 7,000 news report texts across 9 categories (topics).

Dataset: sbintuitions/JMTEB • License: cc-by-nd-2.1-jp • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	jpn	News, Written	derived	found

LivedoorNewsClustering.v2

Clustering of the news reports of a Japanese news site, Livedoor News by RONDHUIT Co, Ltd. in 2012. It contains over 7,000 news report texts across 9 categories (topics). Version 2 updated on LivedoorNewsClustering by removing pairs where one of entries contain an empty sentences.

Dataset: sbintuitions/JMTEB • License: cc-by-nd-2.1-jp • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	jpn	News, Written	derived	found

MLSUMClusteringP2P

Clustering of newspaper article contents and titles from MLSUM dataset. Clustering of 10 sets on the newpaper article topics.

Dataset: mteb/mlsum • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu, fra, rus, spa	News, Written	derived	found

Citation

@article{scialom2020mlsum,
  author = {Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
  journal = {arXiv preprint arXiv:2004.14900},
  title = {MLSUM: The Multilingual Summarization Corpus},
  year = {2020},
}

MLSUMClusteringP2P.v2

Clustering of newspaper article contents and titles from MLSUM dataset. Clustering of 10 sets on the newpaper article topics.

Dataset: mteb/mlsum • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu, fra, rus, spa	News, Written	derived	found

Citation

@article{scialom2020mlsum,
  author = {Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
  journal = {arXiv preprint arXiv:2004.14900},
  title = {MLSUM: The Multilingual Summarization Corpus},
  year = {2020},
}

MLSUMClusteringS2S

Clustering of newspaper article contents and titles from MLSUM dataset. Clustering of 10 sets on the newpaper article topics.

Dataset: mteb/mlsum • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu, fra, rus, spa	News, Written	derived	found

Citation

@article{scialom2020mlsum,
  author = {Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
  journal = {arXiv preprint arXiv:2004.14900},
  title = {MLSUM: The Multilingual Summarization Corpus},
  year = {2020},
}

MLSUMClusteringS2S.v2

Clustering of newspaper article contents and titles from MLSUM dataset. Clustering of 10 sets on the newpaper article topics.

Dataset: mteb/mlsum • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu, fra, rus, spa	News, Written	derived	found

Citation

@article{scialom2020mlsum,
  author = {Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
  journal = {arXiv preprint arXiv:2004.14900},
  title = {MLSUM: The Multilingual Summarization Corpus},
  year = {2020},
}

MasakhaNEWSClusteringP2P

Clustering of news article headlines and texts from MasakhaNEWS dataset. Clustering of 10 sets on the news article label.

Dataset: masakhane/masakhanews • License: afl-3.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	amh, eng, fra, hau, ibo, ... (16)	News, Non-fiction, Written	derived	found

Citation

@article{adelani2023masakhanews,
  author = {David Ifeoluwa Adelani and  Marek Masiak and  Israel Abebe Azime and  Jesujoba Oluwadara Alabi and  Atnafu Lambebo Tonja and  Christine Mwase and  Odunayo Ogundepo and  Bonaventure F. P. Dossou and  Akintunde Oladipo and  Doreen Nixdorf and  Chris Chinenye Emezue and  Sana Sabah al-azzawi and  Blessing K. Sibanda and  Davis David and  Lolwethu Ndolela and  Jonathan Mukiibi and  Tunde Oluwaseyi Ajayi and  Tatiana Moteu Ngoli and  Brian Odhiambo and  Abraham Toluwase Owodunni and  Nnaemeka C. Obiefuna and  Shamsuddeen Hassan Muhammad and  Saheed Salahudeen Abdullahi and  Mesay Gemeda Yigezu and  Tajuddeen Gwadabe and  Idris Abdulmumin and  Mahlet Taye Bame and  Oluwabusayo Olufunke Awoyomi and  Iyanuoluwa Shode and  Tolulope Anu Adelani and  Habiba Abdulganiy Kailani and  Abdul-Hakeem Omotayo and  Adetola Adeeko and  Afolabi Abeeb and  Anuoluwapo Aremu and  Olanrewaju Samuel and  Clemencia Siro and  Wangari Kimotho and  Onyekachi Raphael Ogbu and  Chinedu E. Mbonu and  Chiamaka I. Chukwuneke and  Samuel Fanijo and  Jessica Ojo and  Oyinkansola F. Awosan and  Tadesse Kebede Guge and  Sakayo Toadoum Sari and  Pamela Nyatsine and  Freedmore Sidume and  Oreen Yousuf and  Mardiyyah Oduwole and  Ussen Kimanuka and  Kanda Patrick Tshinu and  Thina Diko and  Siyanda Nxakama and   Abdulmejid Tuni Johar and  Sinodos Gebre and  Muhidin Mohamed and  Shafie Abdi Mohamed and  Fuad Mire Hassan and  Moges Ahmed Mehamed and  Evrard Ngabire and  and Pontus Stenetorp},
  journal = {ArXiv},
  title = {MasakhaNEWS: News Topic Classification for African languages},
  volume = {},
  year = {2023},
}

MasakhaNEWSClusteringS2S

Clustering of news article headlines from MasakhaNEWS dataset. Clustering of 10 sets on the news article label.

