This repository contains the implementation of a language model that utilizes the attention mechanism. The attention mechanism allows the model to focus on different parts of the input text when generating predictions, enabling it to capture long-range dependencies and produce more accurate and contextually relevant outputs.
The integration of Long Short-Term Memory (LSTM) networks with the attention mechanism represents a groundbreaking advancement in sequence modeling, enhancing the LSTM's ability to handle long-range dependencies and capture contextually rich representations. This synergy addresses the limitations of traditional LSTMs by allowing dynamic weighting of input elements, leading to improved performance across various tasks such as machine translation and text summarization. Furthermore, the attention mechanism provides valuable interpretability by highlighting influential input segments, offering insights into model behavior and refining the learning process.
This repository presents an extension of a project originally developed as an assignment for the course AMS 691.03 Deep Learning. The initial task involved building a Recurrent Neural Network (RNN) based language model, setting the foundation for further exploration and enhancement. Leveraging the foundational work, the current project extends the model by incorporating the attention mechanism, a sophisticated enhancement that enables the model to focus on relevant parts of the input text. The implementation is based on the Penn Treebank dataset, a widely recognized benchmark for language modeling tasks. By building upon the RNN-based architecture and integrating attention, this project aims to achieve more nuanced and contextually rich language modeling capabilities, offering a valuable contribution to the field of deep learning and natural language processing.
- Attention Mechanism: Incorporates attention to improve the model's ability to handle long sequences and capture relationships between words.
- Flexibility: The model can be trained on various types of text data, including but not limited to, natural language text, code, and more.
- Modular Design: The architecture is designed with modularity in mind, allowing for easy experimentation and extension.
- Python 3.x
- TensorFlow 2.x
- NumPy
- [Optional] CUDA-enabled GPU for faster training (recommended for large datasets)