Sequence-to-Sequence Model
This project implements a Sequence-to-Sequence (Seq2Seq) model, likely for applications in natural language processing (NLP), such as machine translation, text summarization, or other sequence prediction tasks. The notebook provides a step-by-step guide to building and training the model.
Features
- Implements a Seq2Seq architecture.
- Contains preprocessing steps for input/output sequences.
- Includes detailed explanations and visualizations for key components.
- Supports training, validation, and evaluation of the model.
Requirements
Ensure the following libraries and dependencies are installed:
- Python (>=3.8)
- NumPy
- TensorFlow or PyTorch (depending on the implementation)