Dataset: masakhane/masakhanews • License: afl-3.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	amh, eng, fra, hau, ibo, ... (16)	News, Written	human-annotated	not specified

Citation

@article{adelani2023masakhanews,
  author = {David Ifeoluwa Adelani and  Marek Masiak and  Israel Abebe Azime and  Jesujoba Oluwadara Alabi and  Atnafu Lambebo Tonja and  Christine Mwase and  Odunayo Ogundepo and  Bonaventure F. P. Dossou and  Akintunde Oladipo and  Doreen Nixdorf and  Chris Chinenye Emezue and  Sana Sabah al-azzawi and  Blessing K. Sibanda and  Davis David and  Lolwethu Ndolela and  Jonathan Mukiibi and  Tunde Oluwaseyi Ajayi and  Tatiana Moteu Ngoli and  Brian Odhiambo and  Abraham Toluwase Owodunni and  Nnaemeka C. Obiefuna and  Shamsuddeen Hassan Muhammad and  Saheed Salahudeen Abdullahi and  Mesay Gemeda Yigezu and  Tajuddeen Gwadabe and  Idris Abdulmumin and  Mahlet Taye Bame and  Oluwabusayo Olufunke Awoyomi and  Iyanuoluwa Shode and  Tolulope Anu Adelani and  Habiba Abdulganiy Kailani and  Abdul-Hakeem Omotayo and  Adetola Adeeko and  Afolabi Abeeb and  Anuoluwapo Aremu and  Olanrewaju Samuel and  Clemencia Siro and  Wangari Kimotho and  Onyekachi Raphael Ogbu and  Chinedu E. Mbonu and  Chiamaka I. Chukwuneke and  Samuel Fanijo and  Jessica Ojo and  Oyinkansola F. Awosan and  Tadesse Kebede Guge and  Sakayo Toadoum Sari and  Pamela Nyatsine and  Freedmore Sidume and  Oreen Yousuf and  Mardiyyah Oduwole and  Ussen Kimanuka and  Kanda Patrick Tshinu and  Thina Diko and  Siyanda Nxakama and   Abdulmejid Tuni Johar and  Sinodos Gebre and  Muhidin Mohamed and  Shafie Abdi Mohamed and  Fuad Mire Hassan and  Moges Ahmed Mehamed and  Evrard Ngabire and  and Pontus Stenetorp},
  journal = {ArXiv},
  title = {MasakhaNEWS: News Topic Classification for African languages},
  volume = {},
  year = {2023},
}

MedrxivClusteringP2P

Clustering of titles+abstract from medrxiv. Clustering of 10 sets, based on the main category.

Dataset: mteb/medrxiv-clustering-p2p • License: https://www.medrxiv.org/content/about-medrxiv • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Written	derived	created

MedrxivClusteringP2P.v2

Clustering of titles+abstract from medrxiv across 51 categories.

Dataset: mteb/medrxiv-clustering-p2p • License: https://www.medrxiv.org/content/about-medrxiv • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Medical, Written	derived	created

MedrxivClusteringS2S

Clustering of titles from medrxiv. Clustering of 10 sets, based on the main category.

Dataset: mteb/medrxiv-clustering-s2s • License: https://www.medrxiv.org/content/about-medrxiv • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Medical, Written	derived	created

MedrxivClusteringS2S.v2

Clustering of titles from medrxiv across 51 categories.

Dataset: mteb/medrxiv-clustering-s2s • License: https://www.medrxiv.org/content/about-medrxiv • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Academic, Medical, Written	derived	created

MewsC16JaClustering

MewsC-16 (Multilingual Short Text Clustering Dataset for News in 16 languages) is constructed from Wikinews. This dataset is the Japanese split of MewsC-16, containing topic sentences from Wikinews articles in 12 categories. More detailed information is available in the Appendix E of the citation.

