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