epythonlab/amharic-telegram-ecommerce-entity-extraction

This project builds a real-time system to extract product names, prices, and locations from Amharic e-commerce Telegram channels. Using fine-tuned LLMs for Named Entity Recognition (NER), the extracted data will populate EthioMart’s centralized platform, simplifying e-commerce in Ethiopia by consolidating various channels into one hub. # Entity Extraction for Amharic E-commerce Telegram Channels using LLM Fine-Tuning This project focuses on building a real-time data ingestion and entity extraction pipeline for Amharic messages from Ethiopian e-commerce Telegram channels. The system leverages fine-tuned Large Language Models (LLMs) to identify key business entities such as product names, prices, and locations. The extracted information is used to populate a centralized platform for EthioMart, aiming to streamline e-commerce activities in Ethiopia by consolidating decentralized Telegram channels into a unified hub. The project also includes handling Amharic-specific linguistic features and evaluating model performance for Named Entity Recognition (NER). ## Project directory strucutres The repository is organized into the following directories: .`github/workflows`: Contains GitHub workflow configurations for continuous integration. .`vscode`: Configuration files for the Visual Studio Code editor. `fonts`: Contains files related to analyzing the most common Amharic words in telegram e-commerce messages. `notebooks`: Jupyter notebooks that were used for data exploration, preprocessing, and labeling tasks. `scripts`: Python scripts used for data scraping, preprocessing, and implementing the NER labeling logic. `tests`: Contains test cases for different parts of the project. ## Installation Instructions To run the project locally, follow these steps: Clone the Repository: `git clone github.com` `cd amharic-telegram-ecommerce-entity-extraction` Set up the Virtual Environment: `python3 -m venv .venv` `source .venv/bin/activate # For Windows: .venv\Scripts\activate` Install Dependencies: `pip install -r requirements.txt`