Dataset: sbintuitions/JMTEB • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	jpn	News, Written	derived	found

Citation

@inproceedings{nishikawa-etal-2022-ease,
  abstract = {We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities.The advantage of using entity supervision is twofold: (1) entities have been shown to be a strong indicator of text semantics and thus should provide rich training signals for sentence embeddings; (2) entities are defined independently of languages and thus offer useful cross-lingual alignment supervision.We evaluate EASE against other unsupervised models both in monolingual and multilingual settings.We show that EASE exhibits competitive or better performance in English semantic textual similarity (STS) and short text clustering (STC) tasks and it significantly outperforms baseline methods in multilingual settings on a variety of tasks.Our source code, pre-trained models, and newly constructed multi-lingual STC dataset are available at https://github.com/studio-ousia/ease.},
  address = {Seattle, United States},
  author = {Nishikawa, Sosuke  and
Ri, Ryokan  and
Yamada, Ikuya  and
Tsuruoka, Yoshimasa  and
Echizen, Isao},
  booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  month = jul,
  pages = {3870--3885},
  publisher = {Association for Computational Linguistics},
  title = {{EASE}: Entity-Aware Contrastive Learning of Sentence Embedding},
  url = {https://aclanthology.org/2022.naacl-main.284},
  year = {2022},
}

NLPTwitterAnalysisClustering

Clustering of tweets from twitter across 26 categories.

Dataset: hamedhf/nlp_twitter_analysis • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fas	Social	derived	found

Citation

PlscClusteringP2P

Clustering of Polish article titles+abstracts from Library of Science (https://bibliotekanauki.pl/), either on the scientific field or discipline.

Dataset: PL-MTEB/plsc-clustering-p2p • License: cc0-1.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	pol	Academic, Written	derived	found

PlscClusteringP2P.v2

Clustering of Polish article titles+abstracts from Library of Science (https://bibliotekanauki.pl/), either on the scientific field or discipline.

Dataset: PL-MTEB/plsc-clustering-p2p • License: cc0-1.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	pol	Academic, Written	derived	found

PlscClusteringS2S

Clustering of Polish article titles from Library of Science (https://bibliotekanauki.pl/), either on the scientific field or discipline.

Dataset: PL-MTEB/plsc-clustering-s2s • License: cc0-1.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	pol	Academic, Written	derived	found

PlscClusteringS2S.v2

Clustering of Polish article titles from Library of Science (https://bibliotekanauki.pl/), either on the scientific field or discipline.

Dataset: PL-MTEB/plsc-clustering-s2s • License: cc0-1.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	pol	Academic, Written	derived	found

RedditClustering

Clustering of titles from 199 subreddits. Clustering of 25 sets, each with 10-50 classes, and each class with 100 - 1000 sentences.

Dataset: mteb/reddit-clustering • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Social, Web, Written	derived	found

Citation

@article{geigle:2021:arxiv,
  archiveprefix = {arXiv},
  author = {Gregor Geigle and
Nils Reimers and
Andreas R{\"u}ckl{\'e} and
Iryna Gurevych},
  eprint = {2104.07081},
  journal = {arXiv preprint},
  title = {TWEAC: Transformer with Extendable QA Agent Classifiers},
  url = {http://arxiv.org/abs/2104.07081},
  volume = {abs/2104.07081},
  year = {2021},
}

RedditClustering-VN

A translated dataset from Clustering of titles from 199 subreddits. Clustering of 25 sets, each with 10-50 classes, and each class with 100 - 1000 sentences. The process of creating the VN-MTEB (Vietnamese Massive Text Embedding Benchmark) from English samples involves a new automated system: - The system uses large language models (LLMs), specifically Coherence's Aya model, for translation. - Applies advanced embedding models to filter the translations. - Use LLM-as-a-judge to scoring the quality of the samples base on multiple criteria.

Dataset: GreenNode/reddit-clustering-vn • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	vie	Social, Web, Written	derived	machine-translated and LM verified

Citation

@misc{pham2025vnmtebvietnamesemassivetext,
  archiveprefix = {arXiv},
  author = {Loc Pham and Tung Luu and Thu Vo and Minh Nguyen and Viet Hoang},
  eprint = {2507.21500},
  primaryclass = {cs.CL},
  title = {VN-MTEB: Vietnamese Massive Text Embedding Benchmark},
  url = {https://arxiv.org/abs/2507.21500},
  year = {2025},
}

RedditClustering.v2

Clustering of titles from 199 subreddits. Clustering of 25 sets, each with 10-50 classes, and each class with 100 - 1000 sentences.

Dataset: mteb/reddit-clustering • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Social, Web, Written	derived	found

Citation

@article{geigle:2021:arxiv,
  archiveprefix = {arXiv},
  author = {Gregor Geigle and
Nils Reimers and
Andreas R{\"u}ckl{\'e} and
Iryna Gurevych},
  eprint = {2104.07081},
  journal = {arXiv preprint},
  title = {TWEAC: Transformer with Extendable QA Agent Classifiers},
  url = {http://arxiv.org/abs/2104.07081},
  volume = {abs/2104.07081},
  year = {2021},
}

RedditClusteringP2P

Clustering of title+posts from reddit. Clustering of 10 sets of 50k paragraphs and 40 sets of 10k paragraphs.

