seroetr/Chatbot-using-Transformers-and-Universal-Transformers-in-Pytorch

Practical implemantation of Chatbot using Transformer architecture as a guidance to beginners

Chatbot using Transformers and Universal Transformers in Pytorch

• This repository helps you understand the practical implemantation of Chatbot using Transformer architecture as a guidance to beginners.

• You can look at the paper of transformers from https://arxiv.org/abs/1706.03762, if you want theoretical knowledge.

• In order to train the Transformers, Cornell Movie-Dialog Corpus dataset is used, which you can get via https://www.kaggle.com/datasets/rajathmc/cornell-moviedialog-corpus

Transformers

• Transformers get rid of LSTM and its variances, replace them by transformers.

There are 3 key points for transformers

1. Not sequential like RNNs, all the input (example sentence) is fed once through the model, calculation is performed one time.
2. Attention is generated from the model's own input (self-attention).
3. More than one attention is generated each time (Multi-Head Attention)

• In training, there is no need for something called a timestep anymore, there is no sequence, everything is performed at once.

Difference between Transformers and Universal Transformers

• Only modification that happens in universal transformers is basically the recursion.
• Rather than having N layers, we have N timesteps.
• For example, if we're using 6 layers for a transformers, then all of the parameters of the weights of these 6 layers are shared.
• We don't only have the positional encoding of the sequence, but we also have the positional encoding of timesteps.