MasakhaNEWSClassification

An MTEB dataset

Massive Text Embedding Benchmark

MasakhaNEWS is the largest publicly available dataset for news topic classification in 16 languages widely spoken in Africa. The train/validation/test sets are available for all the 16 languages.

Task category	t2c
Domains	News, Written
Reference	https://arxiv.org/abs/2304.09972

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(["MasakhaNEWSClassification"])
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.

@misc{adelani2023masakhanews,
  archiveprefix = {arXiv},
  author = {David Ifeoluwa Adelani and Marek Masiak and Israel Abebe Azime and Jesujoba 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 al-azzawi and Blessing Sibanda and Davis David and Lolwethu Ndolela and Jonathan Mukiibi and Tunde Ajayi and Tatiana Moteu and Brian Odhiambo and Abraham Owodunni and Nnaemeka Obiefuna and Muhidin Mohamed and Shamsuddeen Hassan Muhammad and Teshome Mulugeta Ababu and Saheed Abdullahi Salahudeen and Mesay Gemeda Yigezu and Tajuddeen Gwadabe and Idris Abdulmumin and Mahlet Taye and Oluwabusayo Awoyomi and Iyanuoluwa Shode and Tolulope Adelani and Habiba Abdulganiyu and Abdul-Hakeem Omotayo and Adetola Adeeko and Abeeb Afolabi and Anuoluwapo Aremu and Olanrewaju Samuel and Clemencia Siro and Wangari Kimotho and Onyekachi Ogbu and Chinedu Mbonu and Chiamaka Chukwuneke and Samuel Fanijo and Jessica Ojo and Oyinkansola Awosan and Tadesse Kebede and Toadoum Sari Sakayo and Pamela Nyatsine and Freedmore Sidume and Oreen Yousuf and Mardiyyah Oduwole and Tshinu Tshinu and Ussen Kimanuka and Thina Diko and Siyanda Nxakama and Sinodos Nigusse and Abdulmejid Johar and Shafie Mohamed and Fuad Mire Hassan and Moges Ahmed Mehamed and Evrard Ngabire and Jules Jules and Ivan Ssenkungu and Pontus Stenetorp},
  eprint = {2304.09972},
  primaryclass = {cs.CL},
  title = {MasakhaNEWS: News Topic Classification for African languages},
  year = {2023},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, 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

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("MasakhaNEWSClassification")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 6242,
        "number_of_characters": 16946423,
        "number_texts_intersect_with_train": 66,
        "min_text_length": 1,
        "average_text_length": 2714.9027555270745,
        "max_text_length": 26369,
        "unique_text": 6234,
        "unique_labels": 7,
        "labels": {
            "business": {
                "count": 785
            },
            "health": {
                "count": 1258
            },
            "politics": {
                "count": 1589
            },
            "sports": {
                "count": 1265
            },
            "entertainment": {
                "count": 762
            },
            "technology": {
                "count": 297
            },
            "religion": {
                "count": 286
            }
        }
    },
    "train": {
        "num_samples": 21734,
        "number_of_characters": 58485151,
        "number_texts_intersect_with_train": null,
        "min_text_length": 1,
        "average_text_length": 2690.952010674519,
        "max_text_length": 46502,
        "unique_text": 21591,
        "unique_labels": 7,
        "labels": {
            "sports": {
                "count": 4401
            },
            "business": {
                "count": 2725
            },
            "health": {
                "count": 4384
            },
            "politics": {
                "count": 5555
            },
            "entertainment": {
                "count": 2654
            },
            "technology": {
                "count": 1029
            },
            "religion": {
                "count": 986
            }
        }
    }
}

---

This dataset card was automatically generated using MTEB