Dataset: mteb/reddit-clustering-p2p • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Social, Web, Written	derived	found

Citation

@article{geigle:2021:arxiv,
  archiveprefix = {arXiv},
  author = {Gregor Geigle and
Nils Reimers and
Andreas R{\"u}ckl{\'e} and
Iryna Gurevych},
  eprint = {2104.07081},
  journal = {arXiv preprint},
  title = {TWEAC: Transformer with Extendable QA Agent Classifiers},
  url = {http://arxiv.org/abs/2104.07081},
  volume = {abs/2104.07081},
  year = {2021},
}

RedditClusteringP2P-VN

A translated dataset from Clustering of title+posts from reddit. Clustering of 10 sets of 50k paragraphs and 40 sets of 10k paragraphs. The process of creating the VN-MTEB (Vietnamese Massive Text Embedding Benchmark) from English samples involves a new automated system: - The system uses large language models (LLMs), specifically Coherence's Aya model, for translation. - Applies advanced embedding models to filter the translations. - Use LLM-as-a-judge to scoring the quality of the samples base on multiple criteria.

Dataset: GreenNode/reddit-clustering-p2p-vn • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	vie	Social, Web, Written	derived	machine-translated and LM verified

Citation

@misc{pham2025vnmtebvietnamesemassivetext,
  archiveprefix = {arXiv},
  author = {Loc Pham and Tung Luu and Thu Vo and Minh Nguyen and Viet Hoang},
  eprint = {2507.21500},
  primaryclass = {cs.CL},
  title = {VN-MTEB: Vietnamese Massive Text Embedding Benchmark},
  url = {https://arxiv.org/abs/2507.21500},
  year = {2025},
}

RedditClusteringP2P.v2

Clustering of title+posts from reddit. Clustering of 10 sets of 50k paragraphs and 40 sets of 10k paragraphs.

Dataset: mteb/reddit-clustering-p2p • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Social, Web, Written	derived	found

Citation

@article{geigle:2021:arxiv,
  archiveprefix = {arXiv},
  author = {Gregor Geigle and
Nils Reimers and
Andreas R{\"u}ckl{\'e} and
Iryna Gurevych},
  eprint = {2104.07081},
  journal = {arXiv preprint},
  title = {TWEAC: Transformer with Extendable QA Agent Classifiers},
  url = {http://arxiv.org/abs/2104.07081},
  volume = {abs/2104.07081},
  year = {2021},
}

RomaniBibleClustering

Clustering verses from the Bible in Kalderash Romani by book.

Dataset: kardosdrur/romani-bible • License: mit • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	rom	Religious, Written	derived	human-translated and localized

RuSciBenchGRNTIClusteringP2P

Clustering of scientific papers (title+abstract) by rubric

Dataset: ai-forever/ru-scibench-grnti-classification • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	rus	Academic, Written	derived	found

RuSciBenchOECDClusteringP2P

Clustering of scientific papers (title+abstract) by rubric

Dataset: ai-forever/ru-scibench-oecd-classification • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	rus	Academic, Written	derived	found

SIB200ClusteringS2S

SIB-200 is the largest publicly available topic classification dataset based on Flores-200 covering 205 languages and dialects annotated. The dataset is annotated in English for the topics, science/technology, travel, politics, sports, health, entertainment, and geography. The labels are then transferred to the other languages in Flores-200 which are human-translated.

Dataset: mteb/sib200 • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	ace, acm, acq, aeb, afr, ... (197)	News, Written	expert-annotated	human-translated and localized

Citation

@article{adelani2023sib,
  author = {Adelani, David Ifeoluwa and Liu, Hannah and Shen, Xiaoyu and Vassilyev, Nikita and Alabi, Jesujoba O and Mao, Yanke and Gao, Haonan and Lee, Annie En-Shiun},
  journal = {arXiv preprint arXiv:2309.07445},
  title = {SIB-200: A simple, inclusive, and big evaluation dataset for topic classification in 200+ languages and dialects},
  year = {2023},
}

SIDClustring

Clustering of summariesfrom SIDClustring across categories.

Dataset: MCINext/sid-clustering • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	fas	Academic	derived	found

Citation

SNLClustering

Webscrabed articles from the Norwegian lexicon 'Det Store Norske Leksikon'. Uses articles categories as clusters.

Dataset: navjordj/SNL_summarization • License: cc-by-nc-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	nob	Encyclopaedic, Non-fiction, Written	derived	found

Citation

@mastersthesis{navjord2023beyond,
  author = {Navjord, J{\o}rgen Johnsen and Korsvik, Jon-Mikkel Ryen},
  school = {Norwegian University of Life Sciences, {\AA}s},
  title = {Beyond extractive: advancing abstractive automatic text summarization in Norwegian with transformers},
  year = {2023},
}

SNLHierarchicalClusteringP2P

Webscrabed articles from the Norwegian lexicon 'Det Store Norske Leksikon'. Uses articles categories as clusters.

