Neural Network-Based Language Model for Next Token Prediction
Project Overview
This project aims to develop a neural network-based language model for next token prediction, leveraging both English and Belarusian languages. The model is designed to understand the context and semantics of sentences, enabling it to predict subsequent words accurately.
Objectives
- Build a language model capable of predicting the next token in a sequence.
- Train the model on datasets from English and Belarusian to enhance multilingual capabilities.
- Evaluate the model's performance using loss metrics and generated text examples.
Features
- Next Token Prediction: Utilizes a neural network to predict the next token based on input sequences.
- Language Support: Incorporates English and Belarusian datasets for training and evaluation.
- Checkpoint System: Implements a checkpoint mechanism to save and load model weights, facilitating long training sessions.
- Loss Analysis: Includes visualizations of training and validation loss curves to assess learning progress and detect overfitting.
- Text Generation: Demonstrates the model's ability to generate coherent text in both English and Belarusian based on given prompts.
Technologies Used
- Programming Language: Python
- Deep Learning Framework: PyTorch
- Numerical Computation: NumPy
- Data Visualization: Matplotlib
- Development Environment: Google Colab
