yordanoswuletaw/amharic-cbow-word-embedding

Amharic Langauge Word Embedding with CBOW, Implemented in Python and NumPy from Scratch ## Amharic CBOW Word Embedding Model This project implements the Continuous Bag-of-Words (CBOW) word embedding model for the Amharic and other languages using Python and Numpy. It learns dense vector representations of Amharic words, useful for downstream NLP tasks like text classification, sentiment analysis, and language modeling. ### Features - **CBOW-Based Word Embeddings**: Uses the Continuous Bag-of-Words approach to predict context words given a target word. - **Amharic Language Support**: Specifically designed for Amharic's structure and morphology. - **Pre-trained Embeddings**: Provides pre-trained embeddings on large Amharic corpora. - **Tokenization**: Implements an Amharic-specific tokenizer for proper word segmentation. - **Embeddings Visualization**: Supports 2D visualization of word embeddings for better exploration of word relationships. - **Scalable Design**: Trains on large Amharic corpora for higher-quality embeddings. ### Installation 1. Clone this repository: ```bash git clone github.com ``` 2. Navigate to the project directory: ```bash cd amharic-cbow-word-embedding ``` 3. Install the required dependencies: ```bash pip install -r requirements.txt ``` ## Repository Structure ```plaintext ├── .vscode/ │ └── settings.json # VS Code settings for environment setup ├── .github/ │ └── workflows/ │ ├── unittests.yml # CI/CD pipeline for unit tests ├── .gitignore # Ignored files and folders ├── requirements.txt # Dependencies for the project ├── README.md # Documentation of the repository ├── data/ # Dataset for training, dev, and testing ├── src/ # Source code for training and processing ├── notebooks/ │ ├── __init__.py # Package initialization │ └── README.md # Documentation for the notebooks ├── tests/ │ ├── __init__.py …