Dataset: mteb/SNLHierarchicalClusteringP2P • License: cc-by-nc-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	nob	Encyclopaedic, Non-fiction, Written	derived	found

Citation

@mastersthesis{navjord2023beyond,
  author = {Navjord, J{\\o}rgen Johnsen and Korsvik, Jon-Mikkel Ryen},
  school = {Norwegian University of Life Sciences, {\\AA}s},
  title = {Beyond extractive: advancing abstractive automatic text summarization in Norwegian with transformers},
  year = {2023},
}

SNLHierarchicalClusteringS2S

Webscrabed articles from the Norwegian lexicon 'Det Store Norske Leksikon'. Uses articles categories as clusters.

Dataset: mteb/SNLHierarchicalClusteringS2S • License: cc-by-nc-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	nob	Encyclopaedic, Non-fiction, Written	derived	found

Citation

@mastersthesis{navjord2023beyond,
  author = {Navjord, J{\\o}rgen Johnsen and Korsvik, Jon-Mikkel Ryen},
  school = {Norwegian University of Life Sciences, {\\AA}s},
  title = {Beyond extractive: advancing abstractive automatic text summarization in Norwegian with transformers},
  year = {2023},
}

SpanishNewsClusteringP2P

Clustering of news articles, 7 topics in total.

Dataset: jinaai/spanish_news_clustering • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	spa	not specified	not specified	not specified

StackExchangeClustering

Clustering of titles from 121 stackexchanges. Clustering of 25 sets, each with 10-50 classes, and each class with 100 - 1000 sentences.

Dataset: mteb/stackexchange-clustering • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Web, Written	derived	found

Citation

@article{geigle:2021:arxiv,
  archiveprefix = {arXiv},
  author = {Gregor Geigle and
Nils Reimers and
Andreas R{\"u}ckl{\'e} and
Iryna Gurevych},
  eprint = {2104.07081},
  journal = {arXiv preprint},
  title = {TWEAC: Transformer with Extendable QA Agent Classifiers},
  url = {http://arxiv.org/abs/2104.07081},
  volume = {abs/2104.07081},
  year = {2021},
}

StackExchangeClustering-VN

A translated dataset from Clustering of titles from 121 stackexchanges. Clustering of 25 sets, each with 10-50 classes, and each class with 100 - 1000 sentences. The process of creating the VN-MTEB (Vietnamese Massive Text Embedding Benchmark) from English samples involves a new automated system: - The system uses large language models (LLMs), specifically Coherence's Aya model, for translation. - Applies advanced embedding models to filter the translations. - Use LLM-as-a-judge to scoring the quality of the samples base on multiple criteria.

Dataset: GreenNode/stackexchange-clustering-vn • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	vie	Web, Written	derived	machine-translated and LM verified

Citation

@misc{pham2025vnmtebvietnamesemassivetext,
  archiveprefix = {arXiv},
  author = {Loc Pham and Tung Luu and Thu Vo and Minh Nguyen and Viet Hoang},
  eprint = {2507.21500},
  primaryclass = {cs.CL},
  title = {VN-MTEB: Vietnamese Massive Text Embedding Benchmark},
  url = {https://arxiv.org/abs/2507.21500},
  year = {2025},
}

StackExchangeClustering.v2

Clustering of titles from 121 stackexchanges. Clustering of 25 sets, each with 10-50 classes, and each class with 100 - 1000 sentences.

Dataset: mteb/stackexchange-clustering • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Web, Written	derived	found

Citation

@article{geigle:2021:arxiv,
  archiveprefix = {arXiv},
  author = {Gregor Geigle and
Nils Reimers and
Andreas R{\"u}ckl{\'e} and
Iryna Gurevych},
  eprint = {2104.07081},
  journal = {arXiv preprint},
  title = {TWEAC: Transformer with Extendable QA Agent Classifiers},
  url = {http://arxiv.org/abs/2104.07081},
  volume = {abs/2104.07081},
  year = {2021},
}

StackExchangeClusteringP2P

Clustering of title+body from stackexchange. Clustering of 5 sets of 10k paragraphs and 5 sets of 5k paragraphs.

Dataset: mteb/stackexchange-clustering-p2p • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Web, Written	derived	found

Citation

@article{geigle:2021:arxiv,
  archiveprefix = {arXiv},
  author = {Gregor Geigle and
Nils Reimers and
Andreas R{\"u}ckl{\'e} and
Iryna Gurevych},
  eprint = {2104.07081},
  journal = {arXiv preprint},
  title = {TWEAC: Transformer with Extendable QA Agent Classifiers},
  url = {http://arxiv.org/abs/2104.07081},
  volume = {abs/2104.07081},
  year = {2021},
}

StackExchangeClusteringP2P-VN

A translated Clustering of title+body from stackexchange. Clustering of 5 sets of 10k paragraphs and 5 sets of 5k paragraphs. The process of creating the VN-MTEB (Vietnamese Massive Text Embedding Benchmark) from English samples involves a new automated system: - The system uses large language models (LLMs), specifically Coherence's Aya model, for translation. - Applies advanced embedding models to filter the translations. - Use LLM-as-a-judge to scoring the quality of the samples base on multiple criteria.

