This project implements an AI-based language translation system using same core technologies: Encoder-Decoder architecture, Long Short-Term Memory (LSTM) networks,Bidirectional LSTM networks and Embedding layers. The system is designed to translate text from one language to another by learning from a dataset of parallel sentences.
- Encoder-Decoder Architecture: Utilizes a sequence-to-sequence model to handle input and output sequences of different lengths.
- LSTM Networks: Employs LSTM cells to manage long-term dependencies and improve translation accuracy.
- Embedding Layers: Converts words into dense vectors that capture semantic meanings, enhancing the model's understanding of language.
- Bidirectional LSTM Networks: Improves context capture by processing input sequences in both forward and backward directions.
- Prepare your dataset: Ensure you have a dataset of parallel sentences in the source and target languages.
- Preprocess the data: Tokenize the sentences and create the necessary input-output pairs.
Once the model is trained, you can use it to translate sentences:
Translate a sentence:
python translate.py --sentence "Your sentence here"ai-language-translation/
│
├── app/
│ ├── __init__.py # initialization application
| ├── model.py # model from hugging face
| └── routes.py # request api
│
├── models/
│ └── dictionary.pkl # Pre-trained model (if available)
│
├── notebook/
│ ├── testing.ipynb # examplenotebook1
│ └── translator.ipynb # examplenotebook2
│
├── .gitignore # gitignore
├── index.html # web-translator
├── README.md # Readme.md
├── requirements.txt # python requirements
├── run.py # app run
└── style.css # styling to index.html
This project is licensed under the MIT License. See the LICENSE file for details.
- This project is inspired by various sequence-to-sequence models in natural language processing.
- Thanks to the open-source community for providing valuable resources and tools.