EricPeter/Topic-Classification-Luganda

# Text Classification Using Embeddings for Luganda Text Corpus This repository contains code for a text classification experiment using embeddings for the Luganda text corpus. In this experiment, we utilized Support Vector Machine (SVM) as the classification model and performed hyper-parameter tuning to optimize its performance. Due to limitations imposed by the Cohere API for free users, the dataset was sampled to 500 samples for training and evaluation purposes. ## Experiment Details - **Model:** Support Vector Machine (SVM) - **Data:** Luganda Text Corpus - **Data Size:** 500 samples (due to API limitations) - **Embeddings:** Utilized embeddings for text representation - **Validation Accuracy:** 45.6% ## Experiment Process 1. **Data Sampling:** Due to limitations in the Cohere API for free users, the Luganda text corpus was sampled to 500 data points for the experiment. 2. **Text Embeddings:** Text data was converted into embeddings to enable numerical representation for the SVM model. Embeddings play a crucial role in capturing semantic meanings of words and phrases, enhancing the model's understanding of the text. 3. **Model Selection:** SVM was chosen as the classification model due to its effectiveness in text classification tasks and its ability to handle high-dimensional data like embeddings. 4. **Hyper-parameter Tuning:** Hyper-parameter tuning was performed to optimize the SVM model's performance. Various hyper-parameters such as C, kernel type, and gamma were tuned to achieve the best results. 5. **Training and Evaluation:** The model was trained on the sampled dataset and evaluated using validation data. The validation accuracy obtained was 45.6%, indicating the model's ability to correctly classify Luganda text samples.