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title: README
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🌍 Lark AI Community
Welcome to Lark, an open and collaborative AI community focused on building equitable, inclusive, and impactful Artificial Intelligence systems for Africa.
We are a research-driven, interdisciplinary initiative dedicated to solving local challenges across medicine, education, content creation, finance, and marketing, while also contributing cutting-edge models and datasets to the global AI ecosystem.
🚀 Mission
Our mission is to advance African-centered AI by:
- Developing domain-specific foundation models and lightweight architectures for low-resource settings.
- Creating and curating clean, scalable, multilingual datasets relevant to African languages, cultures, and industries.
- Empowering researchers, developers, and organizations through open collaboration, training resources, and accessible tools.
📦 Lark Model Series
The Lark Model Series is a family of models released in iterative versions, fine-tuned and pre-trained for applications in the African context.
| Version | Model Type | Domains | Highlights |
|---|---|---|---|
| Lark-1 | Transformer Encoder (BERT-style) | Healthcare NLP | Trained on annotated clinical notes & med-tech literature from African institutions |
| Lark-2 | Multimodal (Text + Image) | Education, Content Creation | Capable of generating localized educational materials and multilingual content |
| Lark-3 | Financial Forecasting Models | Finance, Economics | Built on macro-financial datasets from African markets |
| Lark-4 | LLM (GPT-style) | General Purpose | Fine-tuned on African conversational data, news, literature, and public documents |
Each model is accompanied by:
- 🧾 Model Cards
- 📊 Evaluation Benchmarks
- ⚖️ Responsible AI Documentation
- 💡 Inference & Fine-tuning Notebooks
📚 Datasets
Lark is committed to the ethical acquisition and distribution of high-quality datasets. Our data pipeline includes:
- Data Sourcing: Web scrapes, public records, multilingual corpora, domain-specific archives, with regional legal clearance
- Cleaning & Filtering: Deduplication, de-identification (PII removal), language detection, quality scoring
- Annotation: Manual + semi-automated labeling workflows using Label Studio, Prodigy, and Hugging Face Datasets
We follow the Data Nutrition Labels and Open Data Commons licensing principles.
Current Releases
lark-med-corpus: A multilingual medical dataset for clinical NLP (Swahili, Yoruba, Amharic, Hausa)lark-edu-textbooks: African education corpora (K–12 curriculum, localized pedagogy)lark-financial-news: Economic and financial news data scraped from African business publications
🧠 Research Focus Areas
We are actively researching:
- Multilingual NLP for underrepresented African languages
- Domain-specific model pretraining (e.g., biomedical, financial LMs)
- Few-shot and low-resource adaptation
- Multimodal learning (text + images + voice)
- Responsible and explainable AI tailored to African legal/ethical frameworks
🤝 How to Contribute
We welcome contributions across domains — research, data, engineering, documentation, or advocacy.
Get Started
- Join the Community
- Hugging Face: Lark Community
- Discord/Slack (link placeholder)
- Explore Open Issues
- Models: issues/models
- Datasets: issues/datasets
- Contribute Code or Data
- Fork → Create Branch → PR
- Add your name to
CONTRIBUTORS.md
Guidelines
- Follow our Contribution Guidelines
- Review Ethical AI & Data Use Policy
- Read Model Documentation Standards
🌐 Partners & Supporters
We collaborate with:
- African research labs and universities
- NGOs and health organizations
- EdTech platforms
- FinTech and civic tech startups
- Global open-source communities
If you’re an organization interested in partnering, supporting, or funding Lark, please contact us.
📅 Roadmap
| Quarter | Milestone |
|---|---|
| Q2 2025 | Release Lark-1 + lark-med-corpus |
| Q3 2025 | Launch Multilingual Benchmark Suite (Swahili, Hausa, Amharic, Igbo) |
| Q4 2025 | Lark-2 (Multimodal) + Open Fine-Tuning Platform |
| 2026+ | Regional AI Bootcamps, Dataset Expansion, Deployment Tools |
📄 License
All models and datasets are licensed under:
- Models: Apache 2.0 License
- Datasets: ODC-BY or CC BY 4.0 depending on source
Please check individual model cards or dataset pages for more.
✨ Acknowledgments
We thank the growing Lark community — researchers, students, contributors, and institutions — for your trust and energy. This is just the beginning of building AI by Africa, for Africa.
📫 Contact Us: larkai@protonmail.com
🐦 Twitter/X: @LarkAI_Africa (placeholder)
🧪 Hugging Face Hub: https://huggingface.co/Lark