TrAVis is a Transformer Attention Visualiser. The idea of visualising the attention matrices is inspired by Neural Machine Translation by Jointly Learning to Align and Translate.

The original paper of the Transformer model was named Attention Is All You Need, demonstrating the centrality of the attention mechanism to Transformer-based models. These models generate attention matrices during the computation of the attention mechanism, which indicate how the models process the input data, and can therefore be seen as a concrete representation of the mechanism.

In the BERT Base Uncased model, for example, there are 12 transformer layers, each layer contains 12 heads, and each head generates one attention matrix. TrAVis is the tool for visualising these attention matrices.

As Transformer-based models continue to dominate the field of NLP, it's crucial that we not only utilise these models, but also have a deep understanding of how they operate. TrAVis offers an unprecedented level of insight into the inner workings of these models, allowing us to fully leverage their capabilities to solve complex challenges. With this deeper understanding, we can not only optimise the models for specific tasks, but also inspire new innovations and improvements to the architecture. By taking advantage of TrAVis, we can unlock the full potential of Transformer-based models and drive new advancements in the field of NLP.

The project consists of 4 parts.

Firstly, we implemented the BART model from scratch using JAX. We chose JAX because it is an amazing deep learning framework that enables us to write clear source code, and it can be easily converted to NumPy, which can be executed in-browser. We chose the BART model because it is a complete encoder-decoder model, so it can be easily adapted to other models, such as BERT, by simply taking a subset of the source code.

Secondly, we implemented the HuggingFace BERT Tokeniser in pure Python, as it can be more easily executed in-browser. Moreover, we have optimised the tokenisation algorithm, which is 57% faster than the original HuggingFace implementation.

Thirdly, we use Pyodide to run our Python code in browser. Pyodide supports all Python libraries implemented in pure Python, with additional support for a number of other libraries such as NumPy and SciPy.

Fourthly, we visualise the attention matrices in our web application using d3.js.