NjorogeGodwin/Machine-Language-Translator-model-1

This is a model that translates from Kikuyu to kiswahili # Machine-Language-Translator-model-1 This is a model that translates from Kikuyu to kiswahili Kikuyu-Kiswahili Translation Project Project Overview This project contains a comprehensive dataset of translations in Kikuyu and Kiswahili languages. The dataset is structured to support machine learning and natural language processing tasks, particularly for low-resource African languages. Dataset Contents The repository contains the following key files: - kikuyu-train-data.txt: Complete text in Kikuyu language - kikuyu-train-data.csv: Structured version of the Kikuyu text with additional formatting - kiswahili-train-data.txt: Complete text in Kiswahili language - Kiswahili-train-data.csv: Structured version of the Kiswahili text with additional formatting Data Format The data is provided in two formats: 1. Plain text (.txt) - Contains the raw text of the translations 2. CSV format (.csv) - Structured format with potential for additional metadata Potential Applications This dataset can be used for: - Machine translation between Kikuyu and Kiswahili - Natural language processing for low-resource African languages - Linguistic analysis and comparison between related Bantu languages - Development of language models for Kikuyu and Kiswahili - Text generation and language understanding tasks Dataset Characteristics - Languages: Kikuyu (Gĩkũyũ) and Kiswahili (Swahili) - Content: Biblical texts and numbers and other several words - Kikuyu text: ~3,474 lines - Kikuyu CSV: ~3,499 lines - Kiswahili text: ~3,444 lines Technical Information The dataset is organized in a parallel structure, allowing for alignment between the two languages. The texts appear to be organized by biblical books, chapters, and verses, making it suitable for parallel corpus development. Getting Started To use this dataset for machine learning: 1. Load the text files using standard text processing libraries 2. Preprocess the text as needed (tokenization, normalization) 3. For parallel corpus …