In today’s complex financial landscape, global systemically important banks (G-SIBs) generate far more than just numbers—every earnings call, analyst Q&A, and executive comment is a window into potential risk. Yet much of this information remains buried in lengthy, unstructured transcripts, inaccessible to traditional analytics. As part of a team project with the Bank of England’s
In this project, I leveraged SetFit, Hugging Face’s prompt‑free few‑shot classification framework, to tackle the challenging SST‑5 task—fine-grained sentiment analysis across five classes (very negative to very positive) What makes SetFit so compelling is its efficiency and simplicity: This project highlights how evolving NLP techniques—from static embeddings to contextual transformers—impact performance and interpretability. Fine-tuned transformer
With over 2 million members and 600 gyms across the UK, Denmark, and Switzerland, PureGym has established itself as one of the world’s leading value fitness operators. Since its founding in 2008, the company has appealed to a broad customer base by offering flexible, affordable, and high-quality fitness facilities. Key to its success is a