/NeuroLingua

Visual Decoding of Language Patterns

Primary LanguagePython

NeuroLingua

NeuroLingua is a machine translation system built using PyTorch. It leverages the power of LSTM networks and introduces attention mechanisms to improve the translation from source to target languages.

Features

  • LSTM based encoder-decoder architecture.
  • Incorporates attention mechanisms for enhanced context capture.
  • Easily configurable model parameters for experimentation.
  • Utilizes the TorchText library for efficient data handling.

Requirements

  • Python 3.9 or above
  • PyTorch
  • TorchText
  • spaCy (with en_core_web_sm and fr_core_news_sm models)

Quick Start

  1. Clone the repository:
git clone [Your Repo URL]
cd NeuroLingua
  1. Install the dependencies:
pip install torch torchtext spacy
python -m spacy download en_core_web_sm
python -m spacy download fr_core_news_sm
  1. Run the main script:
python main.py

Structure

  • attention.py: Defines the attention mechanisms for the model.
  • encoder.py: Contains the Encoder LSTM implementation.
  • decoder.py: Contains the Decoder LSTM with attention.
  • seq2seq.py: Combines the encoder and decoder into a single sequence-to-sequence model.
  • main.py: The main driver script which loads data, defines the model, and trains it.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT