MultilabelClassification

• Number of tasks: 11

BrazilianToxicTweetsClassification

ToLD-Br is the biggest dataset for toxic tweets in Brazilian Portuguese, crowdsourced by 42 annotators selected from a pool of 129 volunteers. Annotators were selected aiming to create a plural group in terms of demographics (ethnicity, sexual orientation, age, gender). Each tweet was labeled by three annotators in 6 possible categories: LGBTQ+phobia, Xenophobia, Obscene, Insult, Misogyny and Racism.

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

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	accuracy	por	Constructed, Written	expert-annotated	found

Citation

@article{DBLP:journals/corr/abs-2010-04543,
  author = {Joao Augusto Leite and
Diego F. Silva and
Kalina Bontcheva and
Carolina Scarton},
  eprint = {2010.04543},
  eprinttype = {arXiv},
  journal = {CoRR},
  timestamp = {Tue, 15 Dec 2020 16:10:16 +0100},
  title = {Toxic Language Detection in Social Media for Brazilian Portuguese:
New Dataset and Multilingual Analysis},
  url = {https://arxiv.org/abs/2010.04543},
  volume = {abs/2010.04543},
  year = {2020},
}

CEDRClassification

Classification of sentences by emotions, labeled into 5 categories (joy, sadness, surprise, fear, and anger).

Dataset: ai-forever/cedr-classification • License: apache-2.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	accuracy	rus	Blog, Social, Web, Written	human-annotated	found

Citation

@article{sboev2021data,
  author = {Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman},
  journal = {Procedia Computer Science},
  pages = {637--642},
  publisher = {Elsevier},
  title = {Data-Driven Model for Emotion Detection in Russian Texts},
  volume = {190},
  year = {2021},
}

CovidDisinformationNLMultiLabelClassification

The dataset is curated to address questions of interest to journalists, fact-checkers, social media platforms, policymakers, and the general public.

Dataset: clips/mteb-nl-COVID-19-disinformation • License: cc-by-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	f1	nld	Social, Web, Written	human-annotated	found

Citation

@inproceedings{alam-etal-2021-fighting-covid,
  address = {Punta Cana, Dominican Republic},
  author = {Alam, Firoj  and
Shaar, Shaden  and
Dalvi, Fahim  and
Sajjad, Hassan  and
Nikolov, Alex  and
Mubarak, Hamdy  and
Da San Martino, Giovanni  and
Abdelali, Ahmed  and
Durrani, Nadir  and
Darwish, Kareem  and
Al-Homaid, Abdulaziz  and
Zaghouani, Wajdi  and
Caselli, Tommaso  and
Danoe, Gijs  and
Stolk, Friso  and
Bruntink, Britt  and
Nakov, Preslav},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2021},
  doi = {10.18653/v1/2021.findings-emnlp.56},
  editor = {Moens, Marie-Francine  and
Huang, Xuanjing  and
Specia, Lucia  and
Yih, Scott Wen-tau},
  month = nov,
  pages = {611--649},
  publisher = {Association for Computational Linguistics},
  title = {Fighting the {COVID}-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society},
  url = {https://aclanthology.org/2021.findings-emnlp.56/},
  year = {2021},
}

EmitClassification

The EMit dataset is a comprehensive resource for the detection of emotions in Italian social media texts. The EMit dataset consists of social media messages about TV shows, TV series, music videos, and advertisements. Each message is annotated with one or more of the 8 primary emotions defined by Plutchik (anger, anticipation, disgust, fear, joy, sadness, surprise, trust), as well as an additional label “love.”

