Translator using BERT and Transformer

This project is a Jupyter Notebook implementation of a text-to-text translation model using BERT and Transformer architectures. It demonstrates how to build a machine translation model that can translate sentences from one language to another using state-of-the-art NLP techniques.

🧠 Model Overview

The model is built using:

• BERT (Bidirectional Encoder Representations from Transformers) for capturing rich contextual representations.
• Transformer architecture for sequence-to-sequence translation.

Installation ⬇️

Clone the repository:

git clone https://github.com/yourusername/Text-Translation_BERT.git
cd Text-Translation_BERT

Create a virtual environment to isolate the project dependencies:

python -m venv Translator

Activate the virtual environment:

On Windows:

Translator\Scripts\activate

On macOS/Linux:

source Translator/bin/activate

• Install dependencies: Ensure you have Python installed, then install the required libraries:

pip install transformers datasets torch tqdm

Run the application:

python Translator_Bert_Transformer

⚙️ Key Features

• Tokenization and preprocessing using HuggingFace Transformers.

• Model training and evaluation on translation data.

• Visualization of translation results.