  Large Language Models (LLMs) like BERT have gained significant prominence due
to their remarkable performance in various natural language processing tasks.
However, they come with substantial computational and memory costs.
Additionally, they are essentially black-box models, challenging to explain and
interpret. In this article, we propose Optimus BERT Compression and
Explainability (OBCE), a methodology to bring explainability to BERT models
using persistent homology, aiming to measure the importance of each neuron by
studying the topological characteristics of their outputs. As a result, we can
compress BERT significantly by reducing the number of parameters (58.47% of the
original parameters for BERT Base, 52.3% for BERT Large). We evaluated our
methodology on the standard GLUE Benchmark, comparing the results with
state-of-the-art techniques and achieving outstanding results. Consequently,
our methodology can "whiten" BERT models by providing explainability to its
neurons and reducing the model's size, making it more suitable for deployment
on resource-constrained devices.