Dataset: MattiaSangermano/emit • License: cc-by-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	accuracy	ita	Social, Written	expert-annotated	found

Citation

@inproceedings{araque2023emit,
  author = {Araque, O and Frenda, S and Sprugnoli, R and Nozza, D and Patti, V and others},
  booktitle = {CEUR WORKSHOP PROCEEDINGS},
  organization = {CEUR-WS},
  pages = {1--8},
  title = {EMit at EVALITA 2023: Overview of the Categorical Emotion Detection in Italian Social Media Task},
  volume = {3473},
  year = {2023},
}

KorHateSpeechMLClassification

The Korean Multi-label Hate Speech Dataset, K-MHaS, consists of 109,692 utterances from Korean online news comments, labelled with 8 fine-grained hate speech classes (labels: Politics, Origin, Physical, Age, Gender, Religion, Race, Profanity) or Not Hate Speech class. Each utterance provides from a single to four labels that can handles Korean language patterns effectively. For more details, please refer to the paper about K-MHaS, published at COLING 2022. This dataset is based on the Korean online news comments available on Kaggle and Github. The unlabeled raw data was collected between January 2018 and June 2020. The language producers are users who left the comments on the Korean online news platform between 2018 and 2020.

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

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	accuracy	kor	Social, Written	expert-annotated	found

Citation

@inproceedings{lee-etal-2022-k,
  address = {Gyeongju, Republic of Korea},
  author = {Lee, Jean  and
Lim, Taejun  and
Lee, Heejun  and
Jo, Bogeun  and
Kim, Yangsok  and
Yoon, Heegeun  and
Han, Soyeon Caren},
  booktitle = {Proceedings of the 29th International Conference on Computational Linguistics},
  month = oct,
  pages = {3530--3538},
  publisher = {International Committee on Computational Linguistics},
  title = {K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment},
  url = {https://aclanthology.org/2022.coling-1.311},
  year = {2022},
}

MalteseNewsClassification

A multi-label topic classification dataset for Maltese News Articles. The data was collected from the press_mt subset from Korpus Malti v4.0. Article contents were cleaned to filter out JavaScript, CSS, & repeated non-Maltese sub-headings. The labels are based on the category field from this corpus.

Dataset: MLRS/maltese_news_categories • License: cc-by-nc-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	accuracy	mlt	Constructed, Written	expert-annotated	found

Citation

@inproceedings{maltese-news-datasets,
  author = {Chaudhary, Amit Kumar  and
Micallef, Kurt  and
Borg, Claudia},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation},
  month = may,
  publisher = {Association for Computational Linguistics},
  title = {Topic Classification and Headline Generation for {M}altese using a Public News Corpus},
  year = {2024},
}

MultiEURLEXMultilabelClassification

EU laws in 23 EU languages containing annotated labels for 21 EUROVOC concepts.

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

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	accuracy	bul, ces, dan, deu, ell, ... (23)	Government, Legal, Written	expert-annotated	found

Citation

@inproceedings{chalkidis-etal-2021-multieurlex,
  author = {Chalkidis, Ilias
and Fergadiotis, Manos
and Androutsopoulos, Ion},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods
in Natural Language Processing},
  location = {Punta Cana, Dominican Republic},
  publisher = {Association for Computational Linguistics},
  title = {MultiEURLEX -- A multi-lingual and multi-label legal document
classification dataset for zero-shot cross-lingual transfer},
  url = {https://arxiv.org/abs/2109.00904},
  year = {2021},
}

SensitiveTopicsClassification

Multilabel classification of sentences across 18 sensitive topics.

Dataset: ai-forever/sensitive-topics-classification • License: cc-by-nc-sa-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	accuracy	rus	Social, Web, Written	human-annotated	found

Citation

@inproceedings{babakov-etal-2021-detecting,
  address = {Kiyv, Ukraine},
  author = {Babakov, Nikolay  and
Logacheva, Varvara  and
Kozlova, Olga  and
Semenov, Nikita  and
Panchenko, Alexander},
  booktitle = {Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing},
  editor = {Babych, Bogdan  and
Kanishcheva, Olga  and
Nakov, Preslav  and
Piskorski, Jakub  and
Pivovarova, Lidia  and
Starko, Vasyl  and
Steinberger, Josef  and
Yangarber, Roman  and
Marci{\'n}czuk, Micha{\l}  and
Pollak, Senja  and
P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
Robnik-{\v{S}}ikonja, Marko},
  month = apr,
  pages = {26--36},
  publisher = {Association for Computational Linguistics},
  title = {Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation},
  url = {https://aclanthology.org/2021.bsnlp-1.4},
  year = {2021},
}

