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

# Amharic Hate Speech Detection Using Machine Learning ### Example of Normal Speech >>>>>> 5973de328f0338716adeda7c925017ddf2cf2675 --- ## 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: ```bash git clone github.com ``` 2. Navigate to the project directory: ```bash cd amharic-hate-speech-detection-using-ML ``` 3. Launch Jupyter Notebook: ```bash 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 de …