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---
license: mit
language:
- en
- fr
metrics:
- bleu
library_name: transformers
pipeline_tag: translation
---
# French to English Machine Translation
- **Author:** Kamelia Zaman Moon
- **Project link:** https://huggingface.co/spaces/KameliaZaman/French-to-English-Translation
- **Language(s):** Python
- **License:** MIT
- **Contact:** kamelia.stu2017@juniv.edu
## Table of Contents
- [Introduction](#introduction)
- [Model Architecture](#model-architecture)
- [How-to Guide](#how-to-guide)
- [License](#license)
- [Contributors](#contributors)
## 1. Introduction
This project aims to develop a machine translation system for translating French text into English. The system utilizes state-of-the-art neural network architectures and techniques in natural language processing (NLP) to accurately translate French sentences into their corresponding English equivalents.
## 2. Model Architecture
The machine translation model employs a sequence-to-sequence architecture, specifically utilizing a recurrent neural network (RNN) with an attention mechanism. The model is trained on a parallel corpus consisting of aligned French and English sentences. Key components of the model include encoder and decoder networks, attention mechanism, and tokenization for text processing.
```
── eng_-french.csv - text dataset.
── french_to_english_translator.h5 - generated model.
── french_to_english_translation_using_seq2seq.ipynb - preprocesses input, trains, saves and evaluates the model.
── app.py - this module starts the app interface.
── README.md - readme file of this project.
── requirements.txt - list of required packages.
```
## 3. How-to Guide
### 3.1. Data Preparation
- The parallel corpus containing French and English sentences is preprocessed.
- Text is tokenized and converted into numerical representations suitable for input to the neural network.
### 3.2. Model Training
- The sequence-to-sequence model is constructed, comprising an encoder and decoder.
- Training data is fed into the model, and parameters are optimized using backpropagation and gradient descent algorithms.
### 3.3. Model Evaluation
- The trained model is evaluated on the test set to measure its accuracy.
- Metrics such as BLEU score has been used to quantify the quality of translations.
### 3.4. Deployment
- Gradio is utilized for deploying the trained model.
- Users can input a French text, and the model will translate it to English.
```bash
# clone project
git clone https://huggingface.co/spaces/KameliaZaman/French-to-English-Translation/tree/main
# go inside the project directory
cd French-to-English-Translation
# install the required packages
pip install -r requirements.txt
# run the gradio app
python app.py
```
## 4. License
This project is licensed under the [MIT License](LICENSE).
## 5. Contributors
- Kamelia Zaman Moon - kamelia.stu2017@juniv.edu |