Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Nix-ged/VeriAfrica

Domaine:

natural language processing

Type de record:

software
Créateur:
Nix
Hôte:
Global news, verified for Africa – AI-powered fact-checking app # VeriAfrica Global news, verified for Africa — AI-powered fact-checking and misinformation analysis. ## Project Overview VeriAfrica is a mobile-first app designed to provide African users with verified news and insights. Features include: - Africa Now: verified local news - Global Pulse: curated global news with African context - Viral Claims: trending X/Twitter claims with AI verification - Fact Check: AI-powered claim analysis - Opportunities: scholarships, programs, fellowships ## Tech Stack - Frontend: Windsurf (low-code) - Backend: Serverless APIs / AI scripts - AI/NLP: OpenAI API for summarization and verification - Database: Windsurf collections or serverless DB ## Contribution - Clone the repo, create feature branches, submit pull requests - Issues and feature requests can be tracked here on GitHub ## License [MIT License] (or whichever license you choose)

Visit

github.com

Licenses

MIT

Similaires

bruno-nix/Agri-Smart-RwandaImpact of Source Language Diversity on Cross-Lingual Transferability in Multilingual GED ModelsMorphological Complexity in Synthetic Data and Zero-Shot GED Performance on Agglutinative LanguagesRobustness Analysis of Zero-Shot Cross-Lingual GED Models on Low-Resource Languages

bruno-nix/Agri-Smart-Rwanda

# Agri-Smart Rwanda Agri-Smart Rwanda is an innovative web application designed to empower local fa

Impact of Source Language Diversity on Cross-Lingual Transferability in Multilingual GED Models

Grammatical Error Detection (GED) methods rely heavily on human annotated error corpora. However, th

Morphological Complexity in Synthetic Data and Zero-Shot GED Performance on Agglutinative Languages

Grammatical Error Detection (GED) methods rely heavily on human annotated error corpora. However, th

Robustness Analysis of Zero-Shot Cross-Lingual GED Models on Low-Resource Languages

Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potentia