Dataset: GreenNode/stackexchange-clustering-p2p-vn • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	vie	Web, Written	derived	machine-translated and LM verified

Citation

@misc{pham2025vnmtebvietnamesemassivetext,
  archiveprefix = {arXiv},
  author = {Loc Pham and Tung Luu and Thu Vo and Minh Nguyen and Viet Hoang},
  eprint = {2507.21500},
  primaryclass = {cs.CL},
  title = {VN-MTEB: Vietnamese Massive Text Embedding Benchmark},
  url = {https://arxiv.org/abs/2507.21500},
  year = {2025},
}

StackExchangeClusteringP2P.v2

Clustering of title+body from stackexchange. Clustering of 5 sets of 10k paragraphs and 5 sets of 5k paragraphs.

Dataset: mteb/stackexchange-clustering-p2p • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Web, Written	derived	found

Citation

@article{geigle:2021:arxiv,
  archiveprefix = {arXiv},
  author = {Gregor Geigle and
Nils Reimers and
Andreas R{\"u}ckl{\'e} and
Iryna Gurevych},
  eprint = {2104.07081},
  journal = {arXiv preprint},
  title = {TWEAC: Transformer with Extendable QA Agent Classifiers},
  url = {http://arxiv.org/abs/2104.07081},
  volume = {abs/2104.07081},
  year = {2021},
}

SwednClustering

The SWE-DN corpus is based on 1,963,576 news articles from the Swedish newspaper Dagens Nyheter (DN) during the years 2000--2020. The articles are filtered to resemble the CNN/DailyMail dataset both regarding textual structure. This dataset uses the category labels as clusters.

Dataset: sbx/superlim-2 • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	swe	News, Non-fiction, Written	derived	found

Citation

@inproceedings{monsen2021method,
  author = {Monsen, Julius and J{\"o}nsson, Arne},
  booktitle = {Proceedings of CLARIN Annual Conference},
  title = {A method for building non-english corpora for abstractive text summarization},
  year = {2021},
}

SwednClusteringP2P

The SWE-DN corpus is based on 1,963,576 news articles from the Swedish newspaper Dagens Nyheter (DN) during the years 2000--2020. The articles are filtered to resemble the CNN/DailyMail dataset both regarding textual structure. This dataset uses the category labels as clusters.

Dataset: sbx/superlim-2 • License: cc-by-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	swe	News, Non-fiction, Written	derived	found

Citation

@inproceedings{monsen2021method,
  author = {Monsen, Julius and J{\"o}nsson, Arne},
  booktitle = {Proceedings of CLARIN Annual Conference},
  title = {A method for building non-english corpora for abstractive text summarization},
  year = {2021},
}

SwednClusteringS2S

The SWE-DN corpus is based on 1,963,576 news articles from the Swedish newspaper Dagens Nyheter (DN) during the years 2000--2020. The articles are filtered to resemble the CNN/DailyMail dataset both regarding textual structure. This dataset uses the category labels as clusters.

Dataset: sbx/superlim-2 • License: cc-by-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	swe	News, Non-fiction, Written	derived	found

Citation

@inproceedings{monsen2021method,
  author = {Monsen, Julius and J{\"o}nsson, Arne},
  booktitle = {Proceedings of CLARIN Annual Conference},
  title = {A method for building non-english corpora for abstractive text summarization},
  year = {2021},
}

TenKGnadClusteringP2P

Clustering of news article titles+subheadings+texts. Clustering of 10 splits on the news article category.

Dataset: slvnwhrl/tenkgnad-clustering-p2p • License: cc-by-nc-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu	Web, Written	not specified	found

TenKGnadClusteringP2P.v2

Clustering of news article titles+subheadings+texts. Clustering of 10 splits on the news article category.

Dataset: slvnwhrl/tenkgnad-clustering-p2p • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu	News, Non-fiction, Written	derived	found

TenKGnadClusteringS2S

Clustering of news article titles. Clustering of 10 splits on the news article category.

Dataset: slvnwhrl/tenkgnad-clustering-s2s • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu	News, Non-fiction, Written	not specified	not specified

TenKGnadClusteringS2S.v2

Clustering of news article titles. Clustering of 10 splits on the news article category.

