Gemechu90/EthioMart-Amharic-NER

# Telegram-ECommerce-NER ## Overview This project aims to develop a Named Entity Recognition (NER) system for EthioMart, a centralized e-commerce platform in Ethiopia. The system will extract key business entities such as product names, prices, and locations from Amharic text, images, and documents shared across multiple Telegram channels. ## Business Need EthioMart's vision is to become the primary hub for all Telegram-based e-commerce activities in Ethiopia. By consolidating real-time data from multiple e-commerce Telegram channels, EthioMart aims to provide a seamless experience for customers to explore and interact with multiple vendors in one place. ## Key Objectives 1. Real-time data extraction from Telegram channels 2. Fine-tuning Large Language Models (LLMs) for Amharic Named Entity Recognition 3. Extraction of entities such as Product names, Prices, and Locations ## Project Structure The project is divided into the following main tasks: 1. Data Ingestion and Preprocessing 2. Data Labeling in CoNLL Format 3. Fine-tuning NER Models 4. Model Comparison and Selection 5. Model Interpretability ## Folder Structure ```plaintext Telegram-ECommerce-NER/ ├── .vscode/ │ └── settings.json ├── .github/ │ └── workflows/ │ └── unittests.yml # GitHub Actions ├── .gitignore # files and folders to be ignored by git ├── requirements.txt # contains dependencies for the project ├── README.md # Documentation for the projects ├── src/ │ └── __init__.py ├── notebooks/ │ ├── __init__.py | |──preprocessing_analysis.ipynb # Jupyter notebook for amharic data processing | |──ner_labelling.ipynb # Jupyter notebook to label amharic tokens in conll format | |──ner_finetuning.ipynb # Jupyter notebook to fine-tune a Named Entity Recognition (NER) model | |──model_comparison.ipynb # Jupyter notebook to compares different models for Named Entity Recognition (NER) | |──qenashcom_sin …