SwedishPatentCPCGroupClassification

This dataset contains historical Swedish patent documents (1885-1972) classified according to the Cooperative Patent Classification (CPC) system at the group level. Each document can have multiple labels, making this a challenging multi-label classification task with significant class imbalance and data sparsity characteristics. The dataset includes patent claims text extracted from digitally recreated versions of historical Swedish patents, generated using Optical Character Recognition (OCR) from original paper documents. The text quality varies due to OCR limitations, but all CPC labels were manually assigned by patent engineers at PRV (Swedish Patent and Registration Office), ensuring high reliability for machine learning applications.

Dataset: atheer2104/swedish-patent-cpc-group-new • License: mit • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to text (t2t)	accuracy	swe	Government, Legal	expert-annotated	found

Citation

@mastersthesis{Salim1987995,
  author = {Salim, Atheer},
  institution = {KTH, School of Electrical Engineering and Computer Science (EECS)},
  keywords = {Multi-label Text Classification, Machine Learning, Patent Classification, Deep Learning, Natural Language Processing, Textklassificering med flera Klasser, Maskininlärning, Patentklassificering, Djupinlärning, Språkteknologi},
  number = {2025:571},
  pages = {70},
  school = {KTH, School of Electrical Engineering and Computer Science (EECS)},
  series = {TRITA-EECS-EX},
  title = {Machine Learning for Classifying Historical Swedish Patents : A Comparison of Textual and Combined Data Approaches},
  url = {https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-368254},
  year = {2025},
}

SwedishPatentCPCSubclassClassification

This dataset contains historical Swedish patent documents (1885-1972) classified according to the Cooperative Patent Classification (CPC) system. Each document can have multiple labels, making this a multi-label classification task with significant implications for patent retrieval and prior art search. The dataset includes patent claims text extracted from digitally recreated versions of historical Swedish patents, generated using Optical Character Recognition (OCR) from original paper documents. The text quality varies due to OCR limitations, but all CPC labels were manually assigned by patent engineers at PRV (Swedish Patent and Registration Office), ensuring high reliability for machine learning applications.

Dataset: atheer2104/swedish-patent-cpc-subclass-new • License: mit • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to text (t2t)	accuracy	swe	Government, Legal	expert-annotated	found

Citation

@mastersthesis{Salim1987995,
  author = {Salim, Atheer},
  institution = {KTH, School of Electrical Engineering and Computer Science (EECS)},
  keywords = {Multi-label Text Classification, Machine Learning, Patent Classification, Deep Learning, Natural Language Processing, Textklassificering med flera Klasser, Maskininlärning, Patentklassificering, Djupinlärning, Språkteknologi},
  number = {2025:571},
  pages = {70},
  school = {KTH, School of Electrical Engineering and Computer Science (EECS)},
  series = {TRITA-EECS-EX},
  title = {Machine Learning for Classifying Historical Swedish Patents : A Comparison of Textual and Combined Data Approaches},
  url = {https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-368254},
  year = {2025},
}

VABBMultiLabelClassification

This dataset contains the fourteenth edition of the Flemish Academic Bibliography for the Social Sciences and Humanities (VABB-SHW), a database of academic publications from the social sciences and humanities authored by researchers affiliated to Flemish universities (more information). Publications in the database are used as one of the parameters of the Flemish performance-based research funding system

Dataset: clips/mteb-nl-vabb-mlcls-pr • License: cc-by-4.0 • Learn more →

Task category	Score	Languages	Domains	Annotations Creators	Sample Creation
text to category (t2c)	f1	nld	Academic, Written	human-annotated	found

Citation

@dataset{aspeslagh2024vabb,
  author = {Aspeslagh, Pieter and Guns, Raf and Engels, Tim C. E.},
  doi = {10.5281/zenodo.14214806},
  publisher = {Zenodo},
  title = {VABB-SHW: Dataset of Flemish Academic Bibliography for the Social Sciences and Humanities (edition 14)},
  url = {https://doi.org/10.5281/zenodo.14214806},
  year = {2024},
}