Here’s a professional description for your GitHub repository:
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### EthioMart-LLM-Finetuning: Amharic NLP for E-commerce Integration
This repository contains the development and fine-tuning of large language models (LLMs) for Amharic Named Entity Recognition (NER) to support EthioMart’s vision of becoming the central hub for Telegram-based e-commerce in Ethiopia.
The project addresses challenges in decentralized e-commerce by:
- Extracting key business entities such as product names, prices, and locations from text, images, and documents shared across Telegram channels.
- Populating EthioMart's centralized database with real-time, structured data to create a seamless, unified platform for customers and vendors.
### Key Features:
- **Fine-tuned NLP Models**: Tailored LLMs optimized for Amharic NER tasks.
- **Multimodal Data Processing**: Extracts entities from text, images, and documents.
- **Real-Time Data Consolidation**: Centralizes data from multiple e-commerce Telegram channels.
- **E-commerce Focus**: Enables seamless product discovery, order placement, and vendor interaction.
This repository showcases advanced techniques in natural language processing, particularly fine-tuning transformer models for low-resource languages like Amharic, and demonstrates their application in real-world e-commerce scenarios.
### Tech Stack:
- **Languages**: Python
- **Libraries/Frameworks**: PyTorch, Hugging Face Transformers, SpaCy
- **Models**: BERT, mT5, or other Amharic-supporting architectures
### Use Cases:
- E-commerce platform enhancement
- Named Entity Recognition for low-resource languages
- Real-time data integration
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