🌍Pan-African

AfriBERTa

AI

AfriBERTa is a multilingual masked language model (XLM-RoBERTa architecture, ~126M params) pretrained from scratch on 11 African languages including Amharic, Hausa, Igbo, Swahili, and Yoruba. Built by the Castorini lab (University of Waterloo) for text classification and Named Entity Recognition on low-resource African languages.

AfriTeVa V2

AI

An improved T5 v1.1 model (428M params) pretrained on the Wura corpus covering 16 African languages, with gains on classification, translation, summarization and cross-lingual QA. Published at EMNLP 2023 by the Castorini group with African lead authors.

AfroLID

AI

A neural language identification toolkit that detects which of 517 African languages and varieties a text belongs to across 14 language families, reaching 97.41 macro-F1 after fine-tuning on SERENGETI. Developed by the UBC Deep Learning and NLP Lab and published at EMNLP 2022.

AfroLM

AI

A multilingual masked language model pretrained from scratch on 23 African languages using a self-active learning framework, outperforming AfriBERTa, mBERT and XLMR-base on NER and sentiment tasks. Created by Bonaventure Dossou and collaborators, published at SustaiNLP/EMNLP 2022.

AfroXLMR

AI

AfroXLMR is an XLM-R-large model (0.6B params) adapted to African languages via multilingual adaptive fine-tuning, covering 17 African languages plus Arabic, French, and English. Created by David Adelani (Davlan) and published at COLING 2022 for cross-lingual transfer tasks like NER.

BibleTTS

AI

BibleTTS is a large, high-fidelity open text-to-speech corpus with up to 80+ hours of studio-quality 48kHz single-speaker recordings per language across ten Sub-Saharan African languages (Akuapem Twi, Asante Twi, Chichewa, Ewe, Hausa, Kikuyu, Lingala, Luganda, Luo, Yoruba), built by Masakhane/Coqui.

Cheetah

AI

A massively multilingual natural language generation model supporting 517 African languages, outperforming baselines on five of seven AfroNLG tasks like summarization and translation. Developed by the UBC Deep Learning and NLP Lab and published at ACL 2024.

Deep Learning Indaba

AI

Pan-African grassroots movement strengthening machine learning and AI across the continent through an annual conference, locally-run IndabaX events, mentorship and an Ideathon. The 2026 Indaba is hosted at Pan-Atlantic University in Lagos, Nigeria.

SERENGETI

AI

A massively multilingual masked language model covering 517 African languages and varieties across five scripts, achieving state-of-the-art results on the AfroNLU benchmark. Developed by the UBC Deep Learning and NLP Lab as an Afrocentric resource.

Toucan

AI

An Afrocentric many-to-many machine translation model (1.2B params, mT5) fine-tuned from Cheetah to support 156 African language pairs, evaluated on the AfroLingu-MT benchmark. Developed by the UBC Deep Learning and NLP Lab and published at ACL 2024.

Zindi

AI

Africa's largest data science and AI competition platform where organizations host real-world ML challenges and a community of builders competes to solve them. Offers competitions, learning courses, jobs and leaderboards, with partners including Microsoft, Google, AWS and Google DeepMind.

AfriCOMET

AI

AfriCOMET is a COMET-based machine translation evaluation model for African languages, scoring translation quality from source, hypothesis and reference triplets. The STL-1.1 version uses the afro-xlmr-large-76L encoder and was validated in the WMT 2024 Metrics Shared Task across 13 African-centric language pairs. It is released by the Masakhane community with reference-based and quality-estimation variants.

AfriHuBERT

AI

AfriHuBERT is a compact self-supervised speech representation model based on mHuBERT-147, continually pretrained via multilingual adaptive finetuning on over 10,000 hours of speech spanning more than 1,200 African languages and varieties. It improves spoken language identification and ASR over its base model and acts as an encoder for downstream African speech tasks. Its training data was aggregated from sources including BibleTTS, Kallaama, NaijaVoices and NCHLT.

AfriMT5

AI

AfriMT5 (afri-mt5-base) is an mT5-based machine translation model from the Masakhane community fine-tuned to translate across 16 African languages using multilingual adaptive fine-tuning. It targets news-domain translation for low-resource African languages, several of which were not previously covered by existing benchmarks. It is distributed as open weights on HuggingFace.

IrokoBench

AI

IrokoBench is a human-translated evaluation benchmark for 16 typologically diverse African languages covering three tasks: natural language inference (AfriXNLI), knowledge QA (AfriMMLU) and mathematical reasoning (AfriMGSM). It is widely used to measure the performance gap between English and African languages in large language models. It was released by the Masakhane community and published at NAACL 2025.

Lugha-Llama

AI

Lugha-Llama is a Llama-3.1-8B model continually pretrained on the WURA African-language corpus to lift performance on low-resource African languages. It ships in three variants (wura, wura_edu, wura_math) and reaches leading results among similarly sized models on the IrokoBench and AfriQA African-language benchmarks. It was built by researchers at Princeton University.

NileChat-3B

AI

NileChat-3B is a 3B-parameter LLM built on Qwen2.5-3B and adapted for Egyptian and Moroccan communities, handling Egyptian and Moroccan dialectal Arabic in both Arabic script and Arabizi alongside Modern Standard Arabic, French and English. It was trained with controlled synthetic data and translated corpora and outperforms its Qwen2.5-3B baseline on Arabic benchmarks. It was released by the UBC Deep Learning and NLP Lab and accepted at EMNLP 2025.