Dataset: slvnwhrl/tenkgnad-clustering-s2s • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	deu	News, Non-fiction, Written	derived	found

ThuNewsClusteringP2P

Clustering of titles + abstracts from the THUCNews dataset

Dataset: C-MTEB/ThuNewsClusteringP2P • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	cmn	not specified	not specified	not specified

Citation

@inproceedings{eisner2007proceedings,
  author = {Eisner, Jason},
  booktitle = {Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)},
  title = {Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL)},
  year = {2007},
}

@inproceedings{li2006comparison,
  author = {Li, Jingyang and Sun, Maosong and Zhang, Xian},
  booktitle = {proceedings of the 21st international conference on computational linguistics and 44th annual meeting of the association for computational linguistics},
  pages = {545--552},
  title = {A comparison and semi-quantitative analysis of words and character-bigrams as features in chinese text categorization},
  year = {2006},
}

ThuNewsClusteringP2P.v2

Clustering of titles + abstracts from the THUCNews dataset

Dataset: C-MTEB/ThuNewsClusteringP2P • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	cmn	News, Written	derived	found

Citation

@software{THUCTC,
  author = {Sun, M. and Li, J. and Guo, Z. and Yu, Z. and Zheng, Y. and Si, X. and Liu, Z.},
  note = {THU Chinese Text Classification Toolkit},
  publisher = {THU Natural Language Processing Lab},
  title = {THUCTC: An Efficient Chinese Text Classifier},
  url = {https://github.com/thunlp/THUCTC},
  year = {2016},
}

ThuNewsClusteringS2S

Clustering of titles from the THUCNews dataset

Dataset: C-MTEB/ThuNewsClusteringS2S • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	cmn	not specified	not specified	not specified

Citation

@inproceedings{eisner2007proceedings,
  author = {Eisner, Jason},
  booktitle = {Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)},
  title = {Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL)},
  year = {2007},
}

@inproceedings{li2006comparison,
  author = {Li, Jingyang and Sun, Maosong and Zhang, Xian},
  booktitle = {proceedings of the 21st international conference on computational linguistics and 44th annual meeting of the association for computational linguistics},
  pages = {545--552},
  title = {A comparison and semi-quantitative analysis of words and character-bigrams as features in chinese text categorization},
  year = {2006},
}

ThuNewsClusteringS2S.v2

Clustering of titles from the THUCNews dataset

Dataset: C-MTEB/ThuNewsClusteringS2S • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	cmn	News, Written	derived	found

Citation

@software{THUCTC,
  author = {Sun, M. and Li, J. and Guo, Z. and Yu, Z. and Zheng, Y. and Si, X. and Liu, Z.},
  note = {THU Chinese Text Classification Toolkit},
  publisher = {THU Natural Language Processing Lab},
  title = {THUCTC: An Efficient Chinese Text Classifier},
  url = {https://github.com/thunlp/THUCTC},
  year = {2016},
}

TwentyNewsgroupsClustering

Clustering of the 20 Newsgroups dataset (subject only).

Dataset: mteb/twentynewsgroups-clustering • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	News, Written	derived	found

Citation

@incollection{LANG1995331,
  address = {San Francisco (CA)},
  author = {Ken Lang},
  booktitle = {Machine Learning Proceedings 1995},
  doi = {https://doi.org/10.1016/B978-1-55860-377-6.50048-7},
  editor = {Armand Prieditis and Stuart Russell},
  isbn = {978-1-55860-377-6},
  pages = {331-339},
  publisher = {Morgan Kaufmann},
  title = {NewsWeeder: Learning to Filter Netnews},
  url = {https://www.sciencedirect.com/science/article/pii/B9781558603776500487},
  year = {1995},
}

TwentyNewsgroupsClustering-VN

A translated dataset from Clustering of the 20 Newsgroups dataset (subject only). The process of creating the VN-MTEB (Vietnamese Massive Text Embedding Benchmark) from English samples involves a new automated system: - The system uses large language models (LLMs), specifically Coherence's Aya model, for translation. - Applies advanced embedding models to filter the translations. - Use LLM-as-a-judge to scoring the quality of the samples base on multiple criteria.

Dataset: GreenNode/twentynewsgroups-clustering-vn • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	vie	News, Written	derived	machine-translated and LM verified

Citation

@misc{pham2025vnmtebvietnamesemassivetext,
  archiveprefix = {arXiv},
  author = {Loc Pham and Tung Luu and Thu Vo and Minh Nguyen and Viet Hoang},
  eprint = {2507.21500},
  primaryclass = {cs.CL},
  title = {VN-MTEB: Vietnamese Massive Text Embedding Benchmark},
  url = {https://arxiv.org/abs/2507.21500},
  year = {2025},
}

TwentyNewsgroupsClustering.v2

Clustering of the 20 Newsgroups dataset (subject only).

Dataset: mteb/twentynewsgroups-clustering • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	News, Written	derived	found

Citation

@incollection{LANG1995331,
  address = {San Francisco (CA)},
  author = {Ken Lang},
  booktitle = {Machine Learning Proceedings 1995},
  doi = {https://doi.org/10.1016/B978-1-55860-377-6.50048-7},
  editor = {Armand Prieditis and Stuart Russell},
  isbn = {978-1-55860-377-6},
  pages = {331-339},
  publisher = {Morgan Kaufmann},
  title = {NewsWeeder: Learning to Filter Netnews},
  url = {https://www.sciencedirect.com/science/article/pii/B9781558603776500487},
  year = {1995},
}

VGClustering

Articles and their classes (e.g. sports) from VG news articles extracted from Norsk Aviskorpus.

