Update README.md

README.md

@@ -1,3 +1,80 @@
 ---
 license: mit
+language:
+- en
+- fr
+metrics:
+- bleu
+library_name: transformers
 ---
+
+# 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