Highlight of Some NLP Models That Are Powering African Language Technology
Introduction
African technologists and communities are working to put the continent’s 2,000+ languages on the global map through innovative NLP models and tools. These efforts have improved the accuracy of African language identification, enabled the development of stronger multilingual systems such as Serengeti and Cheetah, and demonstrated the potential of locally built solutions across sectors.
A striking example is Jacaranda Health, which created its own models using AforLLaMA to power a maternal health chatbot that responds to queries in local languages. Such breakthroughs show how African-led initiatives are transforming customer service, machine translation, sentiment analysis, and other language technology applications worldwide.
Purpose of the guide
This guide highlights key African NLP models and tools to help researchers and technologists like you discover what already exists. It also explains how these resources can be applied in your own projects and where you can find opportunities to collaborate with the communities that built them.
1. AfroLID – Neural Language Identification for 517 African Languages
AfroLID is a neural language identification (LID) model was trained to recognise 517 African languages and dialects across 14 language families and spanning 5 orthographic systems ( Latin, Arabic, Ge'ez, N'Ko, Tifinagh).
Performance:
95.89% F₁-score on blind test sets outperforming existing language ID tools.
Usage:
Data Filtering & Cleanup: Automatically spot and separate African language text from noisy web dumps like Common Crawl.
Corpus Mining: Identify and collect language-specific datasets (e.g., Yoruba tweets) to train language-specific models.
Pipeline Routing: Preprocessing for multilingual NLP pipelines to detect input language.
Practical Applications
Emergency Response Monitoring: NGOs could scan local-language social media messages (e.g., Swahili, Amharic) during crises, enabling real-time alerts and faster humanitarian action.
Digital Library Indexing: Cultural organisations automatically tag archived documents or oral transcripts by language, aiding search and preservation.
Training Afro-centric LLMs: Training and improving African language models like Serengeti or Cheetah (hypothetical future LLMs focusing on African languages), improving their language coverage and reducing contamination from non-target languages.
2. AfriHuBERT – Self-Supervised Speech for African Languages
A self-supervised speech model pretrained on over 10,000 hours of speech data spanning 1,226 indigenous African languages and dialects, as well as Arabic, English, French, and Portuguese.
Performance:
+4 % average F₁-score improvement on Language Identification (LID).
–1.2 % average WER reduction for ASR.
Usage:
Fine-tune for LID in speech interfaces—detecting the language being spoken in multilingual voice systems.
Pretraining backbone for low-resource ASR models in African languages, using limited labelled data.
Practical Applications:
Multilingual Voice Assistants: Automatically detect languages and transcribe speech (e.g., Yoruba versus Hausa) to respond to the user in that language.
Accessibility: Accessibility tools for voice-based interaction in indigenous languages.
Broadcast Monitoring: Media organisations tracking radio broadcasts across several African languages.
3. AfroXLMR – XLM-R Adapted for African Languages
AfroXLMR adapts XLM-RoBERTa for 17 African languages and three global languages (English, French, Arabic). It achieves NER F₁ scores between 76.1% and 91.2%, leveraging reduced vocabulary size and adaptive fine-tuning for both low- and high-resource languages.
Performance:
AfroXLMR-Large vs XLM-R-Large for NER (F1): average from ~80.8 - 83.4; per-language gains up to 6.3 points.
AfroXLMR-Base achieves F1 from 69.5% to 91.4% across languages; e.g., Amharic ~76.1, Hausa ~91.2, Yoruba ~82.1
Reduces vocabulary token size and uses multilingual adaptive fine-tuning to maintain strong performance across low- and high-resource languages.
Usage:
Named Entity Recognition (NER) in African languages (e.g., identifying names, locations, organisations).
Cross-lingual transfer for downstream tasks, e.g. sentiment analysis, topic classification, with better representation for African languages.
Practical Applications:
News Analytics for African Markets: Automatically extract entities (e.g., people, places) from news articles in Hausa, Yoruba, and Swahili, aiding media monitoring.
Customer Support Triage: Analyse multilingual support tickets—detect language, extract intents/entities, and route to appropriate teams.
Financial Document Processing: For KYC/AML, extract names and organisations from documents or applications in African languages, improving compliance workflows.
4. Dziribert Transformer-based model for Dziri
Dziribert is a Transformer-based language model specifically pre-trained for Dziri (Algerian Arabic dialect), supporting both Arabic and Latin scripts.
Performance:
Emotion classification: Macro-F1 ≈ 78.5%, Accuracy ≈ 82.2%.
Topic classification: Macro-F1 ≈ 83.0%, Accuracy ≈ 85.6%.
Usage:
Sentiment and Emotion Detection in Algerian Social Media and Messaging/Customer Feedback.
Topic Classification for categorising local news, online content and comments.
Natural language inference for semantic understanding.
Practical Applications:
Political Sentiment Tracking: Governments or researchers analyze public emotion in Dziri tweets during elections.
Brand Monitoring: Companies gauge customer sentiment in Algerian dialect across social media.
Moderation Tools: Platforms identify toxic or off-topic content in user-generated Algerian Arabic text for better community safety.
5. Irokobench Benchmark for LLMs
Irokobench is a human-translated benchmark used for evaluating the performance of large language models on 16 African languages. It covers 3 reasoning tasks:
AfriXNLI (natural language inference)
AfriMGSM (evaluation dataset)
AfriMMLU (multi-choice knowledge QA Research)
Usage:
Benchmarking large language models on African languages.
Evaluating reasoning tasks like natural language inference and math problem solving.
Testing sentiment analysis and language classification capabilities.
Practical Applications:
Selecting LLMs for Public Service Bots: Choose the best model to power multilingual chatbots capable of reasoning in Swahili, Yoruba, or Hausa.
Academic Research: Study performance gaps in NLI or math reasoning across African languages for targeted model improvements.
Customised LLM Fine-tuning: Identify weaknesses via IrokoBench and then fine-tune a model specifically for tasks like knowledge QA in African languages.
Multilingual transformer (BERT)
~11 African languages
Text classification, NER, sentiment, and intent detection
AfriHuBERT
Multilingual transformer (HuBERT variant)
Speech data from several African languages
ASR fine-tuning
Text-Vision Alignment model
African languages with images
Cross-modal retrieval, image captioning in local languages
Afro-XLM-R
Multilingual transformer (XLM-R variant)
50+ African & global languages
Cross-lingual transfer, translation, classification
Language Identification
50+ African languages
Data cleaning, routing, multilingual chatbots
AfroLM
Large Language Model
Dozens of African languages
General text generation and understanding tasks
AfroLlama3
8B-parameter LLM based on Llama3
Swahili, Xhosa, Zulu, Yoruba, Hausa, English
Question answering, domain assistants (health, agriculture, legal, education, tourism, commerce)
Cheetah
ASR system
African speech corpora
Real-time speech-to-text in courts, health broadcasts
GlotLID / GlotScript / GlotCC
Toolkit (LID, script detection, corpus cleaning)
2,000+ languages incl. African
Pre-processing pipelines, dataset curation
InkubaLM
Language Model
Southern African languages (e.g. isiZulu, Sesotho)
Sentiment Analysis, General NLP, education tech
LIG-Aikuma
Mobile app & corpus tool
Field data collection for endangered languages
Crowd-sourced speech/text collection
SwahBERT
BERT-style model
Kiswahili
Search, QA, customer support bots
Toucan
Speech synthesis & ASR
Multiple African languages
TTS/ASR for low-resource languages
African-focused LLM
Kiswahili
Conversational agents and domain-specific assistants for African languages
About Lanfrica Labs
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