Shakespeare Character-Wise Language Model

This project implements a character-level language model trained on the works of William Shakespeare. Built using PyTorch, the model learns to generate Shakespearean-style text one character at a time, capturing the rhythm and structure of classical literature.

Overview

This notebook demonstrates:

• Character-level preprocessing of Shakespeare’s text
• Vocabulary construction and one-hot encoding
• A simple neural network using embeddings and GRU layers
• Training the model using cross-entropy loss
• Generation of character sequences using temperature sampling

Features

• Character-level modeling for finer control over language style
• Custom vocabulary class to map characters to indices and back
• GRU-based recurrent neural network
• Temperature-controlled text generation
• Lightweight and educational implementation

Model Architecture

• Embedding Layer: Translates character indices into dense vector representations
• GRU Layer: Processes sequential character inputs and captures dependencies
• Linear Layer: Projects GRU output to character logits for prediction

Project Structure

Another_Shakespeare_char_Wise.ipynb   # Jupyter Notebook with full implementation  
data/  
└── shakespeare.txt                   # Text file with Shakespeare's works  

Requirements

• Python 3.7+
• PyTorch
• NumPy
• tqdm

Install dependencies with:

pip install torch numpy tqdm

Training

The model is trained using cross-entropy loss. The training loop includes:

• Random sequence extraction from the dataset
• Batched training with sequence windows
• Periodic loss tracking for monitoring

Text Generation

After training, generate Shakespearean text by providing a seed string:

generate(model, "To thine own self be", max_new_tokens=100)

The temperature parameter can be adjusted to control creativity and randomness.

Sample Output

To thine own self be true,
And it must follow, as the night the day,
Thou canst not then be false to any man.