Dataset: navjordj/VG_summarization • License: not specified • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	nob	News, Non-fiction, Written	derived	found

Citation

@mastersthesis{navjord2023beyond,
  author = {Navjord, J{\o}rgen Johnsen and Korsvik, Jon-Mikkel Ryen},
  school = {Norwegian University of Life Sciences, {\AA}s},
  title = {Beyond extractive: advancing abstractive automatic text summarization in Norwegian with transformers},
  year = {2023},
}

VGHierarchicalClusteringP2P

Articles and their classes (e.g. sports) from VG news articles extracted from Norsk Aviskorpus.

Dataset: navjordj/VG_summarization • License: cc-by-nc-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	nob	News, Non-fiction, Written	derived	found

Citation

@mastersthesis{navjord2023beyond,
  author = {Navjord, J{\\o}rgen Johnsen and Korsvik, Jon-Mikkel Ryen},
  school = {Norwegian University of Life Sciences, {\\AA}s},
  title = {Beyond extractive: advancing abstractive automatic text summarization in Norwegian with transformers},
  year = {2023},
}

VGHierarchicalClusteringS2S

Articles and their classes (e.g. sports) from VG news articles extracted from Norsk Aviskorpus.

Dataset: navjordj/VG_summarization • License: cc-by-nc-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	nob	News, Non-fiction, Written	derived	found

Citation

@mastersthesis{navjord2023beyond,
  author = {Navjord, J{\\o}rgen Johnsen and Korsvik, Jon-Mikkel Ryen},
  school = {Norwegian University of Life Sciences, {\\AA}s},
  title = {Beyond extractive: advancing abstractive automatic text summarization in Norwegian with transformers},
  year = {2023},
}

WikiCitiesClustering

Clustering of Wikipedia articles of cities by country from https://huggingface.co/datasets/wikipedia. Test set includes 126 countries, and a total of 3531 cities.

Dataset: jinaai/cities_wiki_clustering • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Encyclopaedic, Written	derived	found

Citation

@online{wikidump,
  author = {Wikimedia Foundation},
  title = {Wikimedia Downloads},
  url = {https://dumps.wikimedia.org},
}

WikiClusteringP2P

Clustering of wikipedia articles inspired by BlubrbsClusteringP2P. Labels are taken from top-level categories of the respective languages (e.g., https://lv.wikipedia.org/wiki/Kategorija:Pamatkategorijas).

Dataset: ryzzlestrizzle/multi-wiki-clustering-p2p • License: cc-by-sa-3.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	bos, cat, ces, dan, eus, ... (14)	Encyclopaedic, Written	derived	created

WikiClusteringP2P.v2

Clustering of wikipedia articles inspired by BlubrbsClusteringP2P. Labels are taken from top-level categories of the respective languages (e.g., https://lv.wikipedia.org/wiki/Kategorija:Pamatkategorijas).

Dataset: ryzzlestrizzle/multi-wiki-clustering-p2p • License: cc-by-sa-3.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	bos, cat, ces, dan, eus, ... (14)	Encyclopaedic, Written	derived	created

WikipediaChemistryTopicsClustering

ChemTEB evaluates the performance of text embedding models on chemical domain data.

Dataset: BASF-AI/WikipediaEasy10Clustering • License: cc-by-nc-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Chemistry	derived	created

Citation

@article{kasmaee2024chemteb,
  author = {Kasmaee, Ali Shiraee and Khodadad, Mohammad and Saloot, Mohammad Arshi and Sherck, Nick and Dokas, Stephen and Mahyar, Hamidreza and Samiee, Soheila},
  journal = {arXiv preprint arXiv:2412.00532},
  title = {ChemTEB: Chemical Text Embedding Benchmark, an Overview of Embedding Models Performance \& Efficiency on a Specific Domain},
  year = {2024},
}

WikipediaSpecialtiesInChemistryClustering

ChemTEB evaluates the performance of text embedding models on chemical domain data.

Dataset: BASF-AI/WikipediaMedium5Clustering • License: cc-by-nc-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	v_measure	eng	Chemistry	derived	created

Citation

@article{kasmaee2024chemteb,
  author = {Kasmaee, Ali Shiraee and Khodadad, Mohammad and Saloot, Mohammad Arshi and Sherck, Nick and Dokas, Stephen and Mahyar, Hamidreza and Samiee, Soheila},
  journal = {arXiv preprint arXiv:2412.00532},
  title = {ChemTEB: Chemical Text Embedding Benchmark, an Overview of Embedding Models Performance \& Efficiency on a Specific Domain},
  year = {2024},
}