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metadata
title: Swahili Text Model
emoji: 🌍
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
pinned: false

Swahili Content Generation

This Hugging Face Space provides an AI-powered tool for generating educational content in Swahili for primary school students in grades 3 and 4. The system uses a Retrieval-Augmented Generation (RAG) approach to create accurate and contextually relevant educational materials.

Features

  • Generate educational content for Math and Science subjects
  • Support for grades 3 and 4
  • Three different content styles: normal, simple, and creative
  • RAG-based approach for accurate and contextually relevant content
  • Easy-to-use interface

How to Use

  1. Select the grade level (3 or 4)
  2. Choose the subject (Math or Science)
  3. Enter a topic (e.g., "namba", "mazingira", "vipimo", etc.)
  4. Select a style (normal, simple, or creative)
  5. Click "Generate Content"

Available Topics

Grade 3 Science

  • mazingira (Environment)
  • nishati (Energy)
  • maada (Matter)
  • mawasiliano (Communication)
  • usafi (Cleanliness)
  • vipimo (Measurements)
  • mlo (Nutrition)
  • mfumo (Systems)
  • maambukizi (Infections)
  • huduma (Services)
  • vifaa (Equipment)

Grade 4 Science

  • kinga (Immunity)
  • magonjwa (Diseases)
  • majaribio (Experiments)
  • maji (Water)
  • ukimwi (HIV/AIDS)
  • huduma (Services)
  • mazingira (Environment)
  • nishati (Energy)
  • mfumo (Systems)
  • mawasiliano (Communication)

Grade 3 Math

  • namba (Numbers)
  • mpangilio (Arrangement)
  • matendo (Operations)
  • sehemu (Fractions)
  • maumbo (Shapes)
  • vipimo (Measurements)
  • fedha (Money)
  • takwimu (Statistics)

Grade 4 Math

  • kugawanya (Division)
  • kujumlisha (Addition)
  • kuzidisha (Multiplication)
  • namba (Numbers)
  • kirumi (Roman Numerals)
  • wakati (Time)
  • mpangilio (Arrangement)
  • vipimo (Measurements)
  • takwimu (Statistics)
  • kutoa (Subtraction)
  • fedha (Money)
  • sehemu (Fractions)
  • maumbo (Shapes)

Technical Details

This application uses:

  • Meta's Llama-3.2-3B-Instruct model for text generation
  • FAISS for efficient vector similarity search
  • SentenceTransformers for text embeddings
  • FastAPI for the backend API
  • Gradio for the user interface

The system employs a Retrieval-Augmented Generation (RAG) approach, which enhances the language model's output by retrieving relevant information from a curated database of educational materials in Swahili.