dawit2123/amharic-hate-speech-detection-using-ML

# Amharic Hate Speech Detection Using Machine Learning ### Model View To view the model: Click Here ### Example of Normal Speech ### Example of Hate Speech --- ## Overview This repository presents a Hate Speech Detection Model for the Amharic language, fine-tuned from the multilingual BERT (mBERT) model. Leveraging the HuggingFace Trainer API, this model is specifically designed to detect hate speech in Amharic with high accuracy and precision. ### Key Features - Fine-tuned mBERT Model: Built on Davlan's bert-base-multilingual-cased-finetuned-amharic from Hugging Face. - HuggingFace Trainer API: Streamlined training and evaluation process. - High Performance: Achieved impressive metrics on a comprehensive dataset. --- ## Model Details ### Model Architecture - Base Model: Davlan's bert-base-multilingual-cased-finetuned-amharic (pretrained multilingual BERT). - Fine-tuned Task: Sequence classification for Amharic hate speech detection. ### Training Parameters - Epochs: 15 - Learning Rate: 5e-5 ### Performance Metrics - F1-Score: 0.9172 - Accuracy: 91.59% --- ## Dataset The model was fine-tuned using a dataset sourced from Mendeley Data. The dataset consists of 30,000 labeled instances, making it one of the most comprehensive datasets for Amharic hate speech detection. ### Dataset Overview - Total Samples: 30,000 - Source: Mendeley Data Repository - Language: Amharic --- ## Installation ### Prerequisites Ensure you have the following installed on your machine: - Python 3.8+ - Jupyter Notebook ### Steps 1. Clone the repository: git clone github.com 2. Navigate to the project directory: cd amharic-hate-speech-detection-using-ML 3. Launch Jupyter Notebook: jupyter notebook 4. Open and run the notebook file: Hate_speech_detection_using_amharic_language.ipynb --- ## Model Usage To use this model for Amharic hate speech detection, you can follow the steps in the Google Colab notebook to l …