Transformer-Based Language Model (GPT-1.0 Inspired)

Project Structure The project is organized into the following directories and files:

project/
├── data/
│   └── text_data.txt                # Preprocessed training data (WikiText-103)
├── models/
│   └── transformer_model.py         # Transformer model architecture
├── utils/
│   └── data_preprocessing.py        # Data preprocessing utilities (tokenization, vocab building)
├── train.py                         # Script for training the transformer model
├── generate.py                      # Script for generating text using the trained model
└── vocab.pkl                        # Saved vocabulary for encoding/decoding

Setup and Installation Step 1: Clone the Repository

git clone https://github.com/your-repository/transformer-language-model.git
cd transformer-language-model

Step 2: Install Dependencies Ensure you have the necessary libraries installed. Use the following command to install them:

pip install torch numpy datasets tqdm

Step 3: Download and Preprocess the Data The dataset will be automatically downloaded from Hugging Face's dataset library. Run the train.py script to load, tokenize, and preprocess the WikiText-103 dataset.

Training the Model To train the model from scratch, use the following command:

python train.py

The model will train for the specified number of epochs (default: 50). Training loss will be printed at the end of each epoch. The trained model will be saved to transformer_model.pth for future use.

Model Hyperparameters:

d_model: 64 (embedding size) num_layers: 2 (number of decoder layers) num_heads: 8 (multi-head attention heads) d_ff: 256 (feed-forward network hidden layer size) batch_size: 2 seq_length: 10 epochs: 50 These hyperparameters can be tuned for larger models or datasets if required.

Generating Text Once the model is trained, you can generate text by running the generate.py script:

python generate.py

Provide the starting text (seed) in the script to generate sequences of words based on the trained model. The generated text will be printed to the console.

Future Improvements Larger Model: Increase the number of layers, model dimensions, and training epochs for better results. Advanced Tokenization: Implement byte pair encoding (BPE) for improved tokenization. Use of GPUs: Train on larger datasets using GPUs for faster training times. Implement Attention Masking for Long Sequences: Scale the model for longer text sequences.