Husnafazal/english-amharic-translation

# English to Amharic Translation Model This project aims to build a neural machine translation model to translate text from English to Amharic using Recurrent Neural Networks (RNNs). ## Language Selection The target language for translation is Amharic. ## Dataset Preparation ### Dataset Quality The dataset contains English sentences and their corresponding translations in Amharic. The sentences are diverse and represent common usage patterns. ### Data Files - `data/english_sentences.txt`: Contains English sentences. - `data/amharic_sentences.txt`: Contains Amharic translations of the English sentences. ## Model Building ### Model Architecture The model utilizes a Recurrent Neural Network (RNN) with Bidirectional Long Short-Term Memory (LSTM) layers. ### Preprocessing Steps 1. **Tokenization**: Convert text into sequences of integers. 2. **Padding**: Pad sequences to ensure uniform input lengths. ## Training The model is trained using the prepared dataset. Key hyperparameters include: - Embedding dimension: 100 - LSTM units: 128 - Batch size: 32 - Epochs: 10 ## Evaluation The model's performance is evaluated using the BLEU score, which measures the accuracy of the translation compared to the reference translations. ## Requirements The project requires the following packages: - TensorFlow - NLTK - NumPy Install the required packages using: ```bash pip install -r requirements.txt