The Covid-19 pandemic has posed many challenges in the medical diagnostics field. One of these has been the need for constant detection and monitoring of the SARS-CoV-2 circulating variants. The most common approach to reliably identify a SARS-CoV-2 variant is exploiting genomics. Such an approach has been enabled by the constant collection of genetic sequences of the virus globally. However, variant identification methods are usually resource-intensive. Thus, small medical laboratories can have issues due to limited diagnostic capacity. This thesis presents a deep learning method to successfully identify variants without requiring high computational resources and long delays. The contribution of this thesis is twofold: 1) the development of a Bidirectional Encoder Representations from Transformers (BERT) fine-tuning architecture for SARS-CoV-2 variant identification; 2) the mathematical and biological interpretation of the model by leveraging its self-attention mechanism.