• Introduction
• Setup
• Run the code
• Training
• Sample Generation
• Connect with me
• License

This is an implementation of a transformer based language model using Pytorch-Lightning framework. The model consists of two transformer layers with 2 heads each combined with embedding and linear layers. The code uses pytorch-lightning framework that is built for fast research prototyping. Although the code does not leverage all the features of the lightning framework, but it has achieved good result in the language modelling task. For learning purposes, a Pytorch classical version from this project is also provided here (inspired from Pytorch team ❤️).

The code is using pipenv as a virtual environment and package manager. To run the code, all you need is to install the necessary dependencies. open the terminal and type:

• $ git clone https://github.com/Khamies/Transformer_Lightning.git
• $ cd Transformer_Lightning
• $ pipenv install

or

• $ pip install requirements.txt

And you should be ready to go to play with code and build upon it!

• To train the model, run: python main.py

• To train the model with specific arguments, run: python main.py --batch_size=64. The following command-line arguments are available:

  • Train batch size: --bsz_train
  • Test batch size: --bsz_test
  • bptt: --bptt
  • Learning rate: --lr
  • Embedding size: --embed_size
  • Size of FeedForward Neural Network (1st layer): --ffnn_size
  • Attention Heads: --nhead
  • Transformer Layers: --nlayers

The model is trained on 10 epochs using Adam as an optimizer with a learning rate = 0.001 and batch size = 32, you can find all the model settings in settings.py. Here is the loss curve for the training step:

• Cross Entropy Loss

  

Here are some generated samples from the model:

<time> warner owns n million francs.
<media> concern said it is a share n million.

@misc{Khamies2021Transformer_Lightning,
author = {Khamies, Waleed},
title = {A pytorch implementation of transformer based language model using Pytorch-Lightning framework.},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{ https://github.com/Khamies/Transformer_Lightning}},
}

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Thank you ❤️