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--- |
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title: Swahili Text Model |
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emoji: 🌍 |
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colorFrom: blue |
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colorTo: green |
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sdk: docker |
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app_port: 7860 |
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pinned: false |
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--- |
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# Swahili Content Generation |
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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. |
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## Features |
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- Generate educational content for Math and Science subjects |
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- Support for grades 3 and 4 |
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- Three different content styles: normal, simple, and creative |
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- RAG-based approach for accurate and contextually relevant content |
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- Easy-to-use interface |
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## How to Use |
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1. Select the grade level (3 or 4) |
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2. Choose the subject (Math or Science) |
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3. Enter a topic (e.g., "namba", "mazingira", "vipimo", etc.) |
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4. Select a style (normal, simple, or creative) |
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5. Click "Generate Content" |
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## Available Topics |
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### Grade 3 Science |
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- mazingira (Environment) |
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- nishati (Energy) |
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- maada (Matter) |
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- mawasiliano (Communication) |
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- usafi (Cleanliness) |
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- vipimo (Measurements) |
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- mlo (Nutrition) |
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- mfumo (Systems) |
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- maambukizi (Infections) |
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- huduma (Services) |
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- vifaa (Equipment) |
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### Grade 4 Science |
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- kinga (Immunity) |
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- magonjwa (Diseases) |
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- majaribio (Experiments) |
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- maji (Water) |
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- ukimwi (HIV/AIDS) |
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- huduma (Services) |
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- mazingira (Environment) |
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- nishati (Energy) |
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- mfumo (Systems) |
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- mawasiliano (Communication) |
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### Grade 3 Math |
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- namba (Numbers) |
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- mpangilio (Arrangement) |
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- matendo (Operations) |
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- sehemu (Fractions) |
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- maumbo (Shapes) |
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- vipimo (Measurements) |
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- fedha (Money) |
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- takwimu (Statistics) |
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### Grade 4 Math |
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- kugawanya (Division) |
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- kujumlisha (Addition) |
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- kuzidisha (Multiplication) |
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- namba (Numbers) |
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- kirumi (Roman Numerals) |
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- wakati (Time) |
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- mpangilio (Arrangement) |
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- vipimo (Measurements) |
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- takwimu (Statistics) |
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- kutoa (Subtraction) |
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- fedha (Money) |
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- sehemu (Fractions) |
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- maumbo (Shapes) |
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## Technical Details |
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This application uses: |
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- Meta's Llama-3.2-3B-Instruct model for text generation |
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- FAISS for efficient vector similarity search |
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- SentenceTransformers for text embeddings |
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- FastAPI for the backend API |
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- Gradio for the user interface |